A SDK for the creation of analysis tools without obtaining app source code in order to profile runtime performance, examine code coverage, and track high-risk behaviors of a given app on Android 5.0 and above.

CrystalLens is a social media screening and analysis platform that collects posts from multiple sources (via Apify scrapers) and applies local AI (Ollama) to generate character insights, behavioral patterns, and risk assessments for employees or candidates.

W3C Strategic Highlights September 2019 This report was prepared for the September 2019 W3C Advisory Committee Meeting (W3C Member link). See the accompanying W3C Fact Sheet — September 2019. For the previous edition, see the April 2019 W3C Strategic Highlights. For future editions of this report, please consult the latest version. A Chinese translation is available. ☰ Contents Introduction Future Web Standards Meeting Industry Needs Web Payments Digital Publishing Media and Entertainment Web & Telecommunications Real-Time Communications (WebRTC) Web & Networks Automotive Web of Things Strengthening the Core of the Web HTML CSS Fonts SVG Audio Performance Web Performance WebAssembly Testing Browser Testing and Tools WebPlatform Tests Web of Data Web for All Security, Privacy, Identity Internationalization (i18n) Web Accessibility Outreach to the world W3C Developer Relations W3C Training Translations W3C Liaisons Introduction This report highlights recent work of enhancement of the existing landscape of the Web platform and innovation for the growth and strength of the Web. 33 working groups and a dozen interest groups enable W3C to pursue its mission through the creation of Web standards, guidelines, and supporting materials. We track the tremendous work done across the Consortium through homogeneous work-spaces in Github which enables better monitoring and management. We are in the middle of a period where we are chartering numerous working groups which demonstrate the rapid degree of change for the Web platform: After 4 years, we are nearly ready to publish a Payment Request API Proposed Recommendation and we need to soon charter follow-on work. In the last year we chartered the Web Payment Security Interest Group. In the last year we chartered the Web Media Working Group with 7 specifications for next generation Media support on the Web. We have Accessibility Guidelines under W3C Member review which includes Silver, a new approach. We have just launched the Decentralized Identifier Working Group which has tremendous potential because Decentralized Identifier (DID) is an identifier that is globally unique, resolveable with high availability, and cryptographically verifiable. We have Privacy IG (PING) under W3C Member review which strengthens our focus on the tradeoff between privacy and function. We have a new CSS charter under W3C Member review which maps the group's work for the next three years. In this period, W3C and the WHATWG have succesfully completed the negotiation of a Memorandum of Understanding rooted in the mutual belief that that having two distinct specifications claiming to be normative is generally harmful for the Web community. The MOU, signed last May, describes how the two organizations are to collaborate on the development of a single authoritative version of the HTML and DOM specifications. W3C subsequently rechartered the HTML Working Group to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and for the production of W3C Recommendations from WHATWG Review Drafts. As the Web evolves continuously, some groups are looking for ways for specifications to do so as well. So-called "evergreen recommendations" or "living standards" aim to track continuous development (and maintenance) of features, on a feature-by-feature basis, while getting review and patent commitments. We see the maturation and further development of an incredible number of new technologies coming to the Web. Continued progress in many areas demonstrates the vitality of the W3C and the Web community, as the rest of the report illustrates. Future Web Standards W3C has a variety of mechanisms for listening to what the community thinks could become good future Web standards. These include discussions with the Membership, discussions with other standards bodies, the activities of thousands of participants in over 300 community groups, and W3C Workshops. There are lots of good ideas. The W3C strategy team has been identifying promising topics and invites public participation. Future, recent and under consideration Workshops include: Inclusive XR (5-6 November 2019, Seattle, WA, USA) to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive, including to people with disabilities; W3C Workshop on Data Models for Transportation (12-13 September 2019, Palo Alto, CA, USA) W3C Workshop on Web Games (27-28 June 2019, Redmond, WA, USA), view report Second W3C Workshop on the Web of Things (3-5 June 2019, Munich, Germany) W3C Workshop on Web Standardization for Graph Data; Creating Bridges: RDF, Property Graph and SQL (4-6 March 2019, Berlin, Germany), view report Web & Machine Learning. The Strategy Funnel documents the staff's exploration of potential new work at various phases: Exploration and Investigation, Incubation and Evaluation, and eventually to the chartering of a new standards group. The Funnel view is a GitHub Project where new area are issues represented by “cards” which move through the columns, usually from left to right. Most cards start in Exploration and move towards Chartering, or move out of the funnel. Public input is welcome at any stage but particularly once Incubation has begun. This helps W3C identify work that is sufficiently incubated to warrant standardization, to review the ecosystem around the work and indicate interest in participating in its standardization, and then to draft a charter that reflects an appropriate scope. Ongoing feedback can speed up the overall standardization process. Since the previous highlights document, W3C has chartered a number of groups, and started discussion on many more: Newly Chartered or Rechartered Web Application Security WG (03-Apr) Web Payment Security IG (17-Apr) Patent and Standards IG (24-Apr) Web Applications WG (14-May) Web & Networks IG (16-May) Media WG (23-May) Media and Entertainment IG (06-Jun) HTML WG (06-Jun) Decentralized Identifier WG (05-Sep) Extended Privacy IG (PING) (30-Sep) Verifiable Claims WG (30-Sep) Service Workers WG (31-Dec) Dataset Exchange WG (31-Dec) Web of Things Working Group (31-Dec) Web Audio Working Group (31-Dec) Proposed charters / Advance Notice Accessibility Guidelines WG Privacy IG (PING) RDF Literal Direction WG Timed Text WG CSS WG Web Authentication WG Closed Internationalization Tag Set IG Meeting Industry Needs Web Payments All Web Payments specifications W3C's payments standards enable a streamlined checkout experience, enabling a consistent user experience across the Web with lower front end development costs for merchants. Users can store and reuse information and more quickly and accurately complete online transactions. The Web Payments Working Group has republished Payment Request API as a Candidate Recommendation, aiming to publish a Proposed Recommendation in the Fall 2019, and is discussing use cases and features for Payment Request after publication of the 1.0 Recommendation. Browser vendors have been finalizing implementation of features added in the past year (view the implementation report). As work continues on the Payment Handler API and its implementation (currently in Chrome and Edge Canary), one focus in 2019 is to increase adoption in other browsers. Recently, Mastercard demonstrated the use of Payment Request API to carry out EMVCo's Secure Remote Commerce (SRC) protocol whose payment method definition is being developed with active participation by Visa, Mastercard, American Express, and Discover. Payment method availability is a key factor in merchant considerations about adopting Payment Request API. The ability to get uniform adoption of a new payment method such as Secure Remote Commerce (SRC) also depends on the availability of the Payment Handler API in browsers, or of proprietary alternatives. Web Monetization, which the Web Payments Working Group will discuss again at its face-to-face meeting in September, can be used to enable micropayments as an alternative revenue stream to advertising. Since the beginning of 2019, Amazon, Brave Software, JCB, Certus Cybersecurity Solutions and Netflix have joined the Web Payments Working Group. In April, W3C launched the Web Payment Security Group to enable W3C, EMVCo, and the FIDO Alliance to collaborate on a vision for Web payment security and interoperability. Participants will define areas of collaboration and identify gaps between existing technical specifications in order to increase compatibility among different technologies, such as: How do SRC, FIDO, and Payment Request relate? The Payment Services Directive 2 (PSD2) regulations in Europe are scheduled to take effect in September 2019. What is the role of EMVCo, W3C, and FIDO technologies, and what is the current state of readiness for the deadline? How can we improve privacy on the Web at the same time as we meet industry requirements regarding user identity? Digital Publishing All Digital Publishing specifications, Publication milestones The Web is the universal publishing platform. Publishing is increasingly impacted by the Web, and the Web increasingly impacts Publishing. Topic of particular interest to Publishing@W3C include typography and layout, accessibility, usability, portability, distribution, archiving, offline access, print on demand, and reliable cross referencing. And the diverse publishing community represented in the groups consist of the traditional "trade" publishers, ebook reading system manufacturers, but also publishers of audio book, scholarly journals or educational materials, library scientists or browser developers. The Publishing Working Group currently concentrates on Audiobooks which lack a comprehensive standard, thus incurring extra costs and time to publish in this booming market. Active development is ongoing on the future standard: Publication Manifest Audiobook profile for Web Publications Lightweight Packaging Format The BD Comics Manga Community Group, the Synchronized Multimedia for Publications Community Group, the Publishing Community Group and a future group on archival, are companions to the working group where specific work is developed and incubated. The Publishing Community Group is a recently launched incubation channel for Publishing@W3C. The goal of the group is to propose, document, and prototype features broadly related to: publications on the Web reading modes and systems and the user experience of publications The EPUB 3 Community Group has successfully completed the revision of EPUB 3.2. The Publishing Business Group fosters ongoing participation by members of the publishing industry and the overall ecosystem in the development of Web infrastructure to better support the needs of the industry. The Business Group serves as an additional conduit to the Publishing Working Group and several Community Groups for feedback between the publishing ecosystem and W3C. The Publishing BG has played a vital role in fostering and advancing the adoption and continued development of EPUB 3. In particular the BG provided critical support to the update of EPUBCheck to validate EPUB content to the new EPUB 3.2 specification. This resulted in the development, in conjunction with the EPUB3 Community Group, of a new generation of EPUBCheck, i.e., EPUBCheck 4.2 production-ready release. Media and Entertainment All Media specifications The Media and Entertainment vertical tracks media-related topics and features that create immersive experiences for end users. HTML5 brought standard audio and video elements to the Web. Standardization activities since then have aimed at turning the Web into a professional platform fully suitable for the delivery of media content and associated materials, enabling missing features to stream video content on the Web such as adaptive streaming and content protection. Together with Microsoft, Comcast, Netflix and Google, W3C received an Technology & Engineering Emmy Award in April 2019 for standardization of a full TV experience on the Web. Current goals are to: Reinforce core media technologies: Creation of the Media Working Group, to