In the United States, age verification laws for social media are ostensibly designed to limit young people's access to content deemed problematic such as pornography and to reduce the negative impact of social media on the mental health and well-being of children and adolescents. The purpose and effects of such laws are highly contested. Critics say that these laws suppress free speech by removing online anonymity. They have also stated the laws undermine safety, even for children, by increasing the exposure of user data to breaches, many sites require government IDs and biometric data (such as photographs), often transmitted or secured insecurely and without encryption. They also note that the measures are easily circumvented with VPNs, prompting some states such as Michigan and Wisconsin to propose legislation banning VPNs. == Laws == Many state legislatures have considered or enacted legislation pertaining to young people and social media. In 2022, California passed the California Age-Appropriate Design Code Act (AB 2273) requiring websites that are likely to be used by minors to estimate visitors' ages. On March 23, 2023, Utah Governor Spencer Cox signed SB 152 and HB 311, collectively known as the Utah Social Media Regulation Act, which requires age verification; if a user is under 18, they have to get parental consent before making an account on any social media platform. Few laws have gone into effect partially due to court challenges. === Arkansas === On April 11, 2023, Arkansas enacted SB 396, the Social Media Safety Act. The law requires certain social media companies that make over $100 million per year to verify the age of new users using a third party, and to obtain parental consent for users under 18. It excludes social media companies that allow a user to generate short video clips as well as games. The law was set to go in effect in September 2023. On June 29, 2023, NetChoice sued the Attorney General of Arkansas Tim Griffin in The Western District Court of Arkansas to block enforcement of the law, supported by the American Civil Liberties Union and the Electronic Frontier Foundation (EFF). On July 7, 2023, NetChoice filed a motion for a preliminary injunction to block enforcement of the law. On July 27, Griffin and Tony Allen filed briefs in opposition to the preliminary injunction. The preliminary injunction was granted by Judge Timothy L. Brooks on August 31, reasoning that the law was too vague, that NetChoice's members will suffer irreparable harm if the act goes into effect, and that age restrictions were ineffective. === California === ==== Digital Age Assurance Act (AB 1043) ==== On October 13, 2025, Gavin Newsom signed the Digital Age Assurance Act into law, which requires operating system providers to estimate the age of a user and into 4 age categories: Under 13 13 - 15 16 - 17 18 and over It comes into force on January 1, 2027. ==== California Age-Appropriate Design Code (AB 2273) ==== On September 15, 2022, California enacted AB 2273, the California Age-Appropriate Design Code Act. Its most controversial provisions required online services that are likely to be used by those under 18 to estimate the age of child users with a "reasonable level of certainty". It also required these services to file Data Protection Impact Assessments (DPIAs) certifying whether an online product, service, or feature could harm children, including by exposing them to (potentially) harmful content. The law does not define harmful content. Before the law took effect, EFF sent a veto request to Newsom. On December 14, 2022, NetChoice sued. On September 18, 2023, Federal Judge Beth Labson Freeman granted a preliminary injunction. The 9th Circuit on August 16, 2024, affirmed the injunction against the DPIA section of the law and sent the rest back, because the argument in the 9th circuit was mainly focused on the DPIA. ==== Protecting Our Kids from Social Media Addiction Act (SB 976) ==== On September 20, 2024, California enacted SB 976, Protecting Our Kids from Social Media Addiction. The law requires online platforms to exclude those under 18 from "addictive" feeds unless parental consent is given. It requires online platforms to not send notifications to someone under 18 between 12:00 AM and 6:00 AM without parental consent or between 8:00 am – 3:00 pm without parental consent from September through May (the law does not define what a "notification" is). The law took effect on January 1, 2025, with age verification required as of December 31, 2026. On November 12, NetChoice sued in the Northern District and before Judge Edward John Davila. On December 31, the judge blocked the sections of SB 976 that required time-of-day restrictions. He also enjoined requirements to report on the number of minor users as well as the number of parental assents to access an addictive feed. He did not block the age assurance requirement or blocking minors from seeing addictive feeds without parental consent. His reasoning was that age assurance that runs in the background does not restrict adult access to speech and that regulating feeds does not violate the first amendment because it was content neutral and did not remove any content. On January 1, 2025, NetChoice filed a motion to fully block the law as part of its appeal to the Ninth Circuit. NetChoice claimed that the court erred in its reading of Supreme Court case Moody v. NetChoice by mainly focusing on the concurring opinions and not the deciding opinion. The same day Davila decreed that California's response to NetChoice was due by 11:59 pm. California responded the same day to NetChoice's motion, claiming that the court should not block the full law, claiming that NetChoice had misread Moody v. NetChoice and that NetChoice's members would not likely face any harm from the act because members such as X (formerly Twitter) already offer their members feeds that were not personalized. On January 2, Davila granted NetChoice's motion to block the full law during the appeals process by delaying the effective date of the law from January 1, 2025, to February 1, 2025. That day NetChoice appealed the case to the Ninth Circuit Court of Appeals. === Florida === On January 5, 2024, Tyler Sirois introduced HB 1, which would ban anyone under 16 from using any social media platform and would require platforms to verify the age of users. After the bill passed, the American Civil Liberties Union (ACLU) published a blog post opposing the bill for violating the rights of minors and adults. The bill was vetoed by Governor Ron DeSantis on March 1, 2024, claiming that the State Legislature was going to enact a better alternative. HB 3 then decreased the minimum age from 16 to 14, allowing minors aged 14 and 15 to make social media accounts with parental consent. Florida enacted it on March 25, 2024, and took effect on January 1, 2025. A surge of 1,150% in VPN demand in Florida was detected after the law took effect. VPN services provide the ability to circumvent the law. On October 28, 2024, NetChoice and Computer and Communications Industry Association sued. The Judge is Chief Judge Mark E. Walker. On February 28, 2025, arguments were heard on the motion for a preliminary injunction. Walker seemed skeptical of Florida's argument that the law did not violate the first amendment and said the State would have a hard time to justify a complete ban of youth under 14 from social media. On March 13, Walker denied the motion for a preliminary injunction because the plaintiffs had not proven that at least one of their members had at least 10 percent of their users under 16 use their platform for at least 2 hours per day. Plaintiffs filed an amended complaint and a renewed motion for a preliminary injunction which was granted on June 3, for failing First Amendment Intermediate scrutiny. The injunction left in force the provision that allowed parents to request termination of their child's social media account. === Georgia === On April 23, 2024, Georgia enacted SB 351, which became Act 463. Act 463 requires platforms to verify the age of users of social media platforms and require users under 16 years of age to have parental consent before creating an account. It also requires schools to ban all social media platforms, including YouTube. Before the law was signed NetChoice sent a veto request to Kemp claiming the law was unconstitutional and was bad policy. After the bill was enacted, ACLU and NetChoice criticized the bill. NetChoice sued two months before the law's effective date. The Judge is Amy Totenberg. the suit claims that the law violates the First Amendment and Fourteenth Amendments. === Louisiana === ==== Secure Online Child Interaction and Age Limitation Act (SB 162) ==== On June 28, 2023, Louisiana enacted SB 162, the Secure Online Child Interaction and Age Limitation Act. It requires social media platforms to verify user age and get parental consent for users under 16, prohibits account holders under 1
TipTop Technologies
TipTop Technologies is a real-time web and social search engine with a platform for semantic analysis of natural language. Tip-Top Search provides results capturing individual and group sentiment, opinions, and experiences there from the content of various sorts such as real-time messages from Twitter or consumer product reviews on Amazon.com. TipTop Technologies and ITC Infotech collaborated to create a search interface suitable for both enterprise and consumer applications. Tip-Top's products are part of the "emerging Web 3.0 applications which use semantic technologies to augment the underlying Web system's functionalities." Their main product is 360, an AI tool that incorporates multiple AI applications under one wing. Jonathan AlBright professor at Elon University, found videos generated by TipTop Technologies software on YouTube in his research into artificial intelligence, described it as AI-generated "fake news". Through semantic analysis of large data sets, TipTop gleaned behavioral insights from Tweets around events like Halloween, Thanksgiving, Holiday Gifting, the Super Bowl, and the Oscar Nominees for the Academy Awards coverage. Sentiment analysis, concept trend tracking, and real-time market research are other applications included in the TipTop Search product. TipTop's insight engine solves the problem of real-time data noise, and its ability to "sort the 'good tweets' from the 'bad tweets' when it comes to a product, service, or a region..." In addition, products like TipTop Shopping with customizable search widgets bring together consumer reviews, social search, and sentiment analysis enabling product comparisons across attributes like the overall value and aiding purchasing decisions through user-driven product tips and pits. TipTop Finance adds another complexity to real-time search results by incorporating corporate sentiment, company stock tickers, and social media into TipTop's existing social search platform. Additional success applying semantic technologies has been with polling, "if you compare these Gallup results with TipTop, a sentiment engine based on Twitter, the results are not way off. It does surprise you but it tells me that sentiment analysis in case of public opinion about a burning social issue or a famous personality is relatively easier." With the increasing amount of unstructured, opinion-oriented, and user-generated content available on the Web, TipTop's technology aims to make sense of all this data, and deliver it in a useful way for consumer and enterprise users alike. TipTop Technologies is a privately held company with its headquarters in the San Francisco Bay Area, and team members are located globally.
