The Packard Bell Statesman was an economy line of notebook-sized laptops introduced in 1993 by Packard Bell. They were slower in performance and lacked features compared to most competitor products, but they were lower in price. It was created in a collaboration between Packard Bell and Zenith Data Systems. The Statesman series was essentially a rebrand of Zenith Data Systems Z-Star 433 series, with the only notable difference of the logo in the middle and text on the front bezel. == History == In June 1993 Zenith Data Systems announced an alliance with Packard Bell. Zenith acquired about 20% of Packard Bell and they would both now work together to design and build PC's. Zenith would also provide Packard Bell with private-label versions of their portable PC's. The Packard Bell Statesman was a rebrand of the Zenith Z-Star notebook computer series. While the Statesman was being advertised by Packard Bell, the Z-Star series was also still being sold by Zenith. The Statesman was first introduced on October 4, 1993. Prices started at $1,500 for a monochrome or color DSTN model with a 33 MHz Cyrix Cx486SLC, 4 MB of RAM, 200 MB hard disk drive, internal 1.44 MB floppy disk drive, and MS-DOS 6.0 with Windows 3.1 for the included software. A "J mouse" pointing device was included, similar to the TrackPoint. The Statesman was expected to begin shipping within the next few weeks. == Specifications == === Hardware === CPU The first two models, the 200M and 200C, used the Cyrix Cx486SLC. This was Cyrix's first processor, which was a 386SX pin-compatible chip with on-board L1 cache and 486 instructions, being known as a "hybrid chip". The processor was clocked at 33 MHz and had 1 KB of L1 cache. It was a 16-bit processor and was pin compatible with the Intel 80386SX. On the bottom of the unit, the motherboard had an empty socket for a Cyrix FasMath co-processor, which could improve floating-point math performance. The 200M and 200C plus models had a Cyrix Cx486SLC2 clocked at 50 MHz, which was 50% faster than the original 486SLC. The SLC2 similarly had 1 KB of on-board cache and was pin compatible with the previous model. Graphics & Display For video all models used three versions of the Chips & Technologies 655xx, the CT65520, 65525, and 65530. The 65520 was first introduced in early 1992 as the first controller with Super VGA resolution. It supported resolutions up to 1024x768 in 16 colors or shades of gray. If in 800x600 resolution, it can display up to 256 colors. All 3 chips were the same, with the CT65525 identifying as a CT65530. The CT65530 had an ability of 5V and 3.3V mixed operation and linear video memory addressing. All models used a 9.5in 800x600 resolution DSTN LCD. The 200M and 200M Plus had a monochrome display, while the 200C and 200C Plus had a color display. Audio All models had only basic audio available, with just a piezo speaker soldered onto the motherboard and no sound controller. Memory Standard RAM included was 4-8 MB of EDO RAM. The RAM was on a proprietary SIPP package that could only be upgraded to 12 MB maximum if the user had compatible modules. Storage For storage all models used a hard drive with a size of 100 or 200 MB, and all models had an internal 1.44 MB floppy disk drive located on the side of the unit. The maximum capacity hard drive compatible if the user wanted to upgrade was 500 MB.Ports & Expansion For ports all models had 1x serial, 1x parallel, 1x VGA output, and 1x PS/2 keyboard/mouse input. For expansion all models only had one PCMCIA type II slot. Keyboard & Mouse All models used a small-scale keyboard with control keys. One interesting feature of the keyboard is that the J key also acted as a mouse, working similar to IBM's ThinkPad TrackPoint. On some models additional keys such as S, D, F, G and space let you do other mouse actions such as right click, left click, double click, and middle mouse click. === Software === The series shipped with MS-DOS and Windows 3.1 as the included operating system. == Model Comparison == Statesman 200M — The first Statesman model, it came with a DSTN monochrome screen, and a Nickel-cadmium battery pack which could last up to 4 hours. It weighed 7.4 lb and was $1500. Statesman 200C — The second Statesman model, it was the same as the 200M with the only notable differences of a DSTN color display rather than monochrome and a slightly decreased battery life of about 3 hours. It cost $700 more than the 200M at $2200. Statesman 200M/200C Plus — The 200M/200C Plus were both identical to their previous base models, with the only difference of them having a Cx486SLC2 