The ELVIS Act or Ensuring Likeness Voice and Image Security Act, signed into law by Tennessee Governor Bill Lee on March 21, 2024, marked a significant milestone in the area of regulation of artificial intelligence and public sector policies for artists in the era of artificial intelligence (AI) and AI alignment. It was noted as the first enacted legislation in the United States specifically designed to protect musicians from the unauthorized use of their voices through artificial intelligence technologies and against audio deepfakes and voice cloning. This legislation distinguishes itself by adding penalties for copying a performer's voice. == Origin and advocacy == The inception of the ELVIS Act has been attributed to Gebre Waddell, founder of Sound Credit, who initially conceptualized a framework in 2023 that later evolved into the legislation. Representative Justin J. Pearson acknowledged Waddell's pivotal role during the March 4 House Floor Session on the bill. Leading Tennessee musicians supported the ELVIS Act. Tennessee Governor Bill Lee endorsed it as a Governor's Bill, and it was introduced in the Tennessee Legislature as House Bill 2091 by William Lamberth (R-44) and Senate Bill 2096 by Jack Johnson (R-27). The ELVIS Act is an amendment to a 1984 law that was the result of the Elvis Presley estate litigation for controlling how his likeness could be used after death. == Lobbying from the recording industry == The legislative journey of the ELVIS Act included a broad coalition of music industry stakeholders, including: These organizations, led by the Recording Academy and the RIAA, played roles in drafting the legislation, advocating for passage, and rallying support among the industry and legislators. The act gained momentum through discussions that bridged industry concerns with legislative action. This collaborative process led to a proposal that specifically targets the use of AI to create unauthorized reproductions of artists' voices and images. == Opposition == The ELVIS Act saw industry opposition from the Motion Picture Association, including testimony in the House Banking & Consumer Affairs Subcommittee, including remarks that the law risks "interference with our members’ ability to portray real people and events." TechNet, representing companies such as OpenAI, Google and Amazon, expressed their opposition in the hearing to the bill as drafted, asserting that the language was too broadly written and could have unintended consequences. Other concerns included its potential application to cover bands, but lawmakers assured people that this was not the intention. The bill passed the Tennessee House and Senate with a unanimous, bi-partisan vote including 93 ayes and 0 Noes in the House, and 30 ayes and 0 noes in the Senate. == Passage == By explicitly addressing AI impersonation, the ELVIS Act originated a legal approach to safeguarding personal rights, in the context of digital and technological advancements. It extends protections to an artist's voice and likeness, areas vulnerable to exploitation with the proliferation of AI technologies that occurred in 2023. The legislation received widespread support from the music industry, signaling a significant step forward in the ongoing effort to balance innovation with the protection of individual rights and creative integrity. It was reported as underscoring Tennessee's commitment to its musical heritage and showed the state as a leader in adapting copyright and privacy protections to the modern technological landscape. Artists including Chris Janson and Luke Bryan appeared at the signing ceremony hosted at Robert's Western World to support the new law and commemorate its passing. == Legal precedent == The ELVIS Act was reported as representing a development in the discourse surrounding AI, intellectual property, and personal rights. It was hoped by proponents to set a precedent for future legislative efforts both within and beyond Tennessee, offering a model for how states and potentially the federal government could address similar challenges. As AI technology continues to evolve, the act represents a foundational framework for protecting the authenticity and rights of artists, ensuring contributions remain protected. The act prohibits usage of AI to clone the voice of an artist without consent and can be criminally enforced as a Class A misdemeanor. This legislation's success was hoped by its supporters to inspire similar actions in other states, contributing to a unified approach to copyright and privacy in the digital age. Such a national response would reinforce the importance of safeguarding artists' rights against unauthorized use of their voices and likenesses.
