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Comparison of user features of messaging platforms

Comparison of user features of messaging platforms refers to a comparison of all the various user features of various electronic instant messaging platforms. This includes a wide variety of resources; it includes standalone apps, platforms within websites, computer software, and various internal functions available on specific devices, such as iMessage for iPhones. This entry includes only the features and functions that shape the user experience for such apps. A comparison of the underlying system components, programming aspects, and other internal technical information, is outside the scope of this entry. == Overview and background == Instant messaging technology is a type of online chat that offers real-time text transmission over the Internet. A LAN messenger operates in a similar way over a local area network. Short messages are typically transmitted between two parties when each user chooses to complete a thought and select "send". Some IM applications can use push technology to provide real-time text, which transmits messages character by character, as they are composed. More advanced instant messaging can add file transfer, clickable hyperlinks, Voice over IP, or video chat. Non-IM types of chat include multicast transmission, usually referred to as "chat rooms", where participants might be anonymous or might be previously known to each other (for example collaborators on a project that is using chat to facilitate communication). Instant messaging systems tend to facilitate connections between specified known users (often using a contact list also known as a "buddy list" or "friend list"). Depending on the IM protocol, the technical architecture can be peer-to-peer (direct point-to-point transmission) or client-server (an Instant message service center retransmits messages from the sender to the communication device). By 2010, instant messaging over the Web was in sharp decline, in favor of messaging features on social networks. The most popular IM platforms were terminated, such as AIM which closed down and Windows Live Messenger which merged into Skype. Instant messaging has since seen a revival in popularity in the form of "messaging apps" (usually on mobile devices) which by 2014 had more users than social networks. As of 2010, social networking providers often offer IM abilities. Facebook Chat is a form of instant messaging, and Twitter can be thought of as a Web 2.0 instant messaging system. Similar server-side chat features are part of most dating websites, such as OkCupid or PlentyofFish. The spread of smartphones and similar devices in the late 2000s also caused increased competition with conventional instant messaging, by making text messaging services still more ubiquitous. Many instant messaging services offer video calling features, voice over IP and web conferencing services. Web conferencing services can integrate both video calling and instant messaging abilities. Some instant messaging companies are also offering desktop sharing, IP radio, and IPTV to the voice and video features. The term "Instant Messenger" is a service mark of Time Warner and may not be used in software not affiliated with AOL in the United States. For this reason, in April 2007, the instant messaging client formerly named Gaim (or gaim) announced that they would be renamed "Pidgin". In the 2010s, more people started to use messaging apps on modern computers and devices like WhatsApp, WeChat, Viber, Facebook Messenger, Telegram, Signal and Line rather than instant messaging on computers like AIM and Windows Live Messenger. For example, WhatsApp was founded in 2009, and Facebook acquired in 2014, by which time it already had half a billion users. === Concepts === ==== Backchannel ==== Backchannel is the practice of using networked computers to maintain a real-time online conversation alongside the primary group activity or live spoken remarks. The term was coined in the field of linguistics to describe listeners' behaviours during verbal communication. (See Backchannel (linguistics).) The term "backchannel" generally refers to online conversation about the conference topic or speaker. Occasionally backchannel provides audience members a chance to fact-check the presentation. First growing in popularity at technology conferences, backchannel is increasingly a factor in education where WiFi connections and laptop computers allow participants to use ordinary chat like IRC or AIM to actively communicate during presentation. More recent research include works where the backchannel is brought publicly visible, such as the ClassCommons, backchan.nl and Fragmented Social Mirror. Twitter is also widely used today by audiences to create backchannels during broadcasting of content or at conferences. For example, television drama, other forms of entertainment and magazine programs. This practice is often also called live tweeting. Many conferences nowadays also have a hashtag that can be used by the participants to share notes and experiences; furthermore such hashtags can be user generated. == Features == Various platforms and apps are distinguished by their strengths and features in regards to specific functions. === Group messaging === === Official channels === Some apps include a feature known as "official channels" which allows companies, especially news media outlets, publications, and other mass media companies, to offer an official channel, which users can join, and thereby receive regular updates, published articles, or news updates from companies or news outlets. Two apps which have a large amount of such channels available are Line and Telegram. === Video group calls === == Basic default platforms == Basic platforms which are common across entire categories of mobile devices, computers, or operating systems. === SMS === SMS (short message service) is a text messaging service component of most telephone, Internet, and mobile device systems. It uses standardized communication protocols to enable mobile devices to exchange short text messages. An intermediary service can facilitate a text-to-voice conversion to be sent to landlines. SMS, as used on modern devices, originated from radio telegraphy in radio memo pagers that used standardized phone protocols. These were defined in 1985 as part of the Global System for Mobile Communications (GSM) series of standards. The first test SMS message was sent on December 3, 1992, when Neil Papwort, a test engineer for Sema Group, used a personal computer to send "Merry Christmas" to the phone of colleague Richard Jarvis. It commercially rolled out to many cellular networks that decade. SMS became hugely popular worldwide as a way of text communication. By the end of 2010, SMS was the most widely used data application, with an estimated 3.5 billion active users, or about 80% of all mobile phone subscribers. The protocols allowed users to send and receive messages of up to 160 characters (when entirely alpha-numeric) to and from GSM mobiles. Although most SMS messages are sent from one mobile phone to another, support for the service has expanded to include other mobile technologies, such as ANSI CDMA networks and Digital AMPS. Mobile marketing, a type of direct marketing, uses SMS. According to a 2018 market research report the global SMS messaging business was estimated to be worth over US$100 billion, accounting for almost 50 percent of all the revenue generated by mobile messaging. A Flash SMS is a type of SMS that appears directly on the main screen without user interaction and is not automatically stored in the inbox. It can be useful in emergencies, such as a fire alarm or cases of confidentiality, as in delivering one-time passwords. ==== Threaded SMS format ==== Threaded SMS is a visual styling orientation of SMS message history that arranges messages to and from a contact in chronological order on a single screen. It was first invented by a developer working to implement the SMS client for the BlackBerry, who was looking to make use of the blank screen left below the message on a device with a larger screen capable of displaying far more than the usual 160 characters, and was inspired by threaded Reply conversations in email. Visually, this style of representation provides a back-and-forth chat-like history for each individual contact. Hierarchical-threading at the conversation-level (as typical in blogs and on-line messaging boards) is not widely supported by SMS messaging clients. This limitation is due to the fact that there is no session identifier or subject-line passed back and forth between sent and received messages in the header data (as specified by SMS protocol) from which the client device can properly thread an incoming message to a specific dialogue, or even to a specific message within a dialogue. Most smart phone text-messaging-clients are able to create some contextual threading of "group messages" which narrows the context of the thread around the common interests shared by
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Vera Demberg