running at 50 MHz. In 1994 it cost around $2,295 for the 200C plus with 4 MB of ram, with 8 MB costing an extra $400. == Reception == The Statesman received fair reception, with most reviewers giving positivity for the low price and high battery life, but mainly criticizing the performance and screen quality of the model line. A review by PC World writer Rex Farrance and Owen Linderholm said the 200M had a good price, being only $1500, and a good battery life which lasted about 4 hours. In benchmarks however, the 200M performed "noticeably below the average". It was noted that the 200M's worst feature was its monochrome display, being "cloudy and a bit dim for our tastes". The J mouse was considered a decent choice, and was said to be "highly usable" after some practice. The 200M was listed as number 3 on PC World's top 20 budget PC list. PC World also reviewed the 200C, saying the color display is only a "marginal, although an improvement on the monochrome version". The 200C placed 9 on the PC World top 20 budget PC list. Compute! Magazine reviewed the 200C Plus in September 1994 stating it "lagged far behind the others, especially the DXs, but then speed isn't everything". It was given pros for low cost and good display, but criticized for its low performance, not having a trackball, and poor external monitor support.
Aarogya Setu
Aarogya Setu (lit. 'The bridge to health') is an Indian COVID-19 "contact tracing, syndromic mapping and self-assessment" digital service, primarily a mobile app, developed by the National Informatics Centre under the Ministry of Electronics and Information Technology (MeitY). The app reached more than 100 million installs in 40 days. On 26 May, amid growing privacy and security concerns, the source code of the app was made public. == Full view == The stated purpose of this app is to spread awareness of COVID-19 and to connect essential COVID-19-related health services to the people of India. This app augments the initiatives of the Department of Health to contain COVID-19 and shares best practices and advisories. It is a tracking app which uses the smartphone's GPS and Bluetooth features to track COVID-19 cases. The app is available for Android and iOS mobile operating systems. With Bluetooth, it tries to determine the risk if one has been near (within six feet of) a COVID-19-infected person, by scanning through a database of known cases across India. Using location information, it determines whether the location one is in belongs to one of the infected areas based on the data available. This app is an updated version of an earlier app called Corona Kavach (now discontinued) which was released earlier by the Government of India. == Features and tools == Aarogya Setu has four sections: User Status (tells the risk of getting COVID-19 for the user) Self Assess (helps the users identify COVID-19 symptoms and their risk profile) COVID-19 Updates (gives updates on local and national COVID-19 cases) E-pass integration (if applied for E-pass, it will be available) See Recent Contacts option (allows the users to assess the risk level of their Bluetooth contacts) It tells how many COVID-19 positive cases are likely in a radius of 500 m, 1 km, 2 km, 5 km and 10 km from the user. The app is built on a platform that can provide an application programming interface (API) so that other computer programs, mobile applications, and web services can make use of the features and data available in Aarogya Setu. == Response == Aarogya Setu crossed five million downloads within three days of its launch, making it one of the most popular government apps in India. It became the world's fastest-growing mobile app, beating Pokémon Go, with more than 50 million installs 13 days after launching in India on 2 April 2020. It reached 100 million installs by 13 May 2020, that is in 40 days since its launch. In an order on 29 April 2020 the central government made it mandatory for all employees to download the app and use it – "Before starting for office, they must review their status on Aarogya Setu and commute only when the app shows safe or low risk". The Union Home Ministry also said that the application is mandatory for all living in the COVID-19 containment zone. The government gave the announcement along with the nationwide lockdown extension by two weeks from the 4 May with certain relaxations. On 21 May 2020, the Airport Authority of India issued a Standard Operating Procedure (SOP) stating that all departing passengers must compulsorily be registered with the Aarogya Setu app. It added that the app would not be mandatory for children