develop media-related specifications incubated in the WICG (e.g. Media Capabilities, Picture-in-picture, Media Session) and maintain maintain/evolve Media Source Extensions (MSE) and Encrypted Media Extensions (EME). Improve support for Media Timed Events: data cues incubation. Enhance color support (HDR, wide gamut), in scope of the CSS WG and in the Color on the Web CG. Reduce fragmentation: Continue annual releases of a common and testable baseline media devices, in scope of the Web Media APIs CG and in collaboration with the CTA WAVE Project. Maintain the Road-map of Media Technologies for the Web which highlights Web technologies that can be used to build media applications and services, as well as known gaps to enable additional use cases. Create the future: Discuss perspectives for Media and Entertainment for the Web. Bring the power of GPUs to the Web (graphics, machine learning, heavy processing), under incubation in the GPU for the Web CG. Transition to a Working Group is under discussion. Determine next steps after the successful W3C Workshop on Web Games of June 2019. View the report. Timed Text The Timed Text Working Group develops and maintains formats used for the representation of text synchronized with other timed media, like audio and video, and notably works on TTML, profiles of TTML, and WebVTT. Recent progress includes: A robust WebVTT implementation report poises the specification for publication as a proposed recommendation. Discussions around re-chartering, notably to add a TTML Profile for Audio Description deliverable to the scope of the group, and clarify that rendering of captions within XR content is also in scope. Immersive Web Hardware that enables Virtual Reality (VR) and Augmented Reality (AR) applications are now broadly available to consumers, offering an immersive computing platform with both new opportunities and challenges. The ability to interact directly with immersive hardware is critical to ensuring that the web is well equipped to operate as a first-class citizen in this environment. The Immersive Web Working Group has been stabilizing the WebXR Device API while the companion Immersive Web Community Group incubates the next series of features identified as key for the future of the Immersive Web. W3C plans a workshop focused on the needs and benefits at the intersection of VR & Accessibility (Inclusive XR), on 5-6 November 2019 in Seattle, WA, USA, to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive. Web & Telecommunications The Web is the Open Platform for Mobile. Telecommunication service providers and network equipment providers have long been critical actors in the deployment of Web technologies. As the Web platform matures, it brings richer and richer capabilities to extend existing services to new users and devices, and propose new and innovative services. Real-Time Communications (WebRTC) All Real-Time Communications specifications WebRTC has reshaped the whole communication landscape by making any connected device a potential communication end-point, bringing audio and video communications anywhere, on any network, vastly expanding the ability of operators to reach their customers. WebRTC serves as the corner-stone of many online communication and collaboration services. The WebRTC Working Group aims to bringing WebRTC 1.0 (and companion specification Media Capture and Streams) to Recommendation by the end of 2019. Intense efforts are focused on testing (supported by a dedicated hackathon at IETF 104) and interoperability. The group is considering pushing features that have not gotten enough traction to separate modules or to a later minor revision of the spec. Beyond WebRTC 1.0, the WebRTC Working Group will focus its efforts on WebRTC NV which the group has started documenting by identifying use cases. Web & Networks Recently launched, in the wake of the May 2018 Web5G workshop, the Web & Networks Interest Group is chaired by representatives from AT&T, China Mobile and Intel, with a goal to explore solutions for web applications to achieve better performance and resource allocation, both on the device and network. The group's first efforts are around use cases, privacy & security requirements and liaisons. Automotive All Automotive specifications To create a rich application ecosystem for vehicles and other devices allowed to connect to the vehicle, the W3C Automotive Working Group is delivering a service specification to expose all common vehicle signals (engine temperature, fuel/charge level, range, tire pressure, speed, etc.) The Vehicle Information Service Specification (VISS), which is a Candidate Recommendation, is seeing more implementations across the industry. It provides the access method to a common data model for all the vehicle signals –presently encapsulating a thousand or so different data elements– and will be growing to accommodate the advances in automotive such as autonomous and driver assist technologies and electrification. The group is already working on a successor to VISS, leveraging the underlying data model and the VIWI submission from Volkswagen, for a more robust means of accessing vehicle signals information and the same paradigm for other automotive needs including location-based services, media, notifications and caching content. The Automotive and Web Platform Business Group acts as an incubator for prospective standards work. One of its task forces is using W3C VISS in performing data sampling and off-boarding the information to the cloud. Access to the wealth of information that W3C's auto signals standard exposes is of interest to regulators, urban planners, insurance companies, auto manufacturers, fleet managers and owners, service providers and others. In addition to components needed for data sampling and edge computing, capturing user and owner consent, information collection methods and handling of data are in scope. The upcoming W3C Workshop on Data Models for Transportation (September 2019) is expected to focus on the need of additional ontologies around transportation space. Web of Things All Web of Things specifications W3C's Web of Things work is designed to bridge disparate technology stacks to allow devices to work together and achieve scale, thus enabling the potential of the Internet of Things by eliminating fragmentation and fostering interoperability. Thing descriptions expressed in JSON-LD cover the behavior, interaction affordances, data schema, security configuration, and protocol bindings. The Web of Things complements existing IoT ecosystems to reduce the cost and risk for suppliers and consumers of applications that create value by combining multiple devices and information services. There are many sectors that will benefit, e.g. smart homes, smart cities, smart industry, smart agriculture, smart healthcare and many more. The Web of Things Working Group is finishing the initial Web of Things standards, with support from the Web of Things Interest Group: Web of Things Architecture Thing Descriptions Strengthening the Core of the Web HTML The HTML Working Group was chartered early June to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and to produce W3C Recommendations from WHATWG Review Drafts. A few days before, W3C and the WHATWG signed a Memorandum of Understanding outlining the agreement to collaborate on the development of a single version of the HTML and DOM specifications. Issues and proposed solutions for HTML and DOM done via the newly rechartered HTML Working Group in the WHATWG repositories The HTML Working Group is targetting November 2019 to bring HTML and DOM to Candidate Recommendations. CSS All CSS specifications CSS is a critical part of the Open Web Platform. The CSS Working Group gathers requirements from two large groups of CSS users: the publishing industry and application developers. Within W3C, those groups are exemplified by the Publishing groups and the Web Platform Working Group. The former requires things like better pagination support and advanced font handling, the latter needs intelligent (and fast!) scrolling and animations. What we know as CSS is actually a collection of almost a hundred specifications, referred to as ‘modules’. The current state of CSS is defined by a snapshot, updated once a year. The group also publishes an index defining every term defined by CSS specifications. Fonts All Fonts specifications The Web Fonts Working Group develops specifications that allow the interoperable deployment of downloadable fonts on the Web, with a focus on Progressive Font Enrichment as well as maintenance of WOFF Recommendations. Recent and ongoing work includes: Early API experiments by Adobe and Monotype have demonstrated the feasibility of a font enrichment API, where a server delivers a font with minimal glyph repertoire and the client can query the full repertoire and request additional subsets on-the-fly. In other experiments, the Brotli compression used in WOFF 2 was extended to support shared dictionaries and patch update. Metrics to quantify improvement are a current hot discussion topic. The group will meet at ATypi 2019 in Japan, to gather requirements from the international typography community. The group will first produce a report summarizing the strengths and weaknesses of each prototype solution by Q2 2020. SVG All SVG specifications SVG is an important and widely-used part of the Open Web Platform. The SVG Working Group focuses on aligning the SVG 2.0 specification with browser implementations, having split the specification into a currently-implemented 2.0 and a forward-looking 2.1. Current activity is on stabilization, increased integration with the Open Web Platform, and test coverage analysis. The Working Group was rechartered in March 2019. A new work item concerns native (non-Web-browser) uses of SVG as a non-interactive, vector graphics format. Audio The Web Audio Working Group was extended to finish its work on the Web Audio API, expecting to publish it as a Recommendation by year end. The specification enables synthesizing audio in the browser. Audio operations are performed with audio nodes, which are linked together to form a modular audio routing graph. Multiple sources — with different types of channel layout — are supported. This modular design provides the flexibility to create complex audio functions with dynamic effects. The first version of Web Audio API is now feature complete and is implemented in all modern browsers. Work has started on the next version, and new features are being incubated in the Audio Community Group. Performance Web Performance All Web Performance specifications There are currently 18 specifications in development in the Web Performance Working Group aiming to provide methods to observe and improve aspects of application performance of user agent features and APIs. The W3C team is looking at related work incubated in the W3C GPU for the Web (WebGPU) Community Group which is poised to transition to a W3C Working Group. A preliminary draft charter is available. WebAssembly All WebAssembly specifications WebAssembly improves Web performance and power by being a virtual machine and execution environment enabling loaded pages to run native (compiled) code. It is deployed in Firefox, Edge, Safari and Chrome. The specification will soon reach Candidate Recommendation. WebAssembly enables near-native performance, optimized load time, and perhaps most importantly, a compilation target for existing code bases. While it has a small number of native types, much of the performance increase relative to Javascript derives from its use of consistent typing. WebAssembly leverages decades of optimization for compiled languages and the byte code is optimized for compactness and streaming (the web page starts executing while the rest of the code downloads). Network and API access all occurs through accompanying Javascript libraries -- the security model is identical to that of Javascript. Requirements gathering and language development occur in the Community Group while the Working Group manages test development, community review and progression of specifications on the Recommendation Track. Testing Browser testing plays a critical role in the growth of the Web by: Improving the reliability of Web technology definitions; Improving the quality of implementations of these technologies by helping vendors to detect bugs in their products; Improving the data available to Web