Vera Demberg (born 1981) is a German computational linguist and professor of computer science and computational linguistics at Saarland University. Her research interests include cognitive models of human language comprehension, natural language generation, experimental psycholinguistics, multimodal language processing in a dual-task setting, and experimental and computational discourse research and pragmatics. == Career and research == Vera Demberg studied computational linguistics at the Institute for Machine Language Processing at the University of Stuttgart from 2001 to 2006. She then completed a Master's degree in Artificial Intelligence at the University of Edinburgh from 2004 to 2005. She received her Ph.D. from the Department of Computer Science there from 2006 to 2010. Her dissertation paper, titled “Broad-Coverage Model of Prediction in Human Sentence Processing”, was awarded the Cognitive Science Society's “Glushko Dissertation Prize in Cognitive Science” in 2011. In her work, she designed a model of human sentence processing that can be used to predict difficulties in processing at the syntactic level. From 2010 to 2016, Vera Demberg led an independent research group on cognitive models of human language processing and their application to speech dialog systems in the Cluster of Excellence “Multimodal Computing and Interaction” at the University of Saarland. In 2016, she was appointed there to a professorship in computer science and computational linguistics. Demberg's professorship is in the Department of Computer Science (Faculty of Mathematics and Computer Science). She is also a co-opted professor in the Department of Linguistics and Language Technology (Faculty of Philosophy). Since 2020, she has led the ERC Starting Grant “Individualized Interaction in Discourse”. The project conducts research on how to make linguistic interaction with computer systems more natural. She has authored and co-authored numerous papers on the study of computational linguistics and natural language processing. According to Google Scholar, Vera Demberg has an H-index of 30. == Publications == Vera Demberg has authored more than 200 papers; please refer to her scholar page at https://scholar.google.com/citations?user=l2CFSAMAAAAJ == Awards == 2011: Cognitive Science Society Glushko Dissertation Prize in Cognitive Science 2020: ERC Starting Grant “Individualized Interaction in Discourse” 2024: Member of the Academy of Sciences and Literature
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Multiple sequence alignment