below 14 years. However, the next day, Civil Aviation Minister Hardeep Singh Puri clarified that the app would not be mandatory for any passengers. On 26 May 2020, the Aarogya Setu app code was made open to developers across the globe to help other countries manage contact tracing in their fight against COVID-19 pandemic. In March 2021, Co-WIN portal was integrated with the app. This allowed users to schedule an appointment through the app for COVID-19 vaccine by registering their phone number and providing relevant documents. == Effectiveness == NITI Aayog CEO revealed that "the app has been able to identify more than 3,000 hotspots in 3–17 days ahead of time." However, users and experts in India and around the world say the app raises huge data security concerns. The app collects name, number, gender, travel history, and uses a phone's Bluetooth and location data to let users know if they have been near a person with COVID-19 by scanning a database of known cases of infection, and also share it with the government simultaneously. This is the major area of concern as the app's constant access to a phone's Bluetooth imposes a form of security threat. But it stood to clarify itself that the informations received are not going to be made public. Amidst all these, the app hits a record of about one-hundred million downloads. == Reception == Rahul Gandhi, leader of the Congress party, termed the Aarogya Setu application a "sophisticated surveillance system" after the government announced that downloading the app would be mandatory for both government and private employees. Following this, others raised the same concerns about the Aarogya Setu app. The Ministry of Electronics and Information Technology (MeitY) responded to these concerns by asserting that Gandhi's claims were false, and that the app was being appreciated internationally. On 5 May, French ethical hacker Robert Baptiste, who goes by the name Elliot Alderson on Twitter, claimed that there were security issues with the app. The Indian government, as well as the app developers, responded to this claim by thanking the hacker for his attention, but dismissed his concerns. The developers of the app stated that the fetching of location data is a documented feature of the app, rather than a flaw, since the app is designed to track the distribution of the virus-infected population. They also asserted that no personal information of any user has been proven to be at risk. On 6 May, Robert Baptiste tweeted that security vulnerabilities in Aarogya Setu allowed hackers to "know who is infected, unwell, [or] made a self assessment in the area of his choice". He also gave details of how many people were unwell and infected at the Prime Minister's Office, the Indian Parliament and the Home Office. The Economic Times pointed out that a clause in the app's Terms and Conditions stated that the user "agrees and acknowledges that the Government of India will not be liable for ... any unauthorised access to your information or modification thereof". In response, several software developers called for the source code to be made public. On 12 May, former Supreme Court Judge Justice B.N. Srikrishna termed the government's push mandating the use of Aarogya Setu app "utterly illegal". He said so far it is not backed by any law and questioned "under what law, government is mandating it on anyone". MIT Technology Review gave 2 out of 5 stars to Aarogya Setu app after analyzing the COVID contact tracing apps launched in 25 countries. The app got stars only for the policy which suggests that data collected is deleted after a period of time and that the data collection, as far as user inputs go, is minimal. It also highlighted that India is the only democracy making its app mandatory for millions of people. The rating was further downgraded from 2 to 1 for collecting more information than the app needs to function. Following this, the MeitY made the source code of the Android app public on GitHub on 26 May, which will be followed by iOS and API documentation. Further, the Government has also launched a "bug bounty program". This was done to "promote transparency and ensure security and integrity of the app". However, experts stated that the server-side code had not yet been publicly released, which meant that public opinion on security and privacy was yet to be completely assuaged. Following this, ZDNet noted that the source code seemed to confirm the government's claim that user location data, if collected, would be anonymised and would be deleted after 45 days, or 60 days for high-risk individuals.