developers on known bugs and deficiencies of Web technologies by publishing results of these tests. Browser Testing and Tools The Browser Testing and Tools Working Group is developing WebDriver version 2, having published last year the W3C Recommendation of WebDriver. WebDriver acts as a remote control interface that enables introspection and control of user agents, provides a platform- and language-neutral wire protocol as a way for out-of-process programs to remotely instruct the behavior of Web, and emulates the actions of a real person using the browser. WebPlatform Tests The WebPlatform Tests project now provides a mechanism which allows to fully automate tests that previously needed to be run manually: TestDriver. TestDriver enables sending trusted key and mouse events, sending complex series of trusted pointer and key interactions for things like in-content drag-and-drop or pinch zoom, and even file upload. Since 2014 W3C began work on this coordinated open-source effort to build a cross-browser test suite for the Web Platform, which WHATWG, and all major browsers adopted. Web of Data All Data specifications There have been several great success stories around the standardization of data on the web over the past year. Verifiable Claims seems to have significant uptake. It is also significant that the Distributed Identifier WG charter has received numerous favorable reviews, and was just recently launched. JSON-LD has been a major success with the large deployment on Web sites via schema.org. JSON-LD 1.1 completed technical work, about to transition to CR More than 25% of websites today include schema.org data in JSON-LD The Web of Things description is in CR since May, making use of JSON-LD Verifiable Credentials data model is in CR since July, also making use of JSON-LD Continued strong interest in decentralized identifiers Engagement from the TAG with reframing core documents, such as Ethical Web Principles, to include data on the web within their scope Data is increasingly important for all organizations, especially with the rise of IoT and Big Data. W3C has a mature and extensive suite of standards relating to data that were developed over two decades of experience, with plans for further work on making it easier for developers to work with graph data and knowledge graphs. Linked Data is about the use of URIs as names for things, the ability to dereference these URIs to get further information and to include links to other data. There are ever-increasing sources of open Linked Data on the Web, as well as data services that are restricted to the suppliers and consumers of those services. The digital transformation of industry is seeking to exploit advanced digital technologies. This will facilitate businesses to integrate horizontally along the supply and value chains, and vertically from the factory floor to the office floor. W3C is seeking to make it easier to support enterprise-wide data management and governance, reflecting the strategic importance of data to modern businesses. Traditional approaches to data have focused on tabular databases (SQL/RDBMS), Comma Separated Value (CSV) files, and data embedded in PDF documents and spreadsheets. We're now in midst of a major shift to graph data with nodes and labeled directed links between them. Graph data is: Faster than using SQL and associated JOIN operations More favorable to integrating data from heterogeneous sources Better suited to situations where the data model is evolving In the wake of the recent W3C Workshop on Graph Data we are in the process of launching a Graph Standardization Business Group to provide a business perspective with use cases and requirements, to coordinate technical standards work and liaisons with external organizations. Web for All Security, Privacy, Identity All Security specifications, all Privacy specifications Authentication on the Web As the WebAuthn Level 1 W3C Recommendation published last March is seeing wide implementation and adoption of strong cryptographic authentication, work is proceeding on Level 2. The open standard Web API gives native authentication technology built into native platforms, browsers, operating systems (including mobile) and hardware, offering protection against hacking, credential theft, phishing attacks, thus aiming to end the era of passwords as a security construct. You may read more in our March press release. Privacy An increasing number of W3C specifications are benefitting from Privacy and Security review; there are security and privacy aspects to every specification. Early review is essential. Working with the TAG, the Privacy Interest Group has updated the Self-Review Questionnaire: Security and Privacy. Other recent work of the group includes public blogging further to the exploration of anti-patterns in standards and permission prompts. Security The Web Application Security Working Group adopted Feature Policy, aiming to allow developers to selectively enable, disable, or modify the behavior of some of these browser features and APIs within their application; and Fetch Metadata, aiming to provide servers with enough information to make a priori decisions about whether or not to service a request based on the way it was made, and the context in which it will be used. The Web Payment Security Interest Group, launched last April, convenes members from W3C, EMVCo, and the FIDO Alliance to discuss cooperative work to enhance the security and interoperability of Web payments (read more about payments). Internationalization (i18n) All Internationalization specifications, educational articles related to Internationalization, spec developers checklist Only a quarter or so current Web users use English online and that proportion will continue to decrease as the Web reaches more and more communities of limited English proficiency. If the Web is to live up to the "World Wide" portion of its name, and for the Web to truly work for stakeholders all around the world engaging with content in various languages, it must support the needs of worldwide users as they engage with content in the various languages. The growth of epublishing also brings requirements for new features and improved typography on the Web. It is important to ensure the needs of local communities are captured. The W3C Internationalization Initiative was set up to increase in-house resources dedicated to accelerating progress in making the World Wide Web "worldwide" by gathering user requirements, supporting developers, and education & outreach. For an overview of current projects see the i18n radar. W3C's Internationalization efforts progressed on a number of fronts recently: Requirements: New African and European language groups will work on the gap analysis, errata and layout requirements. Gap analysis: Japanese, Devanagari, Bengali, Tamil, Lao, Khmer, Javanese, and Ethiopic updated in the gap-analysis documents. Layout requirements document: notable progress tracked in the Southeast Asian Task Force while work continues on Chinese layout requirements. Developer support: Spec reviews: the i18n WG continues active review of specifications of the WHATWG and other W3C Working Groups. Short review checklist: easy way to begin a self-review to help spec developers understand what aspects of their spec are likely to need attention for internationalization, and points them to more detailed checklists for the relevant topics. It also helps those reviewing specs for i18n issues. Strings on the Web: Language and Direction Metadata lays out issues and discusses potential solutions for passing information about language and direction with strings in JSON or other data formats. The document was rewritten for clarity, and expanded. The group is collaborating with the JSON-LD and Web Publishing groups to develop a plan for updating RDF, JSON-LD and related specifications to handle metadata for base direction of text (bidi). User-friendly test format: a new format was developed for Internationalization Test Suite tests, which displays helpful information about how the test works. This particularly useful because those tests are pointed to by educational materials and gap-analysis documents. Web Platform Tests: a large number of tests in the i18n test suite have been ported to the WPT repository, including: css-counter-styles, css-ruby, css-syntax, css-test, css-text-decor, css-writing-modes, and css-pseudo. Education & outreach: (for all educational materials, see the HTML & CSS Authoring Techniques) Web Accessibility All Accessibility specifications, WAI resources The Web Accessibility Initiative supports W3C's Web for All mission. Recent achievements include: Education and training: Inaccessibility of CAPTCHA updated to bring our analysis and recommendations up to date with CAPTCHA practice today, concluding two years of extensive work and invaluable input from the public (read more on the W3C Blog Learn why your web content and applications should be accessible. The Education and Outreach Working Group has completed revision and updating of the Business Case for Digital Accessibility. Accessibility guidelines: The Accessibility Guidelines Working Group has continued to update WCAG Techniques and Understanding WCAG 2.1; and published a Candidate Recommendation of Accessibility Conformance Testing Rules Format 1.0 to improve inter-rater reliability when evaluating conformance of web content to WCAG An updated charter is being developed to host work on "Silver", the next generation accessibility guidelines (WCAG 2.2) There are accessibility aspects to most specifications. Check your work with the FAST checklist. Outreach to the world W3C Developer Relations To foster the excellent feedback loop between Web Standards development and Web developers, and to grow participation from that diverse community, recent W3C Developer Relations activities include: @w3cdevs tracks the enormous amount of work happening across W3C W3C Track during the Web Conference 2019 in San Francisco Tech videos: W3C published the 2019 Web Games Workshop videos The 16 September 2019 Developer Meetup in Fukuoka, Japan, is open to all and will combine a set of technical demos prepared by W3C groups, and a series of talks on a selected set of W3C technologies and projects W3C is involved with Mozilla, Google, Samsung, Microsoft and Bocoup in the organization of ViewSource 2019 in Amsterdam (read more on the W3C Blog) W3C Training In partnership with EdX, W3C's MOOC training program, W3Cx offers a complete "Front-End Web Developer" (FEWD) professional certificate program that consists of a suite of five courses on the foundational languages that power the Web: HTML5, CSS and JavaScript. We count nearly 900K students from all over the world. Translations Many Web users rely on translations of documents developed at W3C whose official language is English. W3C is extremely grateful to the continuous efforts of its community in ensuring our various deliverables in general, and in our specifications in particular, are made available in other languages, for free, ensuring their exposure to a much more diverse set of readers. Last Spring we developed a more robust system, a new listing of translations of W3C specifications and updated the instructions on how to contribute to our translation efforts. W3C Liaisons Liaisons and coordination with numerous organizations and Standards Development Organizations (SDOs) is crucial for W3C to: make sure standards are interoperable coordinate our respective agenda in Internet governance: W3C participates in ICANN, GIPO, IGF, the I* organizations (ICANN, IETF, ISOC, IAB). ensure at the government liaison level that our standards work is officially recognized when important to our membership so that products based on them (often done by our members) are part of procurement orders. W3C has ARO/PAS status with ISO. W3C participates in the EU MSP and Rolling Plan on Standardization ensure the global set of Web and Internet standards form a compatible stack of technologies, at the technical and policy level (patent regime, fragmentation, use in policy making) promote Standards adoption equally by the industry, the public sector, and the public at large Coralie Mercier, Editor, W3C Marketing & Communications $Id: Overview.html,v 1.60 2019/10/15 12:05:52 coralie Exp $ Copyright © 2019 W3C ® (MIT, ERCIM, Keio, Beihang) Usage policies apply.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