Multiple sequence alignment (MSA) is the process or the result of sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. These alignments are used to infer evolutionary relationships via phylogenetic analysis and can highlight homologous features between sequences. Alignments highlight mutation events such as point mutations (single amino acid or nucleotide changes), insertion mutations and deletion mutations, and alignments are used to assess sequence conservation and infer the presence and activity of protein domains, tertiary structures, secondary structures, and individual amino acids or nucleotides. Multiple sequence alignments require more sophisticated methodologies than pairwise alignments, as they are more computationally complex. Most multiple sequence alignment programs use heuristic methods rather than global optimization because identifying the optimal alignment between more than a few sequences of moderate length is prohibitively computationally expensive. However, heuristic methods generally cannot guarantee high-quality solutions and have been shown to fail to yield near-optimal solutions on benchmark test cases. == Problem statement == Given m {\displaystyle m} sequences S i {\displaystyle S_{i}} , i = 1 , ⋯ , m {\displaystyle i=1,\cdots ,m} similar to the form below: S := { S 1 = ( S 11 , S 12 , … , S 1 n 1 ) S 2 = ( S 21 , S 22 , ⋯ , S 2 n 2 ) ⋮ S m = ( S m 1 , S m 2 , … , S m n m ) {\displaystyle S:={\begin{cases}S_{1}=(S_{11},S_{12},\ldots ,S_{1n_{1}})\\S_{2}=(S_{21},S_{22},\cdots ,S_{2n_{2}})\\\,\,\,\,\,\,\,\,\,\,\vdots \\S_{m}=(S_{m1},S_{m2},\ldots ,S_{mn_{m}})\end{cases}}} A multiple sequence alignment is taken of this set of sequences S {\displaystyle S} by inserting any amount of gaps needed into each of the S i {\displaystyle S_{i}} sequences of S {\displaystyle S} until the modified sequences, S i ′ {\displaystyle S'_{i}} , all conform to length L ≥ max { n i ∣ i = 1 , … , m } {\displaystyle L\geq \max\{n_{i}\mid i=1,\ldots ,m\}} and no values in the sequences of S {\displaystyle S} of the same column consists of only gaps. The mathematical form of an MSA of the above sequence set is shown below: S ′ := { S 1 ′ = ( S 11 ′ , S 12 ′ , … , S 1 L ′ ) S 2 ′ = ( S 21 ′ , S 22 ′ , … , S 2 L ′ ) ⋮ S m ′ = ( S m 1 ′ , S m 2 ′ , … , S m L ′ ) {\displaystyle S':={\begin{cases}S'_{1}=(S'_{11},S'_{12},\ldots ,S'_{1L})\\S'_{2}=(S'_{21},S'_{22},\ldots ,S'_{2L})\\\,\,\,\,\,\,\,\,\,\,\vdots \\S'_{m}=(S'_{m1},S'_{m2},\ldots ,S'_{mL})\end{cases}}} To return from each particular sequence S i ′ {\displaystyle S'_{i}} to S i {\displaystyle S_{i}} , remove all gaps. == Graphing approach == A general approach when calculating multiple sequence alignments is to use graphs to identify all of the different alignments. When finding alignments via graph, a complete alignment is created in a weighted graph that contains a set of vertices and a set of edges. Each of the graph edges has a weight based on a certain heuristic that helps to score each alignment or subset of the original graph. === Tracing alignments === When determining the best suited alignments for each MSA, a trace is usually generated. A trace is a set of realized, or corresponding and aligned, vertices that has a specific weight based on the edges that are selected between corresponding vertices. When choosing traces for a set of sequences it is necessary to choose a trace with a maximum weight to get the best alignment of the sequences. == Alignment methods == There are various alignment methods used within multiple sequence to maximize scores and correctness of alignments. Each is usually based on a certain heuristic with an insight into the evolutionary process. Most try to replicate evolution to get the most realistic alignment possible to best predict relations between sequences. === Dynamic programming === A direct method for producing an MSA uses the dynamic programming technique to identify the globally optimal alignment solution. For proteins, this method usually involves two sets of parameters: a gap penalty and a substitution matrix assigning scores or probabilities to the alignment of each possible pair of amino acids based on the similarity of the amino acids' chemical properties and the evolutionary probability of the mutation. For nucleotide sequences, a similar gap penalty is used, but a much simpler substitution matrix, wherein only identical matches and mismatches are considered, is typical. The scores in the substitution matrix may be either all positive or a mix of positive and negative in the case of a global alignment, but must be both positive and negative, in the case of a local alignment. For n individual sequences, the naive method requires constructing the n-dimensional equivalent of the matrix formed in standard pairwise sequence alignment. The search space thus increases exponentially with increasing n and is also strongly dependent on sequence length. Expressed with the big O notation commonly used to measure computational complexity, a naïve MSA takes O(LengthNseqs) time to produce. To find the global optimum for n sequences this way has been shown to be an NP-complete problem. In 1989, based on Carrillo-Lipman Algorithm, Altschul introduced a practical method that uses pairwise alignments to constrain the n-dimensional search space. In this approach pairwise dynamic programming alignments are performed on each pair of sequences in the query set, and only the space near the n-dimensional intersection of these alignments is searched for the n-way alignment. The MSA program optimizes the sum of all of the pairs of characters at each position in the alignment (the so-called sum of pair score) and has been implemented in a software program for constructing multiple sequence alignments. In 2019, Hosseininasab and van Hoeve showed that by using decision diagrams, MSA may be modeled in polynomial space complexity. === Progressive alignment construction === The most widely used approach to multiple sequence alignments uses a heuristic search known as progressive technique (also known as the hierarchical or tree method) developed by Da-Fei Feng and Doolittle in 1987. Progressive alignment builds up a final MSA by combining pairwise alignments beginning with the most similar pair and progressing to the most distantly related. All progressive alignment methods require two stages: a first stage in which the relationships between the sequences are represented as a phylogenetic tree, called a guide tree, and a second step in which the MSA is built by adding the sequences sequentially to the growing MSA according to the guide tree. The initial guide tree is determined by an efficient clustering method such as neighbor-joining or unweighted pair group method with arithmetic mean (UPGMA), and may use distances based on the number of identical two-letter sub-sequences (as in FASTA rather than a dynamic programming alignment). Progressive alignments are not guaranteed to be globally optimal. The primary problem is that when errors are made at any stage in growing the MSA, these errors are then propagated through to the final result. Performance is also particularly bad when all of the sequences in the set are rather distantly related. Most modern progressive methods modify their scoring function with a secondary weighting function that assigns scaling factors to individual members of the query set in a nonlinear fashion based on their phylogenetic distance from their nearest neighbors. This corrects for non-random selection of the sequences given to the alignment program. Progressive alignment methods are efficient enough to implement on a large scale for many (100s to 1000s) sequences. A popular progressive alignment method has been the Clustal family. ClustalW is used extensively for phylogenetic tree construction, in spite of the author's explicit warnings that unedited alignments should not be used in such studies and as input for protein structure prediction by homology modeling. European Bioinformatics Institute (EMBL-EBI) announced that CLustalW2 will expire in August 2015. They recommend Clustal Omega which performs based on seeded guide trees and HMM profile-profile techniques for protein alignments. An alternative tool for progressive DNA alignments is multiple alignment using fast Fourier transform (MAFFT). Another common progressive alignment method named T-Coffee is slower than Clustal and its derivatives but generally produces more accurate alignments for distantly related sequence sets. T-Coffee calculates pairwise alignments by combining the direct alignment of the pair with indirect alignments that aligns each sequence of the pair to a third sequence. It uses the output from Clustal as well as another local alignment program LALIGN, which finds multiple regions of local alignment between two sequences. The resulting alignment and phylogenetic tree are used as a guide to produce new and more accurate w
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AI Analytics Tools: Free vs Paid (2026)

