Digital transaction management (DTM) is a category of cloud services designed to digitally manage document-based transactions. DTM removes the friction inherent in transactions that involve people, documents, and data to create faster, easier, more convenient, and secure processes. DTM goes beyond content and document management to include e-signatures, authentication and non-repudiation; enabling co-browsing between the customer and the business; document transfer and certification; secure archiving that goes beyond records management; and a variety of meta-processes around managing electronic transactions and the documents associated with them. DTM standards are proposed and managed by the xDTM Standard Association Aragon Research has estimated that "by YE 2016, 70% of large enterprises will have a DTM initiative underway or fully implemented."
Mobile cloud storage
Mobile cloud storage is a form of cloud storage that is accessible on mobile devices such as laptops, tablets, and smartphones. Mobile cloud storage providers offer services that allow the user to create and organize files, folders, music, and photos, similar to other cloud computing models. Services are used by both individuals and companies. Most cloud file storage providers offer limited free use but charge for additional storage once the free limit is exceeded. These costs are usually charged as a monthly subscription rate and have different rates depending on the amount of storage desired. In 2018, cloud services revenue was about $182.4 billion and in 2022 it is projected to grow to $331.2 billion. The cloud storage industry was projected to grow 17.2 percent in 2019 (Costello, 2019). == History == The concept of cloud computing trace back to 1960s, when the groundwork for modern internet and network technologies was being laid (Human for humans, 2024). One of the pivotal figures in this early period was J.C.R. Licklider, a visionary computer scientist who worked on ARPANET, the precursor to the internet. Licklider's ideas set the stage for the development of distributed computing systems, which are fundamental to cloud computing. Moving into the 1990s, AT&T introduced PersonaLink Services, a more advanced online platform offering electronic mail and online storage. Major turning point in 2006 The launch of Amazon Web Services (AWS) in 2006 marked a major turning point. AWS introduced Amazon S3 (Simple Storage Service), which allowed businesses and developers to store and retrieve any amount of data, at any time, from anywhere on the web. This development was revolutionary, providing scalable, reliable, and low-cost data storage infrastructure that transformed how organizations managed their data. == Applications == Some mobile device manufacturers include mobile cloud storage apps with their product. These apps facilitate synchronization of user files across multiple platforms. Part of the process for setting up new mobile devices frequently includes configuring a cloud storage service to Backup the device's files and information. Apple iOS devices come pre-loaded and configured to use Apple's mobile cloud storage service iCloud. Google offers a similar feature with the Android operating system by backing up the device using a Google Drive account. The Samsung Galaxy smartphone has partnered with Dropbox, while Microsoft similarly offers Microsoft OneDrive. Some mobile cloud storage apps are platform-independent. For example, Nasuni's Mobile Access app is available on any Android or iOS device. Most companies offering Cloud Storage have secure website to access files allowing use on any device that can browse the Internet.
Machine translation of sign languages
The machine translation of sign languages has been possible, albeit in a limited fashion, since 1977. When a research project successfully matched English letters from a keyboard to ASL manual alphabet letters which were simulated on a robotic hand. These technologies translate signed languages into written or spoken language, and written or spoken language to sign language, without the use of a human interpreter. Sign languages possess different phonological features than spoken languages, which has created obstacles for developers. Developers use computer vision and machine learning to recognize specific phonological parameters and epentheses unique to sign languages, and speech recognition and natural language processing allow interactive communication between hearing and deaf people. == Limitations == Sign language translation technologies are limited in the same way as spoken language translation. None can translate with 100% accuracy. In fact, sign language translation technologies are far behind their spoken language counterparts. This is, in no trivial way, due to the fact that signed languages have multiple articulators. Where spoken languages are articulated through the vocal tract, signed languages are articulated through the hands, arms, head, shoulders, torso, and parts of the face. This multi-channel articulation makes translating sign languages very difficult. An additional challenge for sign language MT is the fact that there is no formal written format for signed languages. There are notations systems but no writing system has been adopted widely enough, by the international Deaf community, that it could be considered the 'written form' of a given sign language. Sign Languages then are recorded in various video formats. There is no gold standard parallel corpus that is large enough for SMT, for example. == History == The history of automatic sign language translation started with the development of hardware such as finger-spelling robotic hands. In 1977, a finger-spelling hand project called RALPH (short for "Robotic Alphabet") created a robotic hand that can translate alphabets into finger-spellings. Later, the use of gloves with motion sensors became the mainstream, and some projects such as the CyberGlove and VPL Data Glove were born. The wearable hardware made it possible to capture the signers' hand shapes and movements with the help of the computer software. However, with the development of computer vision, wearable devices were replaced by cameras due to their efficiency and fewer physical restrictions on signers. To process the data collected through the devices, researchers implemented neural networks such as the Stuttgart Neural Network Simulator for pattern recognition in projects such as the CyberGlove. Researchers also use many other approaches for sign recognition. For example, Hidden Markov Models are used to analyze data statistically, and GRASP and other machine