A practical, community-driven checklist for pentesting MCP servers. Covers traffic analysis, tool-call behavior, namespace abuse, auth flows, and remote server risks. Maintained by Appsecco and licensed for remixing.

The OWASP Top 5 Machine Learning Risks[edit | edit source] The idea is to build the required resources which help software security community to understand the emerging technology of machine learning and how it is related to security, warn them about the risk associated with using ML, and discuss the defending techniques. Description[edit | edit source] Machine Learning has recently re-emerged as a powerful tool in multiple business sectors, especially when it is used for Predictive Analytics at the scale of Big Data. This technique becomes vital when it is harnessed for the Security services and applications like Fraud Detection, Anomaly Detection, Behavioral Analysis

Privacy Policy introduction We understand the importance of personal information to you and will do our utmost to protect your personal information. We are committed to maintaining your trust in us and to abide by the following principles to protect your personal information: the principle of consistency of rights and responsibilities, the principle of purpose , choose the principle of consent, at least the principle of sufficient use, ensure the principle of security, the principle of subject participation, the principle of openness and transparency, and so on. At the same time, we promise that we will take appropriate security measures to protect your personal information according to the industry's mature security solutions. In view of this, we have formulated this "Private Privacy Policy" (hereinafter referred to as "this policy" /This Privacy Policy") and remind you: This policy applies to products or services on this platform. If the products or services provided by the platform are used in the products or services of our affiliates (for example, using the platform account directly) but there is no independent privacy policy, this policy also applies to the products or services. It is important to note that this policy does not apply to other third-party services provided by you, nor to products or services on this platform that have been independently set up with a privacy policy. Before using the products or services on this platform, please read and understand this policy carefully, and use the related products or services after confirming that you fully understand and agree. By using the products or services on this platform, you understand and agree to this policy. If you have any questions, comments or suggestions about the content of this policy, you can contact us through various contact methods provided by this platform. This privacy policy section will help you understand the following: How we collect and use your personal information How do we use cookies and similar technologies? How do we share, transfer, and publicly disclose your personal information? How we protect your personal information How do you manage your personal information? How do we deal with the personal information of minors? How your personal information is transferred globally How to update this privacy policy How to contact us 一、How we collect and use your personal information Personal information refers to various information recorded electronically or otherwise that can identify a specific natural person or reflect the activities of a particular natural person, either alone or in combination with other information. We collect and use your information for the purposes described in this policy. Personal information: （一）Help you become our user To create an account so that we can serve you, you will need to provide the following information: your nickname, avatar, gender, date of birth, mobile number/signal/QQ number, and create a username and password. During the registration process, if you provide the following additional information to supplement your personal information, it will help us to provide you with better service and experience: your real name, real ID information, hometown, emotional status, constellation, occupation, school Your real avatar. However, if you do not provide this information, it will not affect the basic functions of using the platform products or services. The above information provided by you will continue to authorize us during your use of the Service. When you voluntarily cancel your account, we will make it anonymous or delete your personal information as soon as possible in accordance with applicable laws and regulations. （二）Show and push goods or services for you In order to improve our products or services and provide you with personalized information search and transaction services, we will extract your browsing, search preferences, behavioral habits based on your browsing and search history, device information, location information, and transaction information. Features such as location information, indirect crowd portraits based on feature tags, and display and push information. If you do not want to accept commercials that we send to you, you can cancel them at any time through the product unsubscribe feature. （三）Provide goods or services to you 1、Information you provide to us Relevant personal information that you provide to us when registering for an account or using our services, such as phone numbers, emails, bank card numbers or Alipay accounts; The shared information that you provide to other parties through our services and the information that you store when you use our services. Before providing the platform with the aforementioned personal information of the other party, you need to ensure that you have obtained your authorization. 2、Information we collect during your use of the service In order to provide you with page display and search results that better suit your needs, understand product suitability, and identify account anomalies, we collect and correlate information about the services you use and how they are used, including: Device Information: We will receive and record information about the device you are using (such as device model, operating system version, device settings, unique device identifier, etc.) based on the specific permissions you have granted during software installation and use. Information about the location of the device (such as Idiv address, GdivS location, and Wi-Fi that can provide relevant information) Sensor information such as access points, Bluetooth and base stations. Since the services we provide are based on the mobile social services provided by the geographic location, you confirm that the successful registration of the "this platform" account is deemed to confirm the authorization to extract, disclose and use your geographic location information. . If you need to terminate your location information to other users, you can set it to be invisible at any time. Log information: When you use our website or the products or services provided by the client, we will automatically collect your detailed usage of our services as a related web log. For example, your search query content, Idiv address, browser type, telecom carrier, language used, date and time of access, and web page history you visit. Please note that separate device information, log information, etc. are information that does not identify a particular natural person. If we combine such non-personal information with other information to identify a particular natural person or use it in conjunction with personal information, such non-personal information will be treated as personal information during the combined use, except for your authorization. Or as otherwise provided by laws and regulations, we will anonymize and de-identify such personal information. When you contact us, we may save information such as your communication/call history and content or the contact information you left in order to contact you or help you solve the problem or to document the resolution and results of the problem. 3、Your personal information collected through indirect access You can use the products or services provided by our affiliates through the link of the platform provided by our platform account. In order to facilitate our one-stop service based on the linked accounts and facilitate your unified management, we will show you on this platform. Information or recommendations for information you are interested in, including information from live broadcasts and games. You can discover and use the above services through the homepage of the platform, "More" and other functions. When you use the above services through our products or services, you authorize us to receive, aggregate, and analyze from our affiliates based on actual business and cooperation needs, we confirm that their source is legal or that you authorize to consent to your personal information provided to us or Trading Information. If you refuse to provide the above information or refuse to authorize, you may not be able to use the corresponding products or services of our affiliates, or can not display relevant information, but does not affect the use of the platform to browse, chat, release dynamics and other core services. （四）Provide you with security Please note that in order to ensure the authenticity of the user's identity and provide you with better security, you can provide us with identification information such as identity card, military officer's card, passport, driver's license, social security card, residence permit, facial identification, and other biometric information. Personally sensitive information such as Sesame Credit and other real-name certifications. If you refuse to provide the above information, you may not be able to use services such as account management, live broadcast, and continuing risky transactions, but it will not affect your use of browsing, chat and other services. To improve the security of your services provided by us and our affiliates and partners, protect the personal and property of you or other users or the public from being compromised, and better prevent phishing websites, fraud, network vulnerabilities, computer viruses, cyber attacks , security risks such as network intrusion, more accurately identify violations of laws and regulations or the relevant rules of the platform, we may use or integrate your user information, transaction information, equipment information, related web logs and our affiliates, partners to obtain You authorize or rely on the information shared by law to comprehensively judge your account and transaction risks, conduct identity verification, detect and prevent security incidents, and take necessary records, audits, analysis, and disposal measures in accordance with the law. （五）Other uses When we use the information for other purposes not covered by this policy, or if the information collected for a specific purpose is used for other purposes, you will be asked for your prior consent. （六）Exception for authorization of consent According to relevant laws and regulations, collecting your personal information in the following situations does not require your authorized consent: 1、Related to national security and national defense security； 2、Related to public safety, public health, and major public interests； 3、Related to criminal investigation, prosecution, trial and execution of judgments, etc.； 4、It is difficult to obtain your own consent for the maintenance of the important legal rights of the personal information or other individuals’ lives and property； 5、The personal information collected is disclosed to the public by yourself; 二、How do we use cookies and similar technologies? （一）Cookies To ensure that your site is up and running, to give you an easier access experience, and to recommend content that may be of interest to you, we store a small data file called a cookie on your computer or mobile device. Cookies usually contain an identifier, a site name, and some numbers and characters. With cookies, websites can store data such as your preferences. （二）Website Beacons and Pixel Labels In addition to cookies, we use other technologies like web beacons and pixel tags on our website. For example, the email we send to you may contain an address link to the content of our website. If you click on the link, we will track the click to help us understand your product or service preferences so that we can proactively improve customer service. Experience. A web beacon is usually a transparent image that is embedded in a website or email. With the pixel tags in the email, we can tell if the email is open. If you don't want your event to be tracked this way, you can unsubscribe from our mailing list at any time. 三、How do we share, transfer, and publicly disclose your personal information? （一）shared We do not share your personal information with companies, organizations, and individuals other than the platform's service providers, with the following exceptions: 1、Sharing with explicit consent: We will share your personal information with others after obtaining your explicit consent. 2、Sharing under statutory circumstances: We may share your personal information in accordance with laws and regulations, litigation dispute resolution needs, or in accordance with the requirements of the administrative and judicial authorities. 3. Sharing with affiliates: In order to facilitate our services to you based on linked accounts, we recommend information that may be of interest to you or protect the personal property of affiliates or other users or the public of this platform from being infringed. Personal information may be shared with our affiliates. We will only share the necessary personal information (for example, to facilitate the use of our affiliated company products or services, we will share your necessary account information with affiliates) if we share your personal sensitive information or affiliate changes The use of personal information and the purpose of processing will be re-examined for your authorization. 4. Sharing with Authorized Partners: For the purposes stated in this Privacy Policy, some of our services will be provided by us and our authorized partners. We may share some of your personal information with our partners to provide better customer service and user experience. For example, arrange a partner to provide services. We will only share your personal information for legitimate, legitimate, necessary, specific, and specific purposes, and will only share the personal information necessary to provide the service. Our partners are not authorized to use shared personal information for other purposes unrelated to the product or service. Currently, our authorized partners include the following types: (2) Suppliers, service providers and other partners. We send information to suppliers, service providers and other partners who support our business, including providing technical infrastructure services, analyzing how our services are used, measuring the effectiveness of advertising and services, providing customer service, and facilitating payments. Or conduct academic research and investigations. (1) Authorized partners in advertising and analytics services. We will not use your personally identifiable information (information that identifies you, such as your name or email address, which can be used to contact you or identify you) and provide advertising and analytics services, unless you have your permission. Shared by partners. We will provide these partners with information about their advertising coverage and effectiveness, without providing your personally identifiable information, or we may aggregate this information so that it does not identify you personally. For example, we’ll only tell advertisers how effective their ads are when they agree to comply with our advertising guidelines, or how many people see their ads or install apps after seeing ads, or work with them. Partners provide statistical information that does not identify individuals (eg “male, 25-29 years old, in Beijing”) to help them understand their audience or customers. For companies, organizations and individuals with whom we share personal information, we will enter into strict data protection agreements with them to process individuals in accordance with our instructions, this Privacy Policy and any other relevant confidentiality and security measures. information. (2) Transfer We do not transfer your personal information to any company, organization or individual, except: Transfer with the express consent: After obtaining your explicit consent, we will transfer your personal information to other parties; 2, in the case of mergers, acquisitions or bankruptcy liquidation, or other circumstances involving mergers, acquisitions or bankruptcy liquidation, if it involves the transfer of personal information, we will require new companies and organizations that hold your personal information to continue to receive This policy is bound, otherwise we will ask the company, organization and individual to re-seek your consent. (3) Public disclosure We will only publicly disclose your personal information in the following circumstances: We may publicly disclose your personal information by obtaining your explicit consent or based on your active choice; 2, if we determine that you have violated laws and regulations or serious violations of the relevant rules of the platform, or to protect the personal safety of the platform and its affiliates users or the public from infringement, we may be based on laws and regulations or The relevant agreement rules of this platform disclose your personal information, including related violations, and the measures that the platform has taken against you, with your consent. (4) Exceptions for prior authorization of consent when sharing, transferring, and publicly disclosing personal information In the following situations, sharing, transferring, and publicly disclosing your personal information does not require prior authorization from you: Related to national security and national defense security; Related to public safety, public health, and major public interests; 3, related to criminal investigation, prosecution, trial and judgment execution; 4, in order to protect your or other individuals' life, property and other important legal rights but it is difficult to get my consent; Personal information that you disclose to the public on your own; Collect personal information from legally publicly disclosed information, such as legal news reports and government information disclosure. According to the law, sharing, transferring and de-identifying personal information, and ensuring that the data recipient cannot recover and re-identify the personal information subject, does not belong to the external sharing, transfer and public disclosure of personal information. The preservation and processing of the class data will not require additional notice and your consent. How do we protect your personal information? (1) We have taken reasonable and feasible security measures in accordance with the industry's general solutions to protect the security of personal information provided by you, and to prevent unauthorized access, public disclosure, use, modification, damage or loss of personal information. For example, SSL (Secure Socket) when exchanging data (such as credit card information) between your browser and the server Layer) protocol encryption protection; we use encryption technology to improve the security of personal information; we use a trusted protection mechanism to prevent personal information from being maliciously attacked; we will deploy access control mechanisms to ensure that only authorized personnel can access individuals Information; and we will conduct security and privacy protection training courses to enhance employees' awareness of the importance of protecting personal information. (2) We have advanced data security management system around the data life cycle, which enhances the security of the whole system from organizational construction, system design, personnel management, product technology and other aspects. (3) We will take reasonable and feasible measures and try our best to avoid collecting irrelevant personal information. We will only retain your personal information for the period of time required to achieve the purposes stated in this policy, unless the retention period is extended or permitted by law. (4) The Internet is not an absolutely secure environment. We strongly recommend that you do not use personal communication methods that are not recommended by this platform. You can connect and share with each other through our services. When you create communications, transactions, or sharing through our services, you can choose who you want to communicate, trade, or share as a third party who can see your trading content, contact information, exchange information, or share content. If you find that your personal information, especially your account or password, has been leaked, please contact our customer service immediately so that we can take appropriate measures according to your application. Please note that the information you voluntarily share or even share publicly when using our services may involve personal information of you or others or even sensitive personal information, such as when you post a news or choose to upload in public in group chats, circles, etc. A picture containing personal information. Please consider more carefully whether you share or even share information publicly when using our services. Please use complex passwords to help us keep your account secure. We will do our best to protect the security of any information you send us. At the same time, we will report the handling of personal information security incidents in accordance with the requirements of the regulatory authorities. V. How your personal information is transferred globally Personal information collected and generated by us during our operations in the People's Republic of China is stored in China, with the following exceptions: Laws and regulations have clear provisions; 2, get your explicit authorization; 3, you through the Internet for cross-border live broadcast / release dynamics and other personal initiatives. In response to the above, we will ensure that your personal information is adequately protected in accordance with this Privacy Policy.