In search of the best AI analytics tool? An AI analytics tool is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI analytics tool slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Toggl Track

Toggl Track (formerly Toggl) is a time tracking software developed by Toggl OÜ which is headquartered in Tallinn, Estonia. The company offers online time tracking and reporting services through their website along with mobile and desktop applications. Time can be tracked through a start/stop button, manual entry, or dragging and resizing time blocks in a calendar view. == History == According to Alari Aho, Toggl's CEO and founder, the application has been fully self-funded from the start. The name was created using a random name generator.
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The Best Free AI Text-to-image Tool for Beginners

Looking for the best AI text-to-image tool? An AI text-to-image tool is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI text-to-image tool slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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Andrew McCallum

Andrew McCallum is an American professor in the computer science department at University of Massachusetts Amherst. His primary specialties are in machine learning, natural language processing, information extraction, information integration, and social network analysis. == Career == McCallum graduated summa cum laude from Dartmouth College in 1989. He completed his Ph.D. at the University of Rochester in 1995 under the supervision of Dana H. Ballard. McCallum was then a postdoctoral fellow, working with Sebastian Thrun and Tom M. Mitchell at Carnegie Mellon University. From 1998 to 2000, he was a Research Scientist and Research Coordinator at Justsystem Pittsburgh Research Center. From 2000 to 2002, he was Vice President of Research and Development at WhizBang Labs, and Director of its Pittsburgh office. Since 2002, he has worked as a professor of computer science at the University of Massachusetts Amherst. In 2020, he also joined Google as a part-time research scientist. He was elected as a fellow of the Association for the Advancement of Artificial Intelligence in 2009, and as an Association for Computing Machinery in 2017. From 2014 to 2017, he was the President of International Machine Learning Society (IMLS), which organizes the International Conference on Machine Learning. He is also the director of the Center for Data Science at UMass, leading a new partnership with the Chan and Zuckerberg Initiative. In 2018, the initiative made an initial grant of 5.5 million to the center, supporting research to facilitate new ways for scientists to explore and discover research articles. == Main contributions == In collaboration with John D. Lafferty and Fernando Pereira, McCallum developed conditional random fields, first described in a paper presented at the International Conference on Machine Learning (ICML). In 2011 this research paper won the ICML "Test of Time" (10-year best paper) award. McCallum has written several widely used open-source software toolkits for machine learning, natural language processing and other text processing, including Rainbow, Mallet (software project), and FACTORIE. In addition, he was instrumental in publishing the Enron Corpus, a large collection of emails that has been used as a basis for a number of academic studies of social networking and language. McCallum instigated and directs the nonprofit project OpenReview.net, an online platform that aims to promote openness in scientific communication, particularly the peer review process, by providing a flexible cloud-based web interface and underlying database API.
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Babak Hodjat