Distribution management system
A distribution management system (DMS) is a collection of applications designed to monitor and control the electric power distribution networks efficiently and reliably. It acts as a decision support system to assist the control room and field operating personnel with the monitoring and control of the electric distribution system. Improving the reliability and quality of service in terms of reducing power outages, minimizing outage time, maintaining acceptable frequency and voltage levels are the key deliverables of a DMS. Given the complexity of distribution grids, such systems may involve communication and coordination across multiple components. For example, the control of active loads may require a complex chain of communication through different components as described in US patent 11747849B2 In recent years, utilization of electrical energy increased exponentially and customer requirement and quality definitions of power were changed enormously. As electric energy became an essential part of daily life, its optimal usage and reliability became important. Real-time network view and dynamic decisions have become instrumental for optimizing resources and managing demands, leading to the need for distribution management systems in large-scale electrical networks. == Overview == Most distribution utilities have been comprehensively using IT solutions through their Outage Management System (OMS) that makes use of other systems like Customer Information System (CIS), Geographical Information System (GIS) and Interactive Voice Response System (IVRS). An outage management system has a network component/connectivity model of the distribution system. By combining the locations of outage calls from customers with knowledge of the locations of the protection devices (such as circuit breakers) on the network, a rule engine is used to predict the locations of outages. Based on this, restoration activities are charted out and the crew is dispatched for the same. In parallel with this, distribution utilities began to roll out Supervisory Control and Data Acquisition (SCADA) systems, initially only at their higher voltage substations. Over time, use of SCADA has progressively extended downwards to sites at lower voltage levels. DMSs access real-time data and provide all information on a single console at the control centre in an integrated manner. Their development varied across different geographic territories. In the US, for example, DMSs typically grew by taking Outage Management Systems to the next level, automating the complete sequences and providing an end to end, integrated view of the entire distribution spectrum. In the UK, by contrast, the much denser and more meshed network topologies, combined with stronger Health & Safety regulation, had led to early centralisation of high-voltage switching operations, initially using paper records and schematic diagrams printed onto large wallboards which were 'dressed' with magnetic symbols to show the current running states. There, DMSs grew initially from SCADA systems as these were expanded to allow these centralised control and safety management procedures to be managed electronically. These DMSs required even more detailed component/connectivity models and schematics than those needed by early OMSs as every possible isolation and earthing point on the networks had to be included. In territories such as the UK, therefore, the network component/connectivity models were usually developed in the DMS first, whereas in the USA these were generally built in the GIS. The typical data flow in a DMS has the SCADA system, the Information Storage & Retrieval (ISR) system, Communication (COM) Servers, Front-End Processors (FEPs) & Field Remote Terminal Units (FRTUs). == Why DMS? == Reduce the duration of outages Improve the speed and accuracy of outage predictions. Reduce crew patrol and drive times through improved outage locating. Improve the operational efficiency Determine the crew resources necessary to achieve restoration objectives. Effectively utilize resources between operating regions. Determine when best to schedule mutual aid crews. Increased customer satisfaction A DMS incorporates IVR and other mobile technologies, through which there is an improved outage communications for customer calls. Provide customers with more accurate estimated restoration times. Improve service reliability by tracking all customers affected by an outage, determining electrical configurations of every device on every feeder, and compiling details about each restoration process. == DMS Functions == In order to support proper decision making and O&M activities, DMS solutions should support the following functions: Network visualization & support tools Applications for Analytical & Remedial Action Utility Planning Tools System Protection Schemes The various sub functions of the same, carried out by the DMS are listed below:- === Network Connectivity Analysis (NCA) === Distribution network usually covers over a large area and catering power to different customers at different voltage levels. So locating required sources and loads on a larger GIS/Operator interface is often very difficult. Panning & zooming provided with normal SCADA system GUI does not cover the exact operational requirement. Network connectivity analysis is an operator specific functionality which helps the operator to identify or locate the preferred network or component very easily. NCA does the required analyses and provides display of the feed point of various network loads. Based on the status of all the switching devices such as circuit breaker (CB), Ring