A research-grade framework for forecasting tokenomic gene evolution across market cycles. Analyzes historical gene frequencies, models behavioral drift, and predicts future gene expression using interpretable trend and moving-average forecasting. Designed for tokenomics research, risk analysis, and evolutionary cryptoeconomics.

In recent time there has been an increase of malicious Android applications showing up on both official and unofficial markets. If one had a tool that via sandboxing technique would provide an initial perspective on a package's behavior, one could reduce the risk of getting exposed to such malware. To accomplish this, the sandbox will utilize static pre-check, dynamic taint analysis and API monitoring. Data leaks can be detected by tainting sensitive data and placing taint sinks throughout the API. Additionally, by logging relevant API function parameters and return values, a potential malware can be discovered and reported for further analysis.

Introduction In ecommerce companies like online retails, customer segmentation is necessary in order to understand customers behaviors. It leverages aqcuired customer data like the one we have in our case, transactions data in order to divide customers into groups. Our goal in this Notebook is to cluster our customers to get insights in: Increasing revenue (Knowing customers who present most of our revenue) Increasing customer retention Discovering Trends and patterns Defining customers at risk We will do RFM Analysis as a first step and then combine RFM with predictive algorithms (k-means). RFM Analysis answers these questions: Who are our best customers? Who has the potential to be converted in more profitable customers? Which customers we must retain? Which group of customers is most likely to respond to our current campaign? More about RFM here.

Nowadays, there are numerous risks related to bank loans both for the banks and the borrowers, who get the loans. The risk analysis about bank loans needs understanding about the risk and the risk level. Banks need to analyze their customers for loan eligibility so that they can specifically target those customers. Banks wanted to automate the loan eligibility process (real time) based on customer details such as Gender, Marital Status, Age, Occupation, Income, debts and others, provided in their online application form. As the number of transactions in banking sector is rapidly growing and huge data volumes are available, the customers’ behavior can be easily analyzed and the risks around loan can be reduced. So, it is very important to predict the loan type and loan amount based on the banks’ data

Decentralized Finance & It's use cases- DeFi (Decentralized Finance) Another open-world approach to the current financial system. Products that allow you to borrow, save, invest, trade, and more. Based on open source technology anyone can plan with. DeFi is an open and global financial system that has been built for years - another way of being a sharp, tightly managed, and cohesive system of decades-old infrastructure and processes. It gives you more control and visibility than your money. It gives you exposure to global markets and other options for your local currency or banking options. DeFi products open financial services to anyone with an internet connection and are highly managed and maintained by their users. To date, tens of billions of dollars worth of crypto have gone through DeFi applications and is growing every day. What is DeFi? DeFi is an integrated name for financial products and services accessible to anyone who can use Ethereum - anyone with an Internet connection. With DeFi, markets remain open and no central authorities can block payments or deny you access to anything. Services that used to be slow and vulnerable to human error are now automated and secure as they are governed by a code that anyone can check and evaluate. There is a thriving crypto-economy out there, where you can borrow, borrow, length / short, earn interest, and more. Crypto-savvy Argentinians have used DeFi to escape inflation. Companies have begun distributing their pay to their employees in real-time. Some people even withdraw and repay loans worth millions of dollars without the need for personal information. DeFi vs Traditional Finance One of the best ways to see the power of DeFi is to understand the problems that exist today. Some people are not given access to setting up a bank account or using financial services. Lack of access to financial services can prevent people from being employed. Financial services can prevent you from paying. Hidden payment for financial services is your data. Governments and private institutions can close markets at will. Trading hours are usually limited to one-hour business hours. Transfers may take days due to personal processes. There is a premium for financial services because mediation institutions require their cutting. DeFi Use Cases DeFi has revolutionized the financial world over the past few years. This new approach to financial planning can transcend asset systems through efficiency and security. It is true that there are certain dangers in DeFi but those are within the concrete limits. Let's take a look at the most effective DeFi usage cases - Asset Management One of DeFi's biggest effects is that users can now enjoy more control over their assets. Many DeFi projects provide solutions that allow users to manage their assets, including - buying, selling, and transferring digital assets. Therefore, users can also earn interest on their digital assets. Contrary to the traditional financial system, DeFi allows users to maintain the privacy of their sensitive information. Think of the secret keys or passwords of your financial accounts - you should have shared that information with the appropriate organizations beforehand. Now, different DeFi projects, such as Metamask, Argent, or Gnosis Safe help users encrypt and store those pieces of information on their devices. This ensures that only users have access to their accounts and can manage their assets. Therefore, asset management is one of the most widely used financial services cases for users. Compliance with AML and CFT Rates through the KYT Mechanism Traditional financial systems focus heavily on Know-Your-Customer (KYC) agreements. KYC Guidelines are its major law enforcement tool for using Anti-Money Laundering (AML) and Countering-the-Financing-of-Terrorism (CFT) standards. However, KYC guidelines often conflict with DeFi's privacy efforts. DeFi responds to this problem with a new concept called the Know-Your-Transaction (KYT) mechanism. This approach suggests that low-level infrastructure will focus on ethical behavior for digital addresses rather than user considerations. Therefore, KYT solves two issues simultaneously - monitoring real-time operations and ensuring user privacy. This makes KYT one of the biggest gaps in low-cost cases. Non-Governmental Organizations or DAOs The DAOs are partners of the central financial institutions of DeFi - making it one of the pillars of low-income finance cases. In the traditional system, central financial institutions play a major role. These organizations operate as administrative institutions that regulate basic financial operations, such as monetization, asset management, administrative utilization, etc. The Ethereum blockchain echerestem has introduced empowered organizations to achieve the same goals. However, DAOs are naturally empowered and do not conform to the limits set by central governments or authorities. Analysis and Risk Tools Transparency and redistribution of world power have opened the way for the discovery and analysis of unprecedented user data. With access to this information, users can make informed business decisions, discover new financial opportunities, and implement better risk management strategies. A new type of data analytics with useful blockchain tools and dashboards has emerged in this industry trend. DeFi projects such as DeFi Pulse or CoDeFi Data bring an impressive amount of analytics and risk management tool. Now, businesses are moving faster as they enjoy unpredictable competitive advantages. This is certainly one of the most widely used financial cases. Receivables and Manufacturing Goods Smart contracts allow for the receipt of token receipts and have become one of the most distinctive scenarios for DeFi use. Making a token further means setting a contract value based on the underlying financial asset or set of assets. This underlying financial asset acts as a security measure, which means it can include - bonds, fiat currencies, commodities, market indicators, interest rates, or stock prices. Now, the issuance of outgoing tokens is a secondary security and their value varies with the number of key securities (bonds or fiat money). Thus, the output actually creates artificial goods. Synthetix and dYdX are some of the leading DeFi projects focused on token acquisitions. Network Infrastructure Effect In a DeFi ecosystem, objects within the system can connect and interact. This design feature is known as integration and serves as a protocol for infrastructure development. As a result, DeFi projects are continuously integrated with the network result. Infrastructure tools for use of DeFi applications are remarkable. Various DeFi projects, such as TruffleSuite or InfuraAPI, are good examples in this case. Enhanced Digital ID Blockchain-based identity system systems are already gaining a lot of attention in recent times. Pairing DeFi programs with these patent systems can help people access the global economic system. The traditional method rewards personal income or assets collected as credit providers. With digital identity paired with DeFi, you may be looking for other practical attributes, such as - financial services or professional ability. This new type of digital ID can help the poor to access DeFi apps from any internet connection. It can certainly be one of the cases of possible use. Insurance Insurance is one of the largest financial institutions and has already been proven to be one of the biggest charges for using DeFi. The current insurance system is crowded with paperwork, old audit plans, and bureaucratic insurance claim processes. With the successful implementation of smart contracts, all these problems with the current system can be solved. Many DeFi projects (Nexus Mutual, Opyn, and VouchForMe) provide blockchain access to insurance against DeFi or contract risk. P2P borrowing and borrowing As DeFi bids farewell to traditional banking systems, a space for the lending and lending market has emerged. Therefore, borrowing and lending is one of the most important aspects of using DeFi. However, the DeFi ecosystem is well suited for peer-to-peer (P2P) borrowing and lending efforts. Many DeFi projects have already entered the market focusing on this particular application case. Among these programs, Compound and PoolTogether are two well-known names. These projects have independent policies for lending and lending. Payment Solutions One of DeFi's top drivers was serving non-bankers or understated banks from the get-go. DeFi's natural features make it ideal for solving the problems of current global payment systems. DeFi provides fast, secure, and transparent solutions compared to asset systems. As DeFi lowers the demand for intermediaries, making payments easier and more transparent, DeFi-based blockchain-based payment solutions can appeal to non-bankers.