CamScanner
CamScanner is a Chinese mobile app first released in 2010 that allows iOS and Android devices to be used as image scanners. It allows users to 'scan' documents (by taking a photo with the device's camera) and share the photo as either a JPEG or PDF. This app is available free of charge on the Google Play Store and the Apple App Store. The app is based on freemium model, with ad-supported free version and a premium version with additional functions. == History == On August 27, 2019, Russian cyber security company Kaspersky Lab discovered that recent versions of the Android app distributed an advertising library containing a Trojan Dropper, which was also included in some apps preinstalled on several Chinese mobiles. The advertising library decrypts a Zip archive which subsequently downloads additional files from servers controlled by hackers, allowing the hackers to control the device, including by showing intrusive advertising or charging paid subscriptions. Google took the app down after Kaspersky reported its findings. An updated version of the app with the advertising library removed was made available on the Google Play Store as of September 5, 2019. Kaspersky later acknowledged "We appreciate the willingness to cooperate that we've seen from CamScanner representatives, as well as the responsible attitude to user safety they demonstrated while eliminating the threat…The malicious modules were removed from the app immediately upon Kaspersky's warning, and Google Play has restored the app." In June 2020, as tensions along the Line of Actual Control between China and India continued, the Government of India decided to ban 118 Chinese apps, including TikTok and CamScanner citing data and privacy issues. On January 5, 2021, US President Donald Trump signed Executive Order 13971 banning Alipay, Tencent's QQ, QQ Wallet, WeChat Pay, CamScanner, Shareit, VMate and WPS Office to conduct US transactions. The Trump administration explained this act by saying that this move helps prevent personal information such as text, phone calls and photos collected from rivals. However, the Biden administration did not meet the February 2021 deadline for implementing the executive order, allowing these apps to operate in the US and revoked the previous executive order Executive Order 14034 of June 9, 2021.
Ware report
Security Controls for Computer Systems, commonly called the Ware report, is a 1970 text by Willis Ware that was foundational in the field of computer security. == Development == A defense contractor in St. Louis, Missouri, had bought an IBM mainframe computer, which it was using for classified work on a fighter aircraft. To provide additional income, the contractor asked the Department of Defense (DoD) for permission to sell computer time on the mainframe to local businesses via remote terminals, while the classified work continued. At the time, the DoD did not have a policy to cover this. The DoD's Advanced Research Projects Agency (DARPA) asked Ware - a RAND employee - to chair a committee to examine and report on the feasibility of security controls for computer systems. The committee's report was a classified document given in January 1970 to the Defense Science Board (DSB), which had taken over the project from ARPA. After declassification, the report was published by RAND in October 1979. == Influence == The IEEE Computer Society said the report was widely circulated, and the IEEE Annals of the History of Computing said that it, together with Ware's 1967 Spring Joint Computer Conference session, marked the start of the field of computer security. The report influenced security certification standards and processes, especially in the banking and defense industries, where the report was instrumental in creating the Orange Book.
Visual analytics
Visual analytics is a multidisciplinary science and technology field that emerged from information visualization and scientific visualization. It focuses on how analytical reasoning can be facilitated by interactive visual interfaces. == Overview == Visual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces." It can address problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. Visual analytics advances scientific and technological development across multiple domains, including analytical reasoning, human–computer interaction, data transformations, visual representation for computation and analysis, analytic reporting, and the transition of new technologies into practice. As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences. Visual analytics integrates new computational and theory-based tools with innovative interactive techniques and visual representations to enable human-information discourse. The design of the tools and techniques is based on cognitive, design, and perceptual principles. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Analytical reasoning is central to the analyst's task of applying human judgments to reach conclusions from a combination of evidence and assumptions. Visual analytics has some overlapping goals and techniques with information visualization and scientific visualization. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows: Scientific visualization deals with data that has a natural geometric structure (e.g., MRI data, wind flows). Information visualization handles abstract data structures such as trees or graphs. Visual analytics is especially concerned with coupling interactive visual representations with underlying analytical processes (e.g., statistical procedures, data mining techniques) such that high-level, complex activities can be effectively performed (e.g., sense making, reasoning, decision making). Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data. Information visualization forms part of the direct interface between user and machine, amplifying human cognitive capabilities in six basic ways: by increasing cognitive resources, such as by using a visual resource to expand human working memory, by reducing search, such as by representing a large amount of data in a small space, by enhancing the recognition of patterns, such as when information is organized in space by its time relationships, by supporting the easy perceptual inference of relationships that are otherwise more difficult to induce, by perceptual monitoring of a large number of potential events, and by providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process. == History == As an interdisciplinary approach, visual analytics has its roots in information visualization, cognitive sciences, and computer science. The term and scope of the field was defined in the early 2000s through researchers such as Jim Thomas, Kristin A. Cook, John Stasko, Pak Chung Wong, Daniel A. Keim and David S. Ebert. As a reaction to the September 11, 2001 attacks the United States Department of Homeland Security was established in late 2002, combining dozens of previously separated government agencies. Building upon earlier work on visual data mining by Daniel A. Keim starting in the late 1990s, this simultaneously lead to the development of a research agenda for visual analytics. As part of these efforts the National Visualization and Analytics Center (NVAC) at Pacific Northwest National Laboratory was established in 2004, whose charter was to develop system to mitigate information overload after the September 11, 2001 attacks in the intelligence community. Their research work determined core challenges, posed open research questions, and positioned visual analytics as a new research domain, in particular through the 2005 research agenda Illuminating the Path. In 2006, the IEEE VIS community led by Pak Chung Wong and Daniel A. Keim launched the annual IEEE Conference on Visual Analytics Science and Technology (VAST), providing a dedicated venue for research into visual analytics, which in 2020 merged to form the IEEE Visualization conference. In 2008, scope and challenges of visual analytics were conceptually defined by Daniel A. Keim and Jim Thomas in their influential book about visual data mining. The domain was further refined as part of the European Commissions FP7 VisMaster program in the late 2000s. == Topics == === Scope === Visual analytics is a multidisciplinary field that includes the following focus areas: Analytical reasoning techniques that enable users to obtain deep insights that directly support assessment, planning, and decision making Data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis Techniques to support production, presentation, and dissemination of the results of an analysis to communicate information in the appropriate context to a variety of audiences. Visual representations and interaction techniques that take advantage of the human eye's broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. === Analytical reasoning techniques === Analytical reasoning techniques are the method by which users obtain deep insights that directly support situation assessment, planning, and decision making. Visual analytics must facilitate high-quality human judgment with a limited investment of the analysts’ time. Visual analytics tools must enable diverse analytical tasks such as: Understanding past and present situations quickly, as well as the trends and events that have produced current conditions Identifying possible alternative futures and their warning signs Monitoring current events for emergence of warning signs as well as unexpected events Determining indicators of the intent of an action or an individual Supporting the decision maker in times of crisis. These tasks will be conducted through a combination of individual and collaborative analysis, often under extreme time pressure. Visual analytics must enable hypothesis-based and scenario-based analytical techniques, providing support for the analyst to reason based on the available evidence. === Data representations === Data representations are structured forms suitable for computer-based transformations. These structures must exist in the original data or be derivable from the data themselves. They must retain the information and knowledge content and the related context within the original data to the greatest degree possible. The structures of underlying data representations are generally neither accessible nor intuitive to the user of the visual analytics tool. They are frequently more complex in nature than the original data and are not necessarily smaller in size than the original data. The structures of the data representations may contain hundreds or thousands of dimensions and be unintelligible to a person, but they must be transformable into lower-dimensional representations for visualization and analysis. === Theories of visualization === Theories of visualization include: Jacques Bertin's Semiology of Graphics (1967) Nelson Goodman's Languages of Art (1977) Jock D. Mackinlay's Automated