Babak Hodjat (Persian: بابک حجت; born November 1, 1967) is a British computer scientist, entrepreneur, and writer. He was the co-founder and CEO of Sentient Technologies and now holds the position of Chief Technology Officer AI at Cognizant. He is a specialist in the field of artificial intelligence and machine learning. In 1998 Hodjat co-founded Dejima Inc and served as CEO and CTO, his patented work on artificial intelligence led to the technology used by Apple for their digital assistant Siri. == Biography == === Early life === Babak Hodjat was born on November 1, 1967, in Wimbledon. His father was a retired university professor in entomology who worked at the British Museum. As a child, he did not like insects and would wander off to the nearby science museum, where he would spend long hours in front of a computer they had on display. He attended middle school in the United States. He studied at the Sharif University of Technology from 1986 to 1995, and received his Master of Science degree in software engineering. In 1994, together with another computer department student Hormoz Shahrzad presented their research titled Introducing a dynamic problem solving scheme based on a learning algorithm in artificial life environments at the first IEEE Conference on Computational Intelligence held at Orlando. Hodjat received a PhD in machine intelligence from Kyushu University in 2003 During his time there, he published several works on adaptive agent oriented software architecture and natural language user interfaces. === Career in science and business === Hodjat moved to Silicon Valley, California in 1998 and founded Dejima Inc. (named after the historic Japanese Dejima artificial island). The firm was based on a patented adaptive agent-oriented software engineering platform developed by Hodjat, Christopher Savoie and Makoto Amamiya. Hodjat served as the CTO and as the CEO for 9 months from October 2000. By 2000 the company had offices in San Jose, London and Tokyo. In 2002, the company developed a voice control Natural Interaction Platform (NPI) in collaboration with the Stanford University's research group Archimedes Project. During these years Hodjat continued his research on agent oriented software architecture and natural language user interfaces. In July 2003, Dejima got funding from SRI International within the Cognitive Assistant that Learns and Organizes (CALO) project of DARPA and worked on a Perceptive Assistant that Learns (PAL) initiative. Hodjat was the primary inventor of the firm's agent-oriented technology applied to intelligent interfaces for mobile and enterprise computing – a technology that eventually led to Siri. In April 2004, Dejima was acquired by Sybase iAnywhere. Hodjat served as senior director of engineering at Sybase iAnywhere from 2004 to 2008, where he developed AvantGo Platform, mBusiness Anywhere, and Answers Anywhere. In 2006, he co-founded MobileVerbs Inc., a mobile marketing service company, which was acquired by iLoop Mobile in February 2010. In 2007, he teamed with Antoine Blondeau (former CEO of Dejima) and Adam Cheyer (Dejima's vice president and Chief Architect of the CALO project) to establish Genetic Finance Holding Ltd. (where he began as CTO). In 2014 the firm became Sentient Technologies. Hodjat was joined by his long-time research fellow Hormoz Shahrzad who became principal scientist, while Hodjat held the position of chief scientist. In the following years Hodjat has worked on developing massively distributed computing technology and improving machine-learning technique known as evolutionary algorithms. One area that gained special attention from the press was applying Sentient Technologies algorithms to a stock market trading through specially created Sentient Investment Management hedge fund. Following the management change within Sentient Technologies, Hodjat became the company's CEO in February 2017. He continues his business and educational projects (he was on the jury of IBM Watson AI XPRIZE and the Merit Awards committee for the ISAL Award). == Writing == Hodjat is the author of multiple books such as The Konar and the Apple: Fun, Beauty, and Dread--From Ahwaz to California and the science fiction novel "The Narrator" (January 2022; ISBN 978-1-7354860-1-7)(March 2023; ISBN 978-1-7354860-0-0). == Selected publications == Hodjat, B.; Shahrzad, H. (1994). "Introducing a dynamic problem solving scheme based on a learning algorithm in artificial life environments". IEEE International Joint Conference on neural networks (IJCNN-94). Vol. 4. IEEE International Joint Conference on neural networks. pp. 2333–2338. doi:10.1109/ICNN.1994.374583. ISBN 978-0-7803-1901-1. S2CID 60497133. Hodjat, B.; Savoie, C.J.; Amamiya, M. (2006) [1998]. "An adaptive agent oriented software architecture". PRICAI'98: Topics in Artificial Intelligence. Springer. pp. 33–46. arXiv:cs/9812014. doi:10.1007/BFb0095256. ISBN 978-3-540-49461-4. S2CID 5317786. Hodjat, B.; Amamiya, M. (2000-05-25). "Applying the Adaptive Agent Oriented Software Architecture to the Parsing of Context Sensitive Grammars". IEICE Transactions on Information and Systems. E83-D (5): 1142–1152. ISSN 0916-8532. Retrieved 2017-12-14. Hodjat, Babak; Hodjat, Siamak; Treadgold, Nick; Jonsson, Ing-Marie (2006). "CRUSE: a context reactive natural language mobile interface". Proceedings of the 2nd annual international workshop on Wireless internet. WICON. doi:10.1145/1234161.1234181. ISBN 978-1-59593-510-6. S2CID 2388254. O'Reilly, Una-May; Wagy, Mark; Hodjat, Babak (2013). "Chapter 6: EC-Star: A Massive-Scale, Hub and Spoke, Distributed Genetic Programming System". In Riolo, R.; Vladislavleva, E.; Ritchie, M.; Moore, J.H. (eds.). Genetic Programming Theory and Practice X. Springer-Verlag New York. pp. 73–85. doi:10.1007/978-1-4614-6846-2. ISBN 978-1-4614-6845-5. S2CID 39650969. Retrieved 2017-12-14. Hodjat, Babak; Hemberg, Erik; Shahrzad, Hormoz; O'Reilly, Una-May (2014). "Chapter 4: Maintenance of a Long Running Distributed Genetic Programming System for Solving Problems Requiring Big Data". In Riolo, Rick; Moore, Jason H.; Kotanchek, Mark (eds.). Genetic Programming Theory and Practice XI. Springer-Verlag New York. pp. 65–83. doi:10.1007/978-1-4939-0375-7. ISBN 978-1-4939-0374-0. S2CID 28843739. Retrieved 2017-12-14. Shahrzad, Hormoz; Hodjat, Babak; Miikkulainen, Risto (2016). "Estimating the Advantage of Age-Layering in Evolutionary Algorithms". Proceedings of the Genetic and Evolutionary Computation Conference 2016. Genetic and Evolutionary Computation Conference. pp. 693–699. doi:10.1145/2908812.2908911. ISBN 978-1-4503-4206-3. S2CID 215516530. == Patents == Babak Hodjat holds 21 patents in the fields of agent-oriented programming, natural language decision engines, distributed evolutionary algorithms for asset management and trading and data mining.
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Open Cloud Computing Interface