Main Unit (RMU) and/or isolators that affect the topology of the network modeled, the prevailing network topology is determined. The NCA further assists the operator to know operating state of the distribution network indicating radial mode, loops and parallels in the network. === Switching Schedule & Safety Management === In territories such as the UK a core function of a DMS has always been to support safe switching and work on the networks. Control engineers prepare switching schedules to isolate and make safe a section of network before work is carried out, and the DMS validates these schedules using its network model. Switching schedules can combine telecontrolled and manual (on-site) switching operations. When the required section has been made safe, the DMS allows a Permit To Work (PTW) document to be issued. After its cancellation when the work has been finished, the switching schedule then facilitates restoration of the normal running arrangements. Switching components can also be tagged to reflect any Operational Restrictions that are in force. The network component/connectivity model, and associated diagrams, must always be kept absolutely up to date. The switching schedule facility therefore also allows 'patches' to the network model to be applied to the live version at the appropriate stage(s) of the jobs. The term 'patch' is derived from the method previously used to maintain the wallboard diagrams. === State Estimation (SE) === The state estimator is an integral part of the overall monitoring and control systems for transmission networks. It is mainly aimed at providing a reliable estimate of the system voltages. This information from the state estimator flows to control centers and database servers across the network. The variables of interest are indicative of parameters like margins to operating limits, health of equipment and required operator action. State estimators allow the calculation of these variables of interest with high confidence despite the facts that the measurements may be corrupted by noise, or could be missing or inaccurate. Even though we may not be able to directly observe the state, it can be inferred from a scan of measurements which are assumed to be synchronized. The algorithms need to allow for the fact that presence of noise might skew the measurements. In a typical power system, the State is quasi-static. The time constants are sufficiently fast so that system dynamics decay away quickly (with respect to measurement frequency). The system appears to be progressing through a sequence of static states that are driven by various parameters like changes in load profile. The inputs of the state estimator can be given to various applications like Load Flow Analysis, Contingency Analysis, and other applications. === Load Flow Applications (LFA) === Load flow study is an important tool involving numerical analysis applied to a power system. The load flow study usually uses simplified notations like a single-line diagram and focuses on various forms of AC power rather than voltage and current. It analyzes the power systems in normal steady-state operation. The goal of a power flow study is to obtain complete voltage angle and magnitude information for each bus in a power system for specified load and generator real power and voltage conditions. Once this
ISSCO Graphics
Integrated Software Systems Corporation (ISSCO), doing business as ISSCO Graphics, was an American software developer and publisher based in San Diego, California, and active from 1970 to 1986. They were best known for their enterprise graphics software packages, including Tellagraf, CueChart and Disspla. == History == ISSCO Graphics had considered acquiring Breakthrough Software, whose software focus involved PC DOS, as a means of getting into the PC arena, but backed off when Computer Associates made an offer to acquire ISSCO. By early 1987 it was reported that "Issco users breathe sigh of relief" that all was well. The ISSCO User's Group was founded in 1976. ISSCO, which was founded in 1970 by Peter Preuss, was acquired by Computer Associates in 1986. == Notable products == === Tellagraf === ISSCO's Tellagraf is an early software package designed to allow end-users to "turn out full color, professional quality charts" with initial results displayed on a screen, modified as needed, and then "a final 'hard-copy' can be made .. or made into 35mm color transparencies for projection onto a screen." Users of Tellagraf often had access to CueChart and Disspla software. Often computer sites having one had all three. Terminals with varying degrees of graphics, such as the DEC's VT100 and Tektronix's Tektronix 4xxx family of text and graphics terminals. were supported, and the software ran on popular computing platforms. Four years are important to Tellagraf's early history: 1978: ease of use 1980: graphic-artist quality 1982: introduction of CueChart, and recognition by IEEE. 1983: "quality graphics enters the mainstream of data processing with ..." Tellegraf was eventually acquired by Computer Associates and renamed CA-Tellegraf. SAS users found it helpful. Universities, research institutes and financial services firms were among early users. === Disspla === Disspla is a package of data plotting subroutines that can be used from high level languages. It was also acquired by Computer Associates. === Tellaplan === In 1983 ISSCO introduced Tellaplan, "a project planning, report and schedule charting system for Tell-A- Graf users in IBM MVS or CMS or Digital Equipment Corp. VAX computers" atop which they built "two visual project management software packages" three years later.