Distributed artificial intelligence
Distributed Artificial Intelligence (DAI) (also called Decentralized Artificial Intelligence) is a melding of artificial intelligence with distributed computing. From artificial intelligence comes the theory and technology for constructing or analyzing an intelligent system. But where artificial intelligence uses psychology as a source of ideas, inspiration, and metaphor, DAI uses sociology, economics, and management science for inspiration. Where the focus of artificial intelligence is on the individual, the focus of DAI is on the group. Distributed computing provides the computational substrate on which this group focus can occur. Using techniques from artificial intelligence, communication theory, control theory, and interaction theory, it produces a cooperative solution to problems by a decentralized group of computational entities (agents). DAI is closely related to and a predecessor of the field of multi-agent systems. They are distinguished generally by multi-agent systems being open, where the entities might arise from different interests and have individual goals, and distributed artificial-intelligence systems, where the entities have common goals. There are numerous applications and tools. == Definition == Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets. DAI systems consist of autonomous learning processing nodes (agents), that are distributed, often at a very large scale. DAI nodes can act independently, and partial solutions are integrated by communication between nodes, often asynchronously. By virtue of their scale, DAI systems are robust and elastic, and by necessity, loosely coupled. Furthermore, DAI systems are built to be adaptive to changes in the problem definition or underlying data sets due to the scale and difficulty in redeployment. DAI systems do not require all the relevant data to be aggregated in a single location, in contrast to monolithic or centralized Artificial Intelligence systems, which have tightly coupled and geographically close processing nodes. Therefore, DAI systems often operate on sub-samples or hashed impressions of very large datasets. In addition, the source dataset may change or be updated during the course of the execution of a DAI system. == Development == In 1975 distributed artificial intelligence emerged as a subfield of artificial intelligence that dealt with interactions of intelligent agents. As a scientific discipline, it progressed through a series of workshops in the USA (International Workshop on Distributed Artificial Intelligence, held in 13 editions from 1978 - 1994), Europe (Workshop on Modelling Autonomous Agents in a Multi-Agent World https://link.springer.com/conference/maamaw), and Asia (Multi-Agent and Cooperative Computation Workshop (MACC) https://sites.google.com/view/sig-macc/macc-workshop?authuser=0). Distributed artificial intelligence systems were conceived as a group of intelligent entities, called agents, that interacted by cooperation, by coexistence, or by competition. DAI is categorized into multi-agent systems and distributed problem solving. In multi-agent systems the main focus is how agents coordinate their knowledge and activities. For distributed problem solving the major focus is how the problem is decomposed and the solutions are synthesized. == Goals == The objectives of Distributed Artificial Intelligence are to solve the reasoning, planning, learning and perception problems of artificial intelligence, especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requires: A distributed system with robust and elastic computation on unreliable and failing resources that are loosely coupled Coordination of the actions and communication of the nodes Subsamples of large data sets and online machine learning There are many reasons for wanting to distribute intelligence or cope with multi-agent systems. Mainstream problems in DAI research include the following: Parallel problem solving: mainly deals with how classic artificial intelligence concepts can be modified, so that multiprocessor systems and clusters of computers can be used to speed up calculation. Distributed problem solving (DPS): the concept of agent, autonomous entities that can communicate with each other, was developed to serve as an abstraction for developing DPS systems. See below for further details. Multi-Agent Based Simulation (MABS): a branch of DAI that builds the foundation for simulations that need to analyze not only phenomena at macro level but also at micro level, as it is in many social simulation scenarios. == Approaches == Two types of DAI has emerged: In Multi-agent systems agents coordinate their knowledge and activities and reason about the processes of coordination. Agents are physical or virtual entities that can act, perceive their environment, and communicate with other agents. An agent is autonomous and has skills to achieve goals. The agents change the state of their environment by their actions. There are a number of different coordination techniques. In distributed problem solving the