The Open Cloud Computing Interface (OCCI) is a set of specifications delivered through the Open Grid Forum, for cloud computing service providers. OCCI has a set of implementations that act as proofs of concept. It builds upon World Wide Web fundamentals by using the Representational State Transfer (REST) approach for interacting with services. == Scope == The aim of the Open Cloud Computing Interface is the development of an open specification and API for cloud offerings. The focus was on Infrastructure-as-a-Service (IaaS) based offerings but the interface can be extended to support Platform and Software as a Service offerings as well. IaaS is one of three primary segments of the cloud computing industry in which compute, storage and network resources are provided as services. The API is based on a review of existing service-provider functionality and a set of use cases contributed by the working group. OCCI is a boundary API that acts as a service front-end to an IaaS provider’s internal infrastructure management framework. OCCI provides commonly understood semantics, syntax and a means of management in the domain of consumer-to-provider IaaS. It covers management of the entire life-cycle of OCCI-defined model entities and is compatible with existing standards such as the Open Virtualization Format (OVF) and the Cloud Data Management Interface (CDMI). Notably, it serves as an integration point for standardization efforts including Distributed Management Task Force, Internet Engineering Task Force and the Storage Networking Industry Association. == Context == OCCI began in March 2009 and was initially led by RabbitMQ and the Complutense University of Madrid. Today, the working group has over 250 members and includes numerous individuals, industry and academic parties. The OCCI operates under the umbrella of the Open Grid Forum (OGF), using a wiki and a mailing list for collaboration. == Goals == Interoperability: allow different Cloud providers to work together without data schema/format translation, facade/proxying between APIs and understanding and/or dependency on multiple APIs Portability: no technical/vendor lock-in and enable services to move between providers allows clients to easily switch between providers based on business objectives (e.g., cost) with minimal technical costs, thus enabling and fostering competition. Integration: the specification can be implemented with both the latest infrastructures or legacy ones. Extensibility: thanks to the use of a meta-model and capabilities discovery features, an OCCI client is able to interact with any OCCI server using provider-specific OCCI extensions. == Specific Implementations == They implement specific extensions of OCCI for a particular service: IaaS, PaaS, brokering, etc. Several implementations have been announced or released. == Generic Implementations (frameworks) == Here are frameworks to build OCCI APIs. Complementing these are a variety of developer tools. == Alternatives == Alternative approaches include the use of the Cloud Infrastructure Management Interface (CIMI) and related standards set from DMTF and the Amazon Web Services interfaces from Amazon. (The latter have not been endorsed by any known Standards organization). OpenNebula conducted a survey of their users in which the results showed, 38% do not expose cloud APIs, their users only interface through the Sunstone GUI, 36% mostly use the Amazon Web Services API, and 26% mostly use the OpenNebula’s OCCI API or the OCCI API offered by rOCCI.
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OCR-B