design of optimal visualization (APT) (1986) Leland Wilkinson's Grammar of Graphics (1998) Hadley Wickham's Layered Grammar of Graphics (2010) === Visual representations === Visual representations translate data into a visible form that highlights important features, including commonalities and anomalies. These visual representations make it easy for users to perceive salient aspects of their data quickly. Augmenting the cognitive reasoning process with perceptual reasoning through visual representations permits the analytical reasoning process to become faster and more focused. == Process == The input for the data sets used in the visual analytics process are heterogeneous data sources (i.e., the internet, newspapers, books, scientific experiments, expert systems). From these rich sources, the data sets S = S1, ..., Sm are chosen, whereas each Si , i ∈ (1, ..., m) consists of attrib
Distributed concurrency control
Distributed concurrency control is the concurrency control of a system distributed over a computer network (Bernstein et al. 1987, Weikum and Vossen 2001). In database systems and transaction processing (transaction management) distributed concurrency control refers primarily to the concurrency control of a distributed database. It also refers to the concurrency control in a multidatabase (and other multi-transactional object) environment (e.g., federated database, grid computing, and cloud computing environments. A major goal for distributed concurrency control is distributed serializability (or global serializability for multidatabase systems). Distributed concurrency control poses special challenges beyond centralized one, primarily due to communication and computer latency. It often requires special techniques, like distributed lock manager over fast computer networks with low latency, like switched fabric (e.g., InfiniBand). The most common distributed concurrency control technique is strong strict two-phase locking (SS2PL, also named rigorousness), which is also a common centralized concurrency control technique. SS2PL provides both the serializability and strictness. Strictness, a special case of recoverability, is utilized for effective recovery from failure. For large-scale distribution and complex transactions, distributed locking's typical heavy performance penalty (due to delays, latency) can be saved by using the atomic commitment protocol, which is needed in a distributed database for (distributed) transactions' atomicity.
AIOps
AIOps (Artificial Intelligence for IT Operations) refers to the use of artificial intelligence, machine learning, and big data analytics to automate and enhance data center management. It helps organizations manage complex IT environments by detecting, diagnosing, and resolving issues more efficiently than traditional methods. == History == AIOps was first defined by Gartner in 2016, combining "artificial intelligence" and "IT operations" to describe the application of AI and machine learning to enhance IT operations. This concept was introduced to address the increasing complexity and data volume in IT environments, aiming to automate processes such as event correlation, anomaly detection, and causality determination. == Definition == AIOps refers to multi-layered, complex technology platforms that enhance and automate IT operations by using machine learning and analytics to analyze the large amounts of data collected from various DevOps devices and tools, automatically identifying and responding to issues in real-time. AIOps represents a shift from isolated IT data to aggregated observational data (e.g., job logs and monitoring systems) and interaction data (such as ticketing, events, or incident records) within a big data platform. AIOps applies machine learning and analytics to this data, resulting in continuous visibility that, when combined with automation, can lead to ongoing improvements. AIOps connects three IT disciplines (automation, service management, and performance management) to achieve continuous visibility and improvement. This new approach in modern, accelerated, and hyper-scaled IT environments leverages advances in machine learning and big data to overcome previous limitations. == Components == AIOps includes, but is not limited to, the following processes and techniques: Anomaly Detection Log Analysis Root Cause Analysis Cohort Analysis Event Correlation Predictive Analytics Hardware Failure Prediction Automated Remediation Performance Prediction Incident Management Causality Determination Queue Management Resource Scheduling and Optimization Predictive Capacity Management Resource Allocation Service Quality Monitoring Deployment and Integration Testing System Configuration Auto-diagnosis and Problem Localization Efficient ML Training and Inferencing Using LLMs for Cloud Ops Auto Service Healing Data Center Management Customer Support Security and Privacy in Cloud Operations == Comparison with DevOps == AIOps is increasingly compared with DevOps in terms of impact on operational efficiency. While DevOps focuses on collaboration between development and operations teams to accelerate software delivery, AIOps integrates artificial intelligence to enhance monitoring, automation, and predictive capabilities. Various industry analyses have explored the similarities and differences between the two approaches, including discussions on how organizations can combine them to improve incident management and