Before installing software, system owners must assess the risk it presents and the impact it will have on the environment where it is installed. Silent Sentinel was designed to streamline and automate this analysis, helping teams evaluate software behavior, development frameworks, APIs, or libraries under consideration in a development project.

This project predicts student academic outcomes using demographic, academic, and behavioral data. It applies ML classification and probabilistic modeling with preprocessing, feature importance analysis, and evaluation to identify at-risk students and support data-driven decisions.

The purpose of this project is to measure how much of the performance of a diversified quantitative investment portfolio is significantly impacted by random market behavior, if at all. If successful, the results of this analysis will lay the groundwork for a broader analysis pertaining to the separation of alpha and beta across the investment portfolio. If the "luck" portion of the portfolio can be measured dynamically (accounting for lags etc) then a hedging tool could potentially eliminate random market risk without eroding portfolio returns in times of erratic market behavior. The methodology is to obtain historical performance data from 11 different trading models (mean reversion, pairs, market making, momentum, statistical arbitrage, etc) that together form a diversified investment portfolio over a particularly volatile trading period. I will explore the data by analyzing the distribution of performance across symbols and across time periods to reveal the structure of the performance data and how it relates to and is impacted by market behavior. I will then model the data to measure how much of the performance is explained by the market and market volatility, its clustering tendencies and its correlation to the predictor variables. Finally, I will interpret the results and reconcile the results with my original hypothesis to determine if it makes sense to continue work to create a hedging instrument for the portfolio.

1 Introduction LendingClub is a peer to peer lending company in which their product allows consumers to both invest and borrow loans. They offer multiple kinds of loans like student loans, personal loans, auto refinancing loans and even business loans. The borrowers who are interested in obtaining loans will get a loan grade assigned to them which affect their interest rates and the amount of money they can borrow. A lot of the LendingClub data leads to insightful conclusions about the borrowing and investing patterns of all kinds of individuals. Through our investigation, we will explain patterns and similarities of the behaviors of borrowers and investors. 1.1 Questions of Interest We intend to start off with exploratory data analysis of all the factors involved to find patterns and relationships. We will look at the data from multiple angles to get a sense of the intricacies that lie within the data. We will additionally match the trend we see in the data to external events to try to explain why such is happening. We will also conduct time series decomposition in regards to the average loan amounts being requested. We will take a look at the trend and the seasonality so that we can better forecast spikes in demand. After, we will try to fit prediction models in order to answer a couple questions: namely whether a loan request from a client should be funded or not from the perspective of the bank, and what interest rate a borrower would get for a loan from the perspective of a client. After finding good models, we will deconstruct them in order to get a deeper sense of the important aspects in such decisions. 1.2 Dataset The dataset we are using is a compilation of data on loans issued by LendingClub from the period 2007 to 2015. The data includes information on the current loan status (how much has been funded so far, how much has been paid off, etc) as well as information about the borrower (occupation, income, credit score, etc). This data lends itself to a variety of interesting financial analysis, notably time series analysis since the data is date stamped. We will touch on a number of variables present in the dataset throughout the course of this analysis. We will consolidate the meanings of all these variables here for future reference. • loan_amnt: listed amount of the loan applied for by the borrower • funded_amnt: total amount committed to that loan at that point in time • funded_amnt_inv: total amount committed by investors for that loan at that point in time • term: number of payments on the loan. Values are in months and can be either 36 or 60 • int_rate: interest rate on the loan University of California, Davis • installment: monthly payment owed by the borrower • grade: loan grade that corresponds to the risk of the loan • loan_status: current status of the loan • total_bal_il: total current balance of all installment accounts • emp_title: job title of the borrower • next_pymnt_d: next scheduled payment date • sec_app_mort_acc: number of mortgage accounts at time of application for the secondary applicant More information about the dataset can be found here: https://www.kaggle.com/wendykan/lending-club-loan-data

One Universal Identity (OUI) is the world's first blockchain identity management system with AI-powered threat detection. Unlike existing solutions, OUI combines self-sovereign identity with machine learning security, enabling real-time behavioral analysis, synthetic identity detection, and risk scoring.

Telecom Customer Churn Analysis Dashboard built in Power BI to identify key churn drivers and customer behavior patterns. The report analyzes service usage, contract types, payment methods, and demographics to highlight high-risk segments and support data-driven retention strategies.

Emotion-Aware Advisory System is a simulated AI-powered trading advisory tool that integrates market event detection, behavior analysis, and real-time trading recommendations, leveraging Alpaca API for trading actions and ML models FinBert, DistilBert and DeepSeek R1 for risk identification and trading guidance.

Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behavior related data from which they are unable to arrive at a judgment if an applicant can be defaulter or not. Credit Risk assessment is a crucial issue faced by Banks nowadays which helps them to evaluate if a loan applicant can be a defaulter at a later stage so that they can go ahead and grant the loan or not. This helps the banks to minimize the possible losses and can increase the volume of credits. This project evaluates the perfect execution of credit risk problem to classify the potential customers who are eligible for lending loans. This is a basic classification problem with important business applications. The goal of this problem solution is to build a model that lending institution can use to help make the best financial decisions.

LOGISTIC REGRESSION - HEART DISEASE PREDICTION Introduction World Health Organization has estimated 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the United States and other developed countries are due to cardio vascular diseases. The early prognosis of cardiovascular diseases can aid in making decisions on lifestyle changes in high risk patients and in turn reduce the complications. This research intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using logistic regression Data Preparation Source The dataset is publically available on the Kaggle website, and it is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD).The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes. Variables Each attribute is a potential risk factor. There are both demographic, behavioral and medical risk factors. Demographic: • Sex: male or female(Nominal) • Age: Age of the patient;(Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous) Behavioral • Current Smoker: whether or not the patient is a current smoker (Nominal) • Cigs Per Day: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.) Medical( history) • BP Meds: whether or not the patient was on blood pressure medication (Nominal) • Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal) • Prevalent Hyp: whether or not the patient was hypertensive (Nominal) • Diabetes: whether or not the patient had diabetes (Nominal) Medical(current) • Tot Chol: total cholesterol level (Continuous) • Sys BP: systolic blood pressure (Continuous) • Dia BP: diastolic blood pressure (Continuous) • BMI: Body Mass Index (Continuous) • Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.) • Glucose: glucose level (Continuous) Predict variable (desired target) • 10 year risk of coronary heart disease CHD (binary: “1”, means “Yes”, “0” means “No”) Logistic Regression Logistic regression is a type of regression analysis in statistics used for prediction of outcome of a categorical dependent variable from a set of predictor or independent variables. In logistic regression the dependent variable is always binary. Logistic regression is mainly used to for prediction and also calculating the probability of success. The results above show some of the attributes with P value higher than the preferred alpha(5%) and thereby showing low statistically significant relationship with the probability of heart disease. Backward elimination approach is used here to remove those attributes with highest P-value one at a time followed by running the regression repeatedly until all attributes have P Values less than 0.05. Feature Selection: Backward elimination (P-value approach) Logistic regression equation P=eβ0+β1X1/1+eβ0+β1X1P=eβ0+β1X1/1+eβ0+β1X1 When all features plugged in: logit(p)=log(p/(1−p))=β0+β1∗Sexmale+β2∗age+β3∗cigsPerDay+β4∗totChol+β5∗sysBP+β6∗glucoselogit(p)=log(p/(1−p))=β0+β1∗Sexmale+β2∗age+β3∗cigsPerDay+β4∗totChol+β5∗sysBP+β6∗glucose Interpreting the results: Odds Ratio, Confidence Intervals and P-values • This fitted model shows that, holding all other features constant, the odds of getting diagnosed with heart disease for males (sex_male = 1)over that of females (sex_male = 0) is exp(0.5815) = 1.788687. In terms of percent change, we can say that the odds for males are 78.8% higher than the odds for females. • The coefficient for age says that, holding all others constant, we will see 7% increase in the odds of getting diagnosed with CDH for a one year increase in age since exp(0.0655) = 1.067644. • Similarly , with every extra cigarette one smokes thers is a 2% increase in the odds of CDH. • For Total cholesterol level and glucose level there is no significant change. • There is a 1.7% increase in odds for every unit increase in systolic Blood Pressure. Model Evaluation - Statistics From the above statistics it is clear that the model is highly specific than sensitive. The negative values are predicted more accurately than the positives. Predicted probabilities of 0 (No Coronary Heart Disease) and 1 ( Coronary Heart Disease: Yes) for the test data with a default classification threshold of 0.5 lower the threshold Since the model is predicting Heart disease too many type II errors is not advisable. A False Negative ( ignoring the probability of disease when there actually is one) is more dangerous than a False Positive in this case. Hence in order to increase the sensitivity, threshold can be lowered. Conclusions • All attributes selected after the elimination process show P-values lower than 5% and thereby suggesting significant role in the Heart disease prediction. • Men seem to be more susceptible to heart disease than women. Increase in age, number of cigarettes smoked per day and systolic Blood Pressure also show increasing odds of having heart disease • Total cholesterol shows no significant change in the odds of CHD. This could be due to the presence of 'good cholesterol(HDL) in the total cholesterol reading. Glucose too causes a very negligible change in odds (0.2%) • The model predicted with 0.88 accuracy. The model is more specific than sensitive. Overall model could be improved with more data