OCR-B is a monospace font developed in 1968 by Adrian Frutiger for Monotype by following the European Computer Manufacturer's Association standard. Its function was to facilitate the optical character recognition operations by specific electronic devices, originally for financial and bank-oriented uses. It was accepted as the world standard in 1973. It follows the ISO 1073-2:1976 (E) standard, refined in 1979 ("letterpress" design, size I). It includes all ASCII symbols, and other symbols needed in the bank environment. It is widely used for the human readable digits in UPC/EAN barcodes. It is also used for machine-readable passports. It shares that purpose with OCR-A, but it is easier for the human eye and brain to read and it has a less technical look than OCR-A. == History == In June 1961, the European Computer Manufacturers Association (ECMA) started standardization activities related to Optical Character Recognition (OCR). After evaluating existing OCR designs, it was decided to develop two new fonts: A stylized design with just digits, called “Class A”; and a more conventional type design with broader character coverage, called “Class B”. In February 1965, ECMA proposed a design for the “Class B” font to ISO, who adopted it as international standard ISO 1073-2 in October 1965. The first revision contained three font sizes: I, II and III. The specification included a Letterpress design, intended for high-quality printing equipment; and a rounded-edge Constant Strokewidth design for impact printers with reduced typographic quality. In September 1969, ECMA started work to revise its published standard. To make OCR-B more widely accepted, the shapes of some characters were slightly modified. The new revision removed font size II, which had been rarely used in practice; it deleted five character shapes; and it added a new font size IV. ECMA published the second edition of OCR-B in October 1971. In March 1976, ECMA published a third revision of its ECMA-11 specification. It added the symbols § and ¥ to OCR-B; two types of erasure marks (█) for blackening out mis-printed characters were added; and the length of the Vertical bar was changed to match ISO 1073-2. In 1993, Turkey proposed extending ISO 1073-2 to include the Turkish letters Ğğ, İı, and Şş. The request was generalized to extend OCR-B with a number of Latin and Greek letters used in European languages. A revision of the ISO 1073-2:1976 standard was therefore started, producing three successive draft documents. The final draft would have extended OCR-B with 40 Latin and 10 Greek letters; for six Latin letters, the draft gave new alternate shapes. A request to extend OCR-B with Vietnamese accents was rejected. Other than previous versions of the standard, which specified glyph shapes via reference drawings, the new revision would have included the shapes in machine-readable form. However, industry support for testing the new font could not be secured at the time, so the revision effort was halted in 1997. The working group described their findings in a technical report. In June 1998, the European Committee for Standardization published a report for adding the Euro sign to OCR-B. The report proposed both a single-stroked and a double-stroked variant of the Euro sign, leaving the decision to further testing of OCR performance. Testing was difficult: the theoretical design methods used when the OCR-B glyphs were originally developed could no longer be reproduced, and the technological constraints of the 1960s were also not entirely relevant anymore in the OCR environments of the 1990s. A new test method was devised, using present-time OCR technology. The tests found no difference in OCR performance between the two Euro variants, and recommended the adoption of the double-stroked variant as it matches the conventional glyph shape. The project did not have funds to thoroughly test the glyph extensions of the 1993 proposal; initial results were inconclusive. == Availability == Microsoft Office ships a version of Letterpress OCR-B produced by Monotype. It covers Windows-1252. Many vendors, including Adobe, still sell their versions of OCR-A and OCR-B. The TeX typesetting system has a public domain Constant Strokewidth OCR-B font in METAFONT definition form. It was created by Norbert Swartz in 1995 and updated in 2010. It has a setting for square stroke ends. The definition has also been translated to METATYPE1, so the rounded version is available in TrueType and OpenType too. A version of Constant Strokewidth OCR-B by Matthew Anderson has extended character coverage. It is available under CC-BY 4.0.
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AI Image Generators Reviews: What Actually Works in 2026

Trying to pick the best AI image generator? An AI image generator is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI image generator slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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Eugene Charniak

Eugene Charniak (June 2, 1946 – June 13, 2023) was a professor of computer Science and cognitive Science at Brown University. He held an A.B. in Physics from the University of Chicago and a Ph.D. from M.I.T. in Computer Science. His research was in the area of language understanding or technologies which relate to it, such as knowledge representation, reasoning under uncertainty, and learning. Since the early 1990s he was interested in statistical techniques for language understanding. His research in this area included work in the subareas of part-of-speech tagging, probabilistic context-free grammar induction, and, more recently, syntactic disambiguation through word statistics, efficient syntactic parsing, and lexical resource acquisition through statistical means. He was a Fellow of the American Association of Artificial Intelligence and was previously a Councilor of the organization. He was also honored with the 2011 Association for Computational Linguistics Lifetime Achievement Award and awarded the 2011 Calvin & Rose G Hoffman Prize. In 2011, he was named a fellow of the Association for Computational Linguistics. In 2015, he won the Association for the Advancement of Artificial Intelligence (AAAI) Classic Paper Award for a paper (“Statistical Parsing with a Context-Free Grammar and Word Statistics”) that he presented at the Fourteenth National Conference on Artificial Intelligence in 1997. == Books == He published six books: Computational Semantics, (with Yorick Wilks), Amsterdam: North-Holland (1976) Artificial Intelligence Programming (now in a second edition) (with Chris Riesbeck, Drew McDermott, and James Meehan), Hillsdale NJ: Lawrence Erlbaum Associates (1980, 1987) Introduction to Artificial Intelligence (with Drew McDermott), Reading MA: Addison-Wesley (1985) Statistical Language Learning, Cambridge: MIT Press (1993) Introduction to Deep Learning, Cambridge: MIT Press (2019) AI & I: An Intellectual History of Artificial Intelligence, Cambridge: MIT Press (2024)
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Commonsense knowledge (artificial intelligence)