learning programs use training sets to improve the accuracy of sign recognition. Fusion of non-wearable technologies such as cameras and Leap Motion controllers have shown to increase the ability of automatic sign language recognition and translation software. == Technologies == === VISICAST === http://www.visicast.cmp.uea.ac.uk/Visicast_index.html === eSIGN project === http://www.visicast.cmp.uea.ac.uk/eSIGN/index.html === The American Sign Language Avatar Project at DePaul University === http://asl.cs.depaul.edu/ === Spanish to LSE === López-Ludeña, Verónica; San-Segundo, Rubén; González, Carlos; López, Juan Carlos; Pardo, José M. (2012), Methodology for developing a Speech into Sign Language Translation System in a New Semantic Domain (PDF), CiteSeerX 10.1.1.1065.5265, S2CID 2724186 === SignAloud === SignAloud is a technology that incorporates a pair of gloves made by a group of students at University of Washington that transliterate American Sign Language (ASL) into English. In February 2015 Thomas Pryor, a hearing student from the University of Washington, created the first prototype for this device at Hack Arizona, a hackathon at the University of Arizona. Pryor continued to develop the invention and in October 2015, Pryor brought Navid Azodi onto the SignAloud project for marketing and help with public relations. Azodi has a rich background and involvement in business administration, while Pryor has a wealth of experience in engineering. In May 2016, the duo told NPR that they are working more closely with people who use ASL so that they can better understand their audience and tailor their product to the needs of these people rather than the assumed needs. However, no further versions have been released since then. The invention was one of seven to win the Lemelson-MIT Student Prize, which seeks to award and applaud young inventors. Their invention fell under the "Use it!" category of the award which includes technological advances to existing products. They were awarded $10,000. The gloves have sensors that track the users hand movements and then send the data to a computer system via Bluetooth. The computer system analyzes the data and matches it to English words, which are then spoken aloud by a digital voice. The gloves do not have capability for written English input to glove movement output or the ability to hear language and then sign it to a deaf person, which means they do not provide reciprocal communication. The device also does not incorporate facial expressions and other nonmanual markers of sign languages, which may alter the actual interpretation from ASL. === ProDeaf === ProDeaf (WebLibras) is a computer software that can translate both text and voice into Portuguese Libras (Portuguese Sign Language) "with the goal of improving communication between the deaf and hearing." There is currently a beta edition in production for American Sign Language as well. The original team began the project in 2010 with a combination of experts including linguists, designers, programmers, and translators, both hearing and deaf. The team originated at Federal University of Pernambuco (UFPE) from a group of students involved in a computer science project. The group had a deaf team member who had difficulty communicating with the rest of the group. In order to complete the project and help the teammate communicate, the group created Proativa Soluções and have been moving forward ever since. The current beta version in American Sign Language is very limited. For example, there is a dictionary section and the only word under the letter 'j' is 'jump'. If the device has not been programmed with the word, then the digital avatar must fingerspell the word. The last update of the app was in June 2016, but ProDeaf has been featured in over 400 stories across the country's most popular media outlets. The application cannot read sign language and turn it into word or text, so it only serves as a one-way communication. Additionally, the user cannot sign to the app and receive an English translation in any form, as English is still in the beta edition. === Kinect Sign Language Translator === Since 2012, researchers from the Chinese Academy of Sciences and specialists of deaf education from Beijing Union University in China have been collaborating with Microsoft Research Asian team to create Kinect Sign Language Translator. The translator consists of two modes: translator mode and communication mode. The translator mode is capable of translating single words from sign into written words and vice versa. The communication mode can translate full sentences and the conversation can be automatically translated with the use of the 3D avatar. The translator mode can also detect the postures and hand shapes of a signer as well as the movement trajectory using the technologies of machine learning, pattern recognition, and computer vision. The device also allows for reciprocal communication because the speech recognition technology allows the spoken language to be translated into the sign language and the 3D modeling avatar can sign back to the deaf people. The original project was started in China based on translating Chinese Sign Language. In 2013, the project was presented at Microsoft Research Faculty Summit and Microsoft company meeting. Currently, this project is also being worked by researchers in the United States to implement American Sign Language translation. As of now, the device is still a prototype, and the accuracy of translation in the communication mode is still not perfect. === SignAll === SignAll is an automatic sign language translation system provided by Dolphio Technologies in Hungary. The team is "pioneering the first automated sign language translation solution, based on computer vision and natural language processing (NLP), to enable everyday communication between individuals with hearing who use spoken English and deaf or hard of hearing individuals who use ASL." The system of SignAll uses Kinect from Microsoft and other web camera
Tamara Broderick