resource optimization. == Results == AI optimizes IT operations in five ways: First, intelligent monitoring powered by AI helps identify potential issues before they cause outages, improving metrics like Mean Time to Detect (MTTD) by 15-20%. Second, performance data analysis and insights enable quick decision-making by ingesting and analyzing large data sets in real time. Third, AI-driven automated infrastructure optimization efficiently allocates resources and thereby reducing cloud costs. Fourth, enhanced IT service management reduces critical incidents by over 50% through AI-driven end-to-end service management. Lastly, intelligent task automation accelerates problem resolution and automates remedial actions with minimal human intervention. In 2025, Atera Networks was identified as a leader in AIOps by the software review platform G2. == AIOps vs. MLOps == AIOps tools use big data analytics, machine learning algorithms, and predictive analytics to detect anomalies, correlate events, and provide proactive insights. This automation reduces the burden on IT teams, allowing them to focus on strategic tasks rather than routine operational issues. AIOps is widely used by IT operations teams, DevOps, network administrators, and IT service management (ITSM) teams to enhance visibility and enable quicker incident resolution in hybrid cloud environments, data centers, and other IT infrastructures. In contrast to MLOps (Machine Learning Operations), which focuses on the lifecycle management and operational aspects of machine learning models, AIOps focuses on optimizing IT operations using a variety of analytics and AI-driven techniques. While both disciplines rely on AI and data-driven methods, AIOps primarily targets IT operations, whereas MLOps is concerned with the deployment, monitoring, and maintenance of ML models. == Conferences == There are several conferences that are specific to AIOps: AIOps Summit AI Dev Summit IBM Think conference
Qapital
Qapital is a personal finance mobile application (app) for the iOS and Android operating systems, developed by Qapital, LLC. The app is designed to motivate users to save money through a gamification of their spending behavior. It moves money from a user's checking account to a separate Qapital account, when certain rules are triggered. Its database is used by psychology professor Dan Ariely to study consumer behavior. Qapital was released in Sweden in 2013, then in the US in early 2015. The application was later withdrawn from the Swedish market in April 2015, in order to focus on the US market. == History == The idea for Qapital was conceived by ex-bankers in Sweden. The software was designed by twin brothers Daniel and Andreas Källbom of Studio Källbom and released in Sweden in December 2013. The original software was a personal finance dashboard, similar to Mint.com, to show its users how they spent their money. Qapital introduced the app into the US market with a different design in 2014 and started focusing exclusively on the US market. The app was re-designed to focus on building savings rather than managing personal finances. The Swedish version shut down in April 2015. The app was initially restricted to the iOS platform, but an Android version was released at the end of 2015. Shortly after its US launch, Qapital invited psychology professor Dan Ariely to join its team as its "chief behavioral economist". He uses the app's database to conduct research into behavioral economics and Qapital in turn uses Ariely's research in design and programming decisions. In 2017, Qapital added checking and debit card services to the app. == Concept and features == Qapital is a free personal finance app for iOS and Android devices, intended to encourage its users to save money. Qapital directs each of its users to set savings goals, then automatically transfers money from their checking account to an account for savings, when a rule established in the app is met. It uses the "if this then that" (IFTTT) rule-based web-service. For example, one rule could be that if a user purchases a cup of coffee, then the app will round up the charge to the nearest dollar and deposit the difference into savings. Users connect their bank accounts to Qapital, so it knows when purchases are made. When a rule is met, money for savings are transferred to a Qapital account operated in partnership with Lincoln Savings Bank. As of 2015, Qapital can connect to more than 180 other apps, such as Facebook, Twitter, Dropbox and Instagram. For example, connecting to Jawbone allows the user to set a rule that if they take a certain number of steps during the day, a set amount of money is transferred to savings. The app also allows users to monitor activity among their other financial accounts, such as deposits and withdrawals. == Reception == In an October 2015 review, PC Magazine gave Qapital four out of five marks and an editor rating of "excellent." The review praised the app for having a "lovely design" and criticized it for being a, "bit simplistic in some of its rules." Bankrate, in a May 2015 review, gave the app a score of 3/5 for "ease of use," 5/5 for "features," 4/5 for "effectiveness," 4/5 for "value," for a total score of 16/20. The reviewer criticized Qapital's savings account for providing a low-interest rate, but concluded that its numerous features make the app "intriguing" and "it would be difficult to find a standard bank app more fun to use than Qapital."