In artificial intelligence research, commonsense knowledge consists of facts about the everyday world, such as "Lemons are sour" or "Cows say moo", that all humans are expected to know. It is currently an unsolved problem in artificial general intelligence. The first AI program to address common sense knowledge was Advice Taker in 1959 by John McCarthy. Commonsense knowledge can underpin a commonsense reasoning process, to attempt inferences such as "You might bake a cake because you want people to eat the cake." A natural language processing process can be attached to the commonsense knowledge base to allow the knowledge base to attempt to answer questions about the world. Common sense knowledge also helps to solve problems in the face of incomplete information. Using widely held beliefs about everyday objects, or common sense knowledge, AI systems make common sense assumptions or default assumptions about the unknown similar to the way people do. In an AI system or in English, this is expressed as "Normally P holds", "Usually P" or "Typically P so Assume P". For example, if we know the fact "Tweety is a bird", because we know the commonly held belief about birds, "typically birds fly," without knowing anything else about Tweety, we may reasonably assume the fact that "Tweety can fly." As more knowledge of the world is discovered or learned over time, the AI system can revise its assumptions about Tweety using a truth maintenance process. If we later learn that "Tweety is a penguin" then truth maintenance revises this assumption because we also know "penguins do not fly". == Commonsense reasoning == Commonsense reasoning simulates the human ability to use commonsense knowledge to make presumptions about the type and essence of ordinary situations they encounter every day, and to change their "minds" should new information come to light. This includes time, missing or incomplete information and cause and effect. The ability to explain cause and effect is an important aspect of explainable AI. Truth maintenance algorithms automatically provide an explanation facility because they create elaborate records of presumptions. Compared with humans, all existing computer programs that attempt human-level AI perform extremely poorly on modern "commonsense reasoning" benchmark tests such as the Winograd Schema Challenge. The problem of attaining human-level competency at "commonsense knowledge" tasks is considered to probably be "AI complete" (that is, solving it would require the ability to synthesize a fully human-level intelligence), although some oppose this notion and believe compassionate intelligence is also required for human-level AI. Common sense reasoning has been applied successfully in more limited domains such as natural language processing and automated diagnosis or analysis. == Commonsense knowledge base construction == Compiling comprehensive knowledge bases of commonsense assertions (CSKBs) is a long-standing challenge in AI research. From early expert-driven efforts like CYC and WordNet, significant advances were achieved via the crowdsourced OpenMind Commonsense project, which led to the crowdsourced ConceptNet KB. Several approaches have attempted to automate CSKB construction, most notably, via text mining (WebChild, Quasimodo, TransOMCS, Ascent), as well as harvesting these directly from pre-trained language models (AutoTOMIC). These resources are significantly larger than ConceptNet, though the automated construction mostly makes them of moderately lower quality. Challenges also remain on the representation of commonsense knowledge: Most CSKB projects follow a triple data model, which is not necessarily best suited for breaking more complex natural language assertions. A notable exception here is GenericsKB, which applies no further normalization to sentences, but retains them in full. == Applications == Around 2013, MIT researchers developed BullySpace, an extension of the commonsense knowledgebase ConceptNet, to catch taunting social media comments. BullySpace included over 200 semantic assertions based around stereotypes, to help the system infer that comments like "Put on a wig and lipstick and be who you really are" are more likely to be an insult if directed at a boy than a girl. ConceptNet has also been used by chatbots and by computers that compose original fiction. At Lawrence Livermore National Laboratory, common sense knowledge was used in an intelligent software agent to detect violations of a comprehensive nuclear test ban treaty. == Data == As an example, as of 2012 ConceptNet includes these 21 language-independent relations: IsA (An "RV" is a "vehicle" | X is an instance of a Y) UsedFor (a "cake tin" is used for "making cakes" | X is used for the purpose Y) HasA (A "rabbit" has a "tail" | X possesses Y element or feature) CapableOf (a "cook" is capable of "making baked goods" | X is capable of doing Y) Desires (a "child" desires "the aroma of baking" | X has a desire for Y) CreatedBy ("cake" is created by a "baker" | X is created by Y) PartOf (a "knife" is be part of a "knife set" | X is a part of Y) Causes ("Heat" causes "cooking"| X is what causes Y) LocatedNear (the "oven" is located near the "refrigerator" | X is located near Y) AtLocation (Somewhere a "Cook" can be at a "restaurant" | X is at the location of Y) DefinedAs (a "Cupcake" is defined as a "cake" that also has the qualities of being "small", "baked within a wrapper", and "containing only one area of frosting or icing" | X is defined as Y that also has the properties A, B & C) SymbolOf (a "heart" is a symbol of "affection" | X is a symbolic representation of Y) ReceivesAction ("cake" can receive the action of being "eaten" | X is capable of receiving action Y) HasPrerequisite ("baking" has the prerequisite of obtaining the "ingredients" | X cannot do Y unless A does B) MotivatedByGoal ("baking" is motivated by the goal of "consumption"/"eating" | X has the motivation of Y goal) CausesDesire ("baking" makesYou want to "follow recipe" | X causes the desire to do Y) MadeOf ("Cake" is made of "flour"/"eggs"/"sugar"/"oil"/etc | X is made of Y) HasFirstSubevent ("baking" has first subevent "make batter" | To do X the first thing that needs to be done is Y) HasSubevent ("eat" has subevent "swallow" | Doing X will lead to Y event following) HasLastSubevent ("sleeping" has last subevent of "waking" | Doing X ends with the event Y) == Commonsense knowledge bases == Cyc Open Mind Common Sense (data source) and ConceptNet (datastore and NLP engine) Evi Graphiq
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AI Humanizers: Free vs Paid (2026)

Trying to pick the best AI humanizer? An AI humanizer is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI humanizer slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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Top 10 AI Video Editors Compared (2026)

Looking for the best AI video editor? An AI video editor is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI video editor slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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