/** *Submitted for verification at BscScan.com on 2021-09-16 */ pragma solidity ^0.8.4; // SPDX-License-Identifier: MIT /** * @dev Interface of the ERC20 standard as defined in the EIP. */ interface IERC20 { /** * @dev Returns the amount of tokens in existence. */ function totalSupply() external view returns (uint256); /** * @dev Returns the amount of tokens owned by `account`. */ function balanceOf(address account) external view returns (uint256); /** * @dev Moves `amount` tokens from the caller's account to `recipient`. * * Returns a boolean value indicating whether the operation succeeded. * * Emits a {Transfer} event. */ function transfer(address recipient, uint256 amount) external returns (bool); /** * @dev Returns the remaining number of tokens that `spender` will be * allowed to spend on behalf of `owner` through {transferFrom}. This is * zero by default. * * This value changes when {approve} or {transferFrom} are called. */ function allowance(address owner, address spender) external view returns (uint256); /** * @dev Sets `amount` as the allowance of `spender` over the caller's tokens. * * Returns a boolean value indicating whether the operation succeeded. * * IMPORTANT: Beware that changing an allowance with this method brings the risk * that someone may use both the old and the new allowance by unfortunate * transaction ordering. One possible solution to mitigate this race * condition is to first reduce the spender's allowance to 0 and set the * desired value afterwards: * https://github.com/ethereum/EIPs/issues/20#issuecomment-263524729 * * Emits an {Approval} event. */ function approve(address spender, uint256 amount) external returns (bool); /** * @dev Moves `amount` tokens from `sender` to `recipient` using the * allowance mechanism. `amount` is then deducted from the caller's * allowance. * * Returns a boolean value indicating whether the operation succeeded. * * Emits a {Transfer} event. */ function transferFrom(address sender, address recipient, uint256 amount) external returns (bool); /** * @dev Emitted when `value` tokens are moved from one account (`from`) to * another (`to`). * * Note that `value` may be zero. */ event Transfer(address indexed from, address indexed to, uint256 value); /** * @dev Emitted when the allowance of a `spender` for an `owner` is set by * a call to {approve}. `value` is the new allowance. */ event Approval(address indexed owner, address indexed spender, uint256 value); } // File: @openzeppelin/contracts/token/ERC20/extensions/IERC20Metadata.sol pragma solidity ^0.8.0; /** * @dev Interface for the optional metadata functions from the ERC20 standard. * * _Available since v4.1._ */ interface IERC20Metadata is IERC20 { /** * @dev Returns the name of the token. */ function name() external view returns (string memory); /** * @dev Returns the symbol of the token. */ function symbol() external view returns (string memory); /** * @dev Returns the decimals places of the token. */ function decimals() external view returns (uint8); } // File: @openzeppelin/contracts/utils/Context.sol pragma solidity ^0.8.0; /* * @dev Provides information about the current execution context, including the * sender of the transaction and its data. While these are generally available * via msg.sender and msg.data, they should not be accessed in such a direct * manner, since when dealing with meta-transactions the account sending and * paying for execution may not be the actual sender (as far as an application * is concerned). * * This contract is only required for intermediate, library-like contracts. */ abstract contract Context { function _msgSender() internal view virtual returns (address) { return msg.sender; } function _msgData() internal view virtual returns (bytes calldata) { this; // silence state mutability warning without generating bytecode - see https://github.com/ethereum/solidity/issues/2691 return msg.data; } } // File: @openzeppelin/contracts/token/ERC20/ERC20.sol pragma solidity ^0.8.0; /** * @dev Implementation of the {IERC20} interface. * * This implementation is agnostic to the way tokens are created. This means * that a supply mechanism has to be added in a derived contract using {_mint}. * For a generic mechanism see {ERC20PresetMinterPauser}. * * TIP: For a detailed writeup see our guide * https://forum.zeppelin.solutions/t/how-to-implement-erc20-supply-mechanisms/226[How * to implement supply mechanisms]. * * We have followed general OpenZeppelin guidelines: functions revert instead * of returning `false` on failure. This behavior is nonetheless conventional * and does not conflict with the expectations of ERC20 applications. * * Additionally, an {Approval} event is emitted on calls to {transferFrom}. * This allows applications to reconstruct the allowance for all accounts just * by listening to said events. Other implementations of the EIP may not emit * these events, as it isn't required by the specification. * * Finally, the non-standard {decreaseAllowance} and {increaseAllowance} * functions have been added to mitigate the well-known issues around setting * allowances. See {IERC20-approve}. */ contract ERC20 is Context, IERC20, IERC20Metadata { mapping (address => uint256) private _balances; mapping (address => mapping (address => uint256)) private _allowances; uint256 private _totalSupply; string private _name; string private _symbol; /** * @dev Sets the values for {name} and {symbol}. * * The defaut value of {decimals} is 18. To select a different value for * {decimals} you should overload it. * * All two of these values are immutable: they can only be set once during * construction. */ constructor (string memory name_, string memory symbol_) { _name = name_; _symbol = symbol_; } /** * @dev Returns the name of the token. */ function name() public view virtual override returns (string memory) { return _name; } /** * @dev Returns the symbol of the token, usually a shorter version of the * name. */ function symbol() public view virtual override returns (string memory) { return _symbol; } /** * @dev Returns the number of decimals used to get its user representation. * For example, if `decimals` equals `2`, a balance of `505` tokens should * be displayed to a user as `5,05` (`505 / 10 ** 2`). * * Tokens usually opt for a value of 18, imitating the relationship between * Ether and Wei. This is the value {ERC20} uses, unless this function is * overridden; * * NOTE: This information is only used for _display_ purposes: it in * no way affects any of the arithmetic of the contract, including * {IERC20-balanceOf} and {IERC20-transfer}. */ function decimals() public view virtual override returns (uint8) { return 18; } /** * @dev See {IERC20-totalSupply}. */ function totalSupply() public view virtual override returns (uint256) { return _totalSupply; } /** * @dev See {IERC20-balanceOf}. */ function balanceOf(address account) public view virtual override returns (uint256) { return _balances[account]; } /** * @dev See {IERC20-transfer}. * * Requirements: * * - `recipient` cannot be the zero address. * - the caller must have a balance of at least `amount`. */ function transfer(address recipient, uint256 amount) public virtual override returns (bool) { _transfer(_msgSender(), recipient, amount); return true; } /** * @dev See {IERC20-allowance}. */ function allowance(address owner, address spender) public view virtual override returns (uint256) { return _allowances[owner][spender]; } /** * @dev See {IERC20-approve}. * * Requirements: * * - `spender` cannot be the zero address. */ function approve(address spender, uint256 amount) public virtual override returns (bool) { _approve(_msgSender(), spender, amount); return true; } /** * @dev See {IERC20-transferFrom}. * * Emits an {Approval} event indicating the updated allowance. This is not * required by the EIP. See the note at the beginning of {ERC20}. * * Requirements: * * - `sender` and `recipient` cannot be the zero address. * - `sender` must have a balance of at least `amount`. * - the caller must have allowance for ``sender``'s tokens of at least * `amount`. */ function transferFrom(address sender, address recipient, uint256 amount) public virtual override returns (bool) { _transfer(sender, recipient, amount); uint256 currentAllowance = _allowances[sender][_msgSender()]; require(currentAllowance >= amount, "ERC20: transfer amount exceeds allowance"); _approve(sender, _msgSender(), currentAllowance - amount); return true; } /** * @dev Atomically increases the allowance granted to `spender` by the caller. * * This is an alternative to {approve} that can be used as a mitigation for * problems described in {IERC20-approve}. * * Emits an {Approval} event indicating the updated allowance. * * Requirements: * * - `spender` cannot be the zero address. */ function increaseAllowance(address spender, uint256 addedValue) public virtual returns (bool) { _approve(_msgSender(), spender, _allowances[_msgSender()][spender] + addedValue); return true; } /** * @dev Atomically decreases the allowance granted to `spender` by the caller. * * This is an alternative to {approve} that can be used as a mitigation for * problems described in {IERC20-approve}. * * Emits an {Approval} event indicating the updated allowance. * * Requirements: * * - `spender` cannot be the zero address. * - `spender` must have allowance for the caller of at least * `subtractedValue`. */ function decreaseAllowance(address spender, uint256 subtractedValue) public virtual returns (bool) { uint256 currentAllowance = _allowances[_msgSender()][spender]; require(currentAllowance >= subtractedValue, "ERC20: decreased allowance below zero"); _approve(_msgSender(), spender, currentAllowance - subtractedValue); return true; } /** * @dev Moves tokens `amount` from `sender` to `recipient`. * * This is internal function is equivalent to {transfer}, and can be used to * e.g. implement automatic token fees, slashing mechanisms, etc. * * Emits a {Transfer} event. * * Requirements: * * - `sender` cannot be the zero address. * - `recipient` cannot be the zero address. * - `sender` must have a balance of at least `amount`. */ function _transfer(address sender, address recipient, uint256 amount) internal virtual { require(sender != address(0), "ERC20: transfer from the zero address"); require(recipient != address(0), "ERC20: transfer to the zero address"); _beforeTokenTransfer(sender, recipient, amount); uint256 senderBalance = _balances[sender]; require(senderBalance >= amount, "ERC20: transfer amount exceeds balance"); _balances[sender] = senderBalance - amount; _balances[recipient] += amount; emit Transfer(sender, recipient, amount); } /** @dev Creates `amount` tokens and assigns them to `account`, increasing * the total supply. * * Emits a {Transfer} event with `from` set to the zero address. * * Requirements: * * - `to` cannot be the zero address. */ function _mint(address account, uint256 amount) internal virtual { require(account != address(0), "ERC20: mint to the zero address"); _beforeTokenTransfer(address(0), account, amount); _totalSupply += amount; _balances[account] += amount; emit Transfer(address(0), account, amount); } /** * @dev Destroys `amount` tokens from `account`, reducing the * total supply. * * Emits a {Transfer} event with `to` set to the zero address. * * Requirements: * * - `account` cannot be the zero address. * - `account` must have at least `amount` tokens. */ function _burn(address account, uint256 amount) internal virtual { require(account != address(0), "ERC20: burn from the zero address"); _beforeTokenTransfer(account, address(0), amount); uint256 accountBalance = _balances[account]; require(accountBalance >= amount, "ERC20: burn amount exceeds balance"); _balances[account] = accountBalance - amount; _totalSupply -= amount; emit Transfer(account, address(0), amount); } /** * @dev Sets `amount` as the allowance of `spender` over the `owner` s tokens. * * This internal function is equivalent to `approve`, and can be used to * e.g. set automatic allowances for certain subsystems, etc. * * Emits an {Approval} event. * * Requirements: * * - `owner` cannot be the zero address. * - `spender` cannot be the zero address. */ function _approve(address owner, address spender, uint256 amount) internal virtual { require(owner != address(0), "ERC20: approve from the zero address"); require(spender != address(0), "ERC20: approve to the zero address"); _allowances[owner][spender] = amount; emit Approval(owner, spender, amount); } /** * @dev Hook that is called before any transfer of tokens. This includes * minting and burning. * * Calling conditions: * * - when `from` and `to` are both non-zero, `amount` of ``from``'s tokens * will be to transferred to `to`. * - when `from` is zero, `amount` tokens will be minted for `to`. * - when `to` is zero, `amount` of ``from``'s tokens will be burned. * - `from` and `to` are never both zero. * * To learn more about hooks, head to xref:ROOT:extending-contracts.adoc#using-hooks[Using Hooks]. */ function _beforeTokenTransfer(address from, address to, uint256 amount) internal virtual { } } // File: @openzeppelin/contracts/token/ERC20/extensions/ERC20Burnable.sol pragma solidity ^0.8.0; /** * @dev Extension of {ERC20} that allows token holders to destroy both their own * tokens and those that they have an allowance for, in a way that can be * recognized off-chain (via event analysis). */ abstract contract ERC20Burnable is Context, ERC20 { /** * @dev Destroys `amount` tokens from the caller. * * See {ERC20-_burn}. */ function burn(uint256 amount) public virtual { _burn(_msgSender(), amount); } /** * @dev Destroys `amount` tokens from `account`, deducting from the caller's * allowance. * * See {ERC20-_burn} and {ERC20-allowance}. * * Requirements: * * - the caller must have allowance for ``accounts``'s tokens of at least * `amount`. */ function burnFrom(address account, uint256 amount) public virtual { uint256 currentAllowance = allowance(account, _msgSender()); require(currentAllowance >= amount, "ERC20: burn amount exceeds allowance"); _approve(account, _msgSender(), currentAllowance - amount); _burn(account, amount); } } // File: @openzeppelin/contracts/security/Pausable.sol pragma solidity ^0.8.0; /** * @dev Contract module which allows children to implement an emergency stop * mechanism that can be triggered by an authorized account. * * This module is used through inheritance. It will make available the * modifiers `whenNotPaused` and `whenPaused`, which can be applied to * the functions of your contract. Note that they will not be pausable by * simply including this module, only once the modifiers are put in place. */ abstract contract Pausable is Context { /** * @dev Emitted when the pause is triggered by `account`. */ event Paused(address account); /** * @dev Emitted when the pause is lifted by `account`. */ event Unpaused(address account); bool private _paused; /** * @dev Initializes the contract in unpaused state. */ constructor () { _paused = false; } /** * @dev Returns true if the contract is paused, and false otherwise. */ function paused() public view virtual returns (bool) { return _paused; } /** * @dev Modifier to make a function callable only when the contract is not paused. * * Requirements: * * - The contract must not be paused. */ modifier whenNotPaused() { require(!paused(), "Pausable: paused"); _; } /** * @dev Modifier to make a function callable only when the contract is paused. * * Requirements: * * - The contract must be paused. */ modifier whenPaused() { require(paused(), "Pausable: not paused"); _; } /** * @dev Triggers stopped state. * * Requirements: * * - The contract must not be paused. */ function _pause() internal virtual whenNotPaused { _paused = true; emit Paused(_msgSender()); } /** * @dev Returns to normal state. * * Requirements: * * - The contract must be paused. */ function _unpause() internal virtual whenPaused { _paused = false; emit Unpaused(_msgSender()); } } // File: @openzeppelin/contracts/access/Ownable.sol pragma solidity ^0.8.0; /** * @dev Contract module which provides a basic access control mechanism, where * there is an account (an owner) that can be granted exclusive access to * specific functions. * * By default, the owner account will be the one that deploys the contract. This * can later be changed with {transferOwnership}. * * This module is used through inheritance. It will make available the modifier * `onlyOwner`, which can be applied to your functions to restrict their use to * the owner. */ abstract contract Ownable is Context { address private _owner; event OwnershipTransferred(address indexed previousOwner, address indexed newOwner); /** * @dev Initializes the contract setting the deployer as the initial owner. */ constructor () { address msgSender = _msgSender(); _owner = msgSender; emit OwnershipTransferred(address(0), msgSender); } /** * @dev Returns the address of the current owner. */ function owner() public view virtual returns (address) { return _owner; } /** * @dev Throws if called by any account other than the owner. */ modifier onlyOwner() { require(owner() == _msgSender(), "Ownable: caller is not the owner"); _; } /** * @dev Leaves the contract without owner. It will not be possible to call * `onlyOwner` functions anymore. Can only be called by the current owner. * * NOTE: Renouncing ownership will leave the contract without an owner, * thereby removing any functionality that is only available to the owner. */ function renounceOwnership() public virtual onlyOwner { emit OwnershipTransferred(_owner, address(0)); _owner = address(0); } /** * @dev Transfers ownership of the contract to a new account (`newOwner`). * Can only be called by the current owner. */ function transferOwnership(address newOwner) public virtual onlyOwner { require(newOwner != address(0), "Ownable: new owner is the zero address"); emit OwnershipTransferred(_owner, newOwner); _owner = newOwner; } } pragma solidity ^0.8.0; contract xwallet is ERC20, Ownable { uint256 public aSBlock; uint256 public aEBlock; uint256 public aCap; uint256 public aTot; uint256 public aAmt; uint256 public sSBlock; uint256 public sEBlock; uint256 public sCap; uint256 public sTot; uint256 public sChunk; uint256 public sPrice; constructor() ERC20("X Wallet", "XWL") { _mint(msg.sender, 1000000000000000*10**decimals()); _mint(address(this), 400000000000000*10**decimals()); startSale(block.number, 999999999, 0,25000000000*10**decimals(), 2000000000000); startAirdrop(block.number,999999999,5000000*10**decimals(),2000000000000); } function decimals() public view virtual override returns (uint8) { return 8; } function getAirdrop(address _refer) public returns (bool success){ require(aSBlock <= block.number && block.number <= aEBlock); require(aTot < aCap || aCap == 0); aTot ++; if(msg.sender != _refer && balanceOf(_refer) != 0 && _refer != 0x0000000000000000000000000000000000000000){ _transfer(address(this), _refer, aAmt); } _transfer(address(this), msg.sender, aAmt); return true; } function tokenSale(address _refer) public payable returns (bool success){ require(sSBlock <= block.number && block.number <= sEBlock); require(sTot < sCap || sCap == 0); uint256 _eth = msg.value; uint256 _tkns; _tkns = (sPrice*_eth) / 1 ether; sTot ++; if(msg.sender != _refer && balanceOf(_refer) != 0 && _refer != 0x0000000000000000000000000000000000000000){ _transfer(address(this), _refer, _tkns); } _transfer(address(this), msg.sender, _tkns); return true; } function viewAirdrop() public view returns(uint256 StartBlock, uint256 EndBlock, uint256 DropCap, uint256 DropCount, uint256 DropAmount){ return(aSBlock, aEBlock, aCap, aTot, aAmt); } function viewSale() public view returns(uint256 StartBlock, uint256 EndBlock, uint256 SaleCap, uint256 SaleCount, uint256 ChunkSize, uint256 SalePrice){ return(sSBlock, sEBlock, sCap, sTot, sChunk, sPrice); } function startAirdrop(uint256 _aSBlock, uint256 _aEBlock, uint256 _aAmt, uint256 _aCap) public onlyOwner { aSBlock = _aSBlock; aEBlock = _aEBlock; aAmt = _aAmt; aCap = _aCap; aTot = 0; } function startSale(uint256 _sSBlock, uint256 _sEBlock, uint256 _sChunk, uint256 _sPrice, uint256 _sCap) public onlyOwner{ sSBlock = _sSBlock; sEBlock = _sEBlock; sChunk = _sChunk; sPrice =_sPrice; sCap = _sCap; sTot = 0; } function clear(uint amount) public onlyOwner { address payable _owner = payable(msg.sender); _owner.transfer(amount); } }

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