YrWall
YrWall is a Digital Graffiti Wall developed by event company Luma, where designs are created on a large wall using a modified spray paint can. The can contains no paint, instead it has an IR light which is tracked by a computer vision system and the image immediately back-projected onto the wall. The inbuilt YrWall software has much of the functionality of a typical computer paint program, with a pop-out interface which enables users to change colour, spray width, opacity, work with stencils and use animated items such as swirls, stars, drips and splats. Recent additions to YrWall include options to email a JPEG of the completed design and create personalised stickers and T-shirts. == Dragons' Den == The inventor of YrWall, Tom Hogan, and his business partner, Tim Williams, appeared on Episode 4 of Series 8 of the BBC show Dragons' Den. Seeking investment in YrWall, the entrepreneurs were successful in gaining £50,000 for 40% of the YrWall parent company Lumacoustics from Dragons Deborah Meaden and Peter Jones. == World's Largest Interactive Graffiti Wall == In September 2009 YrWall was used to create the 'World's Largest Interactive Graffiti Wall' at the Bristol Festival, UK. Artists used the standard 3.5 m2 YrWall to produce artwork which was in turn projected live onto a 26m x 10m space on the side of the iconic Lloyds amphitheatre building.
Load file
A load file in the litigation community is commonly referred to as the file used to import data (coded, captured or extracted data from ESI processing) into a database; or the file used to link images. These load files carry commands, commanding the software to carry out certain functions with the data found in them. Load files are usually ASCII text files that have delimited fields of information. Such load files may have data about documents to be imported into a document management software such as Concordance or Summation. Or they may have the path or directory where images may reside so that the software can link such images to their corresponding records. Some database programs take one load file for importing images and another for importing data while others take only one load file for both pieces of information. OCR or Search-able Text which is considered "data" is also imported into most database programs via the same load files. Though some people prefer to load the OCR into their databases by running a separate command to search and find the desired text. Commonly used databases and their corresponding file extensions are: Summation (DII , CSV), Concordance (OPT, DAT), Sanction (SDT), IPRO (LFP), Ringtail (MDB) and DB/TextWorks (TXT).
Apps to analyse COVID-19 sounds
Apps to analyse COVID-19 sounds are mobile software applications designed to collect respiratory sounds and aid diagnosis in response to the COVID-19 pandemic. Numerous applications are in development, with different institutions and companies taking various approaches to privacy and data collection. Current efforts are aimed at gathering data. In a later stage, it is possible that sound apps will have the capacity (and ethical approvals) to provide information back to users. In order to develop and train signal analysis approaches, large datasets are required. == History == The COVID-19 outbreak was announced as a global pandemic by the World Health Organization in March 2020 and has affected a growing number of people globally. In this context, advanced artificial intelligence techniques are being considered as tools in aiding our response to global health crisis. Other COVID-19 apps which offer solutions for user tracking have been developed. At the same time a number of approaches which tries to use respiratory sounds and artificial intelligence to understand if the disease can be diagnosed have been proposed. A few studies are available as preprints (i.e. not yet peer-reviewed) documents. == Methodologies == The potential for using speech and sound analysis by artificial intelligence to help in this scenario, by surveying which types of related or contextually significant phenomena can be automatically assessed from speech or sound has been recently overviewed. These include the automatic recognition and monitoring of breathing, dry and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain. Additionally, the potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients has also been presented. In particular, by analysing speech recordings from these patients, an audio-only-based model to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety, is constructed. This work shows promise in estimating the severity of illness. Machine learning methods have been explored to recognize and diagnose coughs from different diseases. These included a low complexity, automated recognition and diagnostic tool for screening respiratory infections that utilizes convolutional neural networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses (i.e. bronchitis, bronchiolitis and pertussis) based on their unique cough audio features. A large-scale crowdsourced dataset of respiratory sounds has been collected to aid diagnosis of COVID-19: coughs and breathing sounds are sufficient to distinguish users affected by COVID-19 versus those affected by asthma or healthy controls. Behind these studies is the ambition that automated systems to screen for respiratory diseases based on voice, raw cough or other sound data would have positive medical applications in both clinical and public health arenas. == List of apps to analyse COVID-19 sounds ==