Tamara Ann Broderick is an American computer scientist at the Massachusetts Institute of Technology. She works on machine learning and Bayesian inference. == Education and early career == Broderick is from Parma Heights, Ohio. She attended Laurel School and graduated in 2003. Whilst at high school she took part in the inaugural Massachusetts Institute of Technology Women's Technology Program. She studied mathematics at Princeton University, earning a bachelor's degree in 2007. She was a Marshall scholar, allowing her to pursue graduate research at the University of Cambridge. She was a runner-up in the Association for Women in Mathematics Alice T. Shafer Prize for Excellence in Mathematics. She was co-president of the Princeton Math Club and organised a competition for high school maths teams. She won the Phi Beta Kappa Prize for the highest academic average at Princeton University. During her undergraduate degree, Broderick worked on dark matter haloes with Rachel Mandelbaum. Broderick moved to the United Kingdom for her graduate studies, earning a Master of Advanced Studies for completing Part III of the Mathematical Tripos at the University of Cambridge in 2009. Her Master's thesis looked at the Nomon selection method, improving the efficiency of communications. She returned to America in 2009, joining University of California, Berkeley for her Master's and PhD. Her graduate research was supported by the Berkeley Fellowship and a National Science Foundation Fellowship. Her PhD thesis Clusters and features from combinatorial stochastic processes looked at clustering and speeding up the analysis of large, streaming data sets. In 2013 she was selected for the Berkeley EECS Rising Stars conference. == Research and career == Broderick joined Massachusetts Institute of Technology as an assistant professor in 2015. She is interested in Bayesian statistics and graphical models. She was the recipient of a Google Faculty Research Grant and International Society for Bayesian Analysis Lifetime Members Junior Researcher Award. She was awarded an Army Research Office young investigator program award to investigate machine-learning to quantify uncertainty in data analysis. Broderick is also Alfred P. Sloan Foundation scholar. === Academic service === In 2018, Broderick spoke at the Harvard University Institute for Applied Computational Science Women in Data Science conference. She spoke about Bayesian inference at the 2018 International Conference on Machine Learning. She led a three-day Masterclass on machine learning at University College London in June 2018. Broderick is a scientific advisor for AI.Reverie and WiML (Women in Machine Learning). She has developed a high-school level introduction to machine learning with the Women's Technology Program (WTP). Software she has developed is available on her website. === Awards and honors === Broderick was awarded the Evelyn Fix Memorial Medal and Citation and the International Society for Bayesian Analysis Savage Award for her doctoral thesis. She was awarded a National Science Foundation CAREER Award to scale her machine learning techniques. She was a 2021 Leadership Academy winner of the Committee of Presidents of Statistical Societies.
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Spike-and-slab regression
Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients is chosen such that only a subset of the possible regressors is retained. The technique is particularly useful when the number of possible predictors is larger than the number of observations. The idea of the spike-and-slab model was originally proposed by Mitchell & Beauchamp (1988). The approach was further significantly developed by Madigan & Raftery (1994) and George & McCulloch (1997). A recent and important contribution to this literature is Ishwaran & Rao (2005). == Model description == Suppose we have P possible predictors in some model. Vector γ has a length equal to P and consists of zeros and ones. This vector indicates whether a particular variable is included in the regression or not. If no specific prior information on initial inclusion probabilities of particular variables is available, a Bernoulli prior distribution is a common default choice. Conditional on a predictor being in the regression, we identify a prior distribution for the model coefficient, which corresponds to that variable (β). A common choice on that step is to use a normal prior with a mean equal to zero and a large variance calculated based on ( X T X ) − 1 {\displaystyle (X^{T}X)^{-1}} (where X {\displaystyle X} is a design matrix of explanatory variables of the model). A draw of γ from its prior distribution is a list of the variables included in the regression. Conditional on this set of selected variables, we take a draw from the prior distribution of the regression coefficients (if γi = 1 then βi ≠ 0 and if γi = 0 then βi = 0). βγ denotes the subset of β for which γi = 1. In the next step, we calculate a posterior probability for both inclusion and coefficients by applying a standard statistical procedure. All steps of the described algorithm are repeated thousands of times using the Markov chain Monte Carlo (MCMC) technique. As a result, we obtain a posterior distribution of γ (variable inclusion in the model), β (regression coefficient values) and the corresponding prediction of y. The model got its name (spike-and-slab) due to the shape of the two prior distributions. The "spike" is the probability of a particular coefficient in the model to be zero. The "slab" is the prior distribution for the regression coefficient values. An advantage of Bayesian variable selection techniques is that they are able to make use of prior knowledge about the model. In the absence of such knowledge, some reasonable default values can be used; to quote Scott and Varian (2013): "For the analyst who prefers simplicity at the cost of some reasonable assumptions, useful prior information can be reduced to an expected model size, an expected R2, and a sample size ν determining the weight given to the guess at R2." Some researchers suggest the following default values: R2 = 0.5, ν = 0.01, and π = 0.5 (parameter of a prior Bernoulli distribution).
Node2vec
node2vec is an algorithm to generate vector representations of nodes on a graph. The node2vec framework learns low-dimensional representations for nodes in a graph through the use of random walks through a graph starting at a target node. It is useful for a variety of machine learning applications. node2vec follows the intuition that random walks through a graph can be treated like sentences in a corpus. Each node in a graph is treated like an individual word, and a random walk is treated as a sentence. By feeding these "sentences" into a skip-gram, or by using the continuous bag of words model, paths found by random walks can be treated as sentences, and traditional data-mining techniques for documents can be used. The algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and argues that the added flexibility in exploring neighborhoods is the key to learning richer representations of nodes in graphs. The algorithm is considered one of the best graph classifiers.