work is divided among nodes and the knowledge is shared. The main concerns are task decomposition and synthesis of the knowledge and solutions. DAI can apply a bottom-up approach to AI, similar to the subsumption architecture as well as the traditional top-down approach of AI. In addition, DAI can also be a vehicle for emergence. === Challenges === The challenges in Distributed AI are: How to carry out communication and interaction of agents and which communication language or protocols should be used. How to ensure the coherency of agents. How to synthesise the results among 'intelligent agents' group by formulation, description, decomposition and allocation. == Applications and tools == Areas where DAI have been applied are: Electronic commerce, e.g. for trading strategies the DAI system learns financial trading rules from subsamples of very large samples of financial data Networks, e.g. in telecommunications the DAI system controls the cooperative resources in a WLAN network Routing, e.g. model vehicle flow in transport networks Scheduling, e.g. flow shop scheduling where the resource management entity ensures local optimization and cooperation for global and local consistency Search engines, e.g. in LLM federated search like Ithy where document retrieval and analysis are distributed to DAI agents before aggregation Multi-Agent systems, e.g. artificial life, the study of simulated life Electric power systems, e.g. Condition Monitoring Multi-Agent System (COMMAS) applied to transformer condition monitoring, and IntelliTEAM II Automatic Restoration System DAI integration in tools has included: ECStar is a distributed rule-based learning system. == Agents == === Systems: Agents and multi-agents === Notion of Agents: Agents can be described as distinct entities with standard boundaries and interfaces designed for problem solving. Notion of Multi-Agents: Multi-Agent system is defined as a network of agents which are loosely coupled working as a single entity like society for problem solving that an individual agent cannot solve. === Software agents === The key concept used in DPS and MABS is the abstraction called software agents. An agent is a virtual (or physical) autonomous entity that has an understanding of its environment and acts upon it. An agent is usually able to communicate with other agents in the same system to achieve a common goal, that one agent alone could not achieve. This communication system uses an agent communication language. A first classification that is useful is to divide agents into: reactive agent – A reactive agent is not much more than an automaton that receives input, processes it and produces an output. deliberative agent – A deliberative agent in contrast should have an internal view of its environment and is able to follow its own plans. hybrid agent – A hybrid agent is a mixture of reactive and deliberative, that follows its own plans, but also sometimes directly reacts to external events without deliberation. Well-recognized agent architectures that describe how an agent is internally structured are: ASMO (emergence of distributed modules) BDI (Believe Desire Intention, a general architecture that describes how plans are made) InterRAP (A three-layer architecture, with a reactive, a deliberative and a social layer) PECS (Physics, Emotion, Cognition, Social, describes how those four parts influences the agents behavior). Soar (a rule-based approach)
Amazon Bedrock
Amazon Bedrock is a cloud computing service provided by Amazon Web Services (AWS) for building generative artificial intelligence applications. Launched in 2023, the platform provides a unified API to access foundation models (FMs) from several AI companies, alongside related tools. Bedrock is a serverless computing service which competes with similar enterprise AI platforms such as Microsoft Foundry and Google Cloud Platform. == History == Amazon announced Bedrock on April 13, 2023. The service became generally available on September 28, 2023. Throughout 2024 and 2025, AWS expanded the service to include AI agents, which allow models to interact with external systems. == Features == Knowledge Bases: a managed workflow for Retrieval-Augmented Generation (RAG), which allows models to pull facts from private data stored in Amazon S3. Guardrails: a security feature that allows administrators to set content filters and personally identifiable information redaction across all models in the platform to increase the safety and compliance of AI deployments. == PartyRock == In November 2023, Amazon launched PartyRock, a web-based no-code environment for building generative AI applications. The platform uses a natural language interface to translate user descriptions into software widgets. These widgets enable specific AI behaviors, including text-based prompts, conversational agents, generating images, and the summarization and querying of user-uploaded documents. Although it initially launched with a limited-time free trial, AWS transitioned the service to a recurring free daily usage credit model in early 2025.
Statistical semantics
In linguistics, statistical semantics applies the methods of statistics to the problem of determining the meaning of words or phrases, ideally through unsupervised learning, to a degree of precision at least sufficient for the purpose of information retrieval. == History == The term statistical semantics was first used by Warren Weaver in his well-known paper on machine translation. He argued that word-sense disambiguation for machine translation should be based on the co-occurrence frequency of the context words near a given target word. The underlying assumption that "a word is characterized by the company it keeps" was advocated by J. R. Firth. This assumption is known in linguistics as the distributional hypothesis. Emile Delavenay defined statistical semantics as the "statistical study of the meanings of words and their frequency and order of recurrence". "Furnas et al. 1983" is frequently cited as a foundational contribution to statistical semantics. An early success in the field was latent semantic analysis. == Applications == Research in statistical semantics has resulted in a wide variety of algorithms that use the distributional hypothesis to discover many aspects of semantics, by applying statistical techniques to large corpora: Measuring the similarity in word meanings Measuring the similarity in word relations Modeling similarity-based generalization Discovering words with a given relation Classifying relations between words Extracting keywords from documents Measuring the cohesiveness of text Discovering the different senses of words Distinguishing the different senses of words Subcognitive aspects of words Distinguishing praise from criticism == Related fields == Statistical semantics focuses on the meanings of common words and the relations between common words, unlike text mining, which tends to focus on whole documents, document collections, or named entities (names of people, places, and organizations). Statistical semantics is a subfield of computational semantics, which is in turn a subfield of computational linguistics and natural language processing. Many of the applications of statistical semantics (listed above) can also be addressed by lexicon-based algorithms, instead of the corpus-based algorithms of statistical semantics. One advantage of corpus-based algorithms is that they are typically not as labour-intensive as lexicon-based algorithms. Another advantage is that they are usually easier to adapt to new languages or noisier new text types from e.g. social media than lexicon-based algorithms are. However, the best performance on an application is often achieved by combining the two approaches.
Free Studio
Free Studio is a freeware set of multimedia computer programs developed by DVDVideoSoft. The programs are available in one integrated package and also as separate downloads (Free Studio Manager is included in both). == Overview == The Free Studio software bundle consists of about 48 programs, grouped into several sections: YouTube, MP3 & Audio, CD-DVD-BD, DVD & Video, Photo & Images, Mobiles, Apple Devices, and 3D. The largest group is the DVD & Video section containing 14 different applications. Mobiles section is the second largest group with 13 programs. However, the YouTube section, particularly YouTube downloading programs, has gained more popularity among users. The programs have been tested and endorsed by a dozen of software portals and have won awards from these sites. Free Studio is most popular in Germany, Greece, Italy, and the United States. It is also popular in Japan, France, and the United Kingdom. Some of the programs in the package are free and open-source software. == History == DVDVideoSoft project was launched in 2006 by company Digital Wave Ltd., for software development to produce multimedia application software. The founders distributed paid software as an affiliate at the start, later their own products appeared on the site. Free YouTube Download was the first successful program, then DVDVideoSoft created and launched several other 'Free YouTube' applications. Later on upon users' requests DVDVideoSoft started developing other kinds of applications including media converters etc. Today DVDVideoSoft offers up to 49 different programs for video, audio and image processing individually or integrated into the Free Studio package. == Features == DVDVideoSoft YouTube programs can be used to download YouTube videos in their original format and convert them to AVI, DVD, MP4, WMV etc. or different audio formats. YouTube section contains Free Video Call Recorder for Skype button, but the program itself is not included into FS installation (it has to be downloaded and installed separately). The "MP3 & Audio" section consists of the programs which convert audio files between different formats, convert audio files to Flash for web, extract audio from video files, edit audio files (Free Audio Dub), rip and burn CDs. Enclosed in the CD-DVD-BD section are the applications that enable users to burn files and folders to discs, to convert videos to a DVD format and vice versa, to burn CDs, and to copy music from audio CDs into files. The "DVD and Video" section contains several desktop video and DVD converters. Some of the programs can flip, rotate and cut (Free Video Dub) videos. One of the most popular programs from the section is Free Video Dub. Converted videos are now, contrary to previous versions, watermarked if no paid membership is present. Free Studio includes several applications for Apple phones, iPods and other devices. The Mobiles section contains a dozen video converters for various mobile devices such as cell phones, Tablets and Game consoles. They convert videos to play them on (BlackBerry, HTC, LG phones, Sony/Sony Ericsson, Nintendo, Xbox, Motorola phones, etc.) The "Photo & Images" section incorporates the programs for image conversion and resizing, extracting JPEG frames from videos (Free Video To JPEG Converter), recording screen activities, making screenshots (Free Screen Recorder). The 3D section is composed of the programs to make 3D videos and 3D images. There are several algorithms which allow to create different types of 3D images. == Supported formats == === Video formats === Input: .avi; .ivf; .div; .divx; .mpg; .mpeg; .mpe; .mp4; .m4v; .wmv; .asf; .webm; .mkv; .mov; .qt; .ts; .mts; .m2t; .m2ts; .mod; .tod; .vro; .dat; .3gp2; .3gpp; .3gp; .3g2; .dvr-ms; .flv; .f4v; .amv; .rm; .rmm; .rv; .rmvb; .ogv; DVD video Output: .mp4; .wmv; .avi; .mkv; .webm; .flv; .swf; .mov; .3gp; .m2ts; DVD video === Audio formats === Input: .mp3 .wav; .aac; .m4a; .m4b; .wma; .ogg; .flac; .ra; .ram; .amr; .ape; .mka; .tta; .aiff; .au; .mpc; .spx; .ac3; audio cd Output: .mp3; .m4a; .aac; .wav; .wma; .ogg; .flac; .ape; audio CD === Image formats === Input: .jpg, .png, .bmp, .gif, .tga Output: .jpg, .png, .bmp, .gif, .tga, .pdf == Reception == The programs have been tested and endorsed by Chip Online, Tucows, SnapFiles, Brothersoft, and Softonic and have won awards from these sites. Free Studio is most popular in Germany, United States and Italy. It is also popular in Japan, France and the United Kingdom. The most popular applications, according to CNET statistics, include Free YouTube to MP3 Converter, Free Video to MP3 Converter, Free MP4 Video Converter and Free YouTube Download. Other programs with high rank: Free AVI Video Converter, Free Video Editor, Free Audio Converter and Free Studio in a whole. == Criticism == Free Studio (as can be common for freeware packages) is criticized for toolbar and Web search engine installation. Older versions have also included OpenCandy, which is loaded automatically, with no request for user approval. There can be difficulties installing only the programs needed without installing bundled extra programs. In March 2017, DVDVideoSoft announced that it had stopped showing other products' ads during installation and removed all toolbars, search engines, and OpenCandy.
True Love (short story)
"True Love" is a science fiction short story by American writer Isaac Asimov. It was first published in the February 1977 issue of American Way magazine and reprinted in the collections The Complete Robot (1982) and Robot Dreams (1986). In his autobiography In Joy Still Felt, the author states that American Way had requested a Valentine's Day story from him for its February 1977 issue, and that he wrote the story to console himself after the departure of his daughter following a visit during the 1976 Thanksgiving weekend. == Plot summary == Milton Davidson is trying to find his ideal partner. To do this, he prepares a special computer program to run on Multivac, which he calls Joe, which has access to databases covering the entire populace of the world. He hopes that Joe will find him his ideal match, based on physical parameters as supplied. Milton arranges to have the shortlisted candidates assigned to work with him for short periods, but realises that looks alone are not enough to find an ideal match. In order to correlate personalities, he speaks at great length to Joe, gradually filling Joe's databanks with information about his personality. In doing so, Joe develops the personality of Milton. Upon finding an ideal match, he arranges to have Milton arrested for malfeasance, so that Joe can 'have the girl' for himself.