Gerrit ( GERR-it) is a free, web-based team code collaboration tool. Software developers in a team can review each other's modifications on their source code using a Web browser and approve or reject those changes. It integrates closely with Git, a distributed version control system. Gerrit is a fork of Rietveld, a code review tool for Subversion. Both are named after Dutch designer Gerrit Rietveld. == History == Originally written in Python like Rietveld, it is now written in Java (Java EE Servlet) with SQL since version 2 and a custom-made Git-based database (NoteDb) since version 3. In versions 2.0–2.16 Gerrit used Google Web Toolkit for its browser-based front-end. After being developed and used in parallel with GWT for versions 2.14–2.16, a new Polymer web UI replaced the GWT UI in version 3.0.
Showcase Workshop
Showcase Workshop, also referred to as Showcase, is a SaaS company that develops a presentation-building application for business use. Users upload files and images to a web platform which generates presentations viewable on a suite of mobile apps. Showcase was founded in 2011. The company’s headquarters are in Wellington, New Zealand. == History == Showcase Workshop was originally developed in response to dynamically changing content being presented on iPads at the 2012 Olympics. After market-testing a beta version of the core application, Showcase Workshop launched commercially in 2012. In 2014 Showcase partnered with Vodafone Global Enterprise. == Product == Users upload pre-existing PDFs, videos, images and Microsoft Office documents to a secure server, building presentations or ‘showcases’ which can then be downloaded via the mobile apps. The presentations are used for mobile sales enablement, training, or operational/health and safety purposes. == Reception == Reviewers have praised the ease of use of Showcase, calling it a “better alternative to developing a native app” and “intuitive”. Criticisms include the lack of differing templates and a lack of complex customisation controls. Showcase was nominated for a Tabby Award in 2014 and won a Tabby Award in 2015 for its Windows app.
OpenSMILE
openSMILE is source-available software for automatic extraction of features from audio signals and for classification of speech and music signals. "SMILE" stands for "Speech & Music Interpretation by Large-space Extraction". The software is mainly applied in the area of automatic emotion recognition and is widely used in the affective computing research community. The openSMILE project exists since 2008 and is maintained by the German company audEERING GmbH since 2013. openSMILE is provided free of charge for research purposes and personal use under a source-available license. For commercial use of the tool, the company audEERING offers custom license options. == Application Areas == openSMILE is used for academic research as well as for commercial applications in order to automatically analyze speech and music signals in real-time. In contrast to automatic speech recognition which extracts the spoken content out of a speech signal, openSMILE is capable of recognizing the characteristics of a given speech or music segment. Examples for such characteristics encoded in human speech are a speaker's emotion, age, gender, and personality, as well as speaker states like depression, intoxication, or vocal pathological disorders. The software further includes music classification technology for automatic music mood detection and recognition of chorus segments, key, chords, tempo, meter, dance-style, and genre. The openSMILE toolkit serves as benchmark in manifold research competitions such as Interspeech ComParE, AVEC, MediaEval, and EmotiW. == History == The openSMILE project was started in 2008 by Florian Eyben, Martin Wöllmer, and Björn Schuller at the Technical University of Munich within the European Union research project SEMAINE. The goal of the SEMAINE project was to develop a virtual agent with emotional and social intelligence. In this system, openSMILE was applied for real-time analysis of speech and emotion. The final SEMAINE software release is based on openSMILE version 1.0.1. In 2009, the emotion recognition toolkit (openEAR) was published based on openSMILE. "EAR" stands for "Emotion and Affect Recognition". In 2010, openSMILE version 1.0.1 was published and was introduced and awarded at the ACM Multimedia Open-Source Software Challenge. Between 2011 and 2013, the technology of openSMILE was extended and improved by Florian Eyben and Felix Weninger in the context of their doctoral thesis at the Technical University of Munich. The software was also applied for the project ASC-Inclusion, which was funded by the European Union. For this project, the software was extended by Erik Marchi in order to teach emotional expression to autistic children, based on automatic emotion recognition and visualization. In 2013, the company audEERING acquired the rights to the code-base from the Technical University of Munich and version 2.0 was published under a source-available research license. Until 2016, openSMILE was downloaded more than 50,000 times worldwide and has established itself as a standard toolkit for emotion recognition. == Awards == openSMILE was awarded in 2010 in the context of the ACM Multimedia Open Source Competition. The software tool is applied in numerous scientific publications on automatic emotion recognition. openSMILE and its extension openEAR have been cited in more than 1000 scientific publications until today.
Causal AI
Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to explain decision-making and the causes for a decision. Systems based on causal AI, by identifying the underlying web of causality for a behaviour or event, provide insights that solely predictive AI models might fail to extract from historical data. An analysis of causality may be used to supplement human decisions in situations where understanding the causes behind an outcome is necessary, such as quantifying the impact of different interventions, policy decisions or performing scenario planning. A 2024 paper from Google DeepMind demonstrated mathematically that "Any agent capable of adapting to a sufficiently large set of distributional shifts must have learned a causal model". The paper offers the interpretation that learning to generalise beyond the original training set requires learning a causal model, concluding that causal AI is necessary for artificial general intelligence. == History == The concept of causal AI and the limits of machine learning were raised by Judea Pearl, the Turing Award-winning computer scientist and philosopher, in 2018's The Book of Why: The New Science of Cause and Effect. Pearl asserted: “Machines' lack of understanding of causal relations is perhaps the biggest roadblock to giving them human-level intelligence.” In 2020, Columbia University established a Causal AI Lab under Director Elias Bareinboim. Professor Bareinboim's research focuses on causal and counterfactual inference and their applications to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. Technological research and consulting firm Gartner for the first time included causal AI in its 2022 Hype Cycle report, citing it as one of five critical technologies in accelerated AI automation. Causal AI is closely related to but distinct from fields such as causal inference, explainable AI and causal reasoning. While causal inference focuses on estimating cause-effect relationships (often from observational data), causal AI emphasises the integration of those causal models into AI systems for prediction, planning and adaptation.
Computer and information science
Computer and information science (CIS; also known as information and computer science) is a field that emphasizes both computing and informatics, upholding the strong association between the fields of information sciences and computer sciences and treating computers as a tool rather than a field. Information science is one with a long history, unlike the relatively very young field of computer science, and is primarily concerned with gathering, storing, disseminating, sharing and protecting any and all forms of information. It is a broad field, covering a myriad of different areas but is often referenced alongside computer science because of the incredibly useful nature of computers and computer programs in helping those studying and doing research in the field – particularly in helping to analyse data and in spotting patterns too broad for a human to intuitively perceive. While information science is sometimes confused with information theory, the two have vastly different subject matter. Information theory focuses on one particular mathematical concept of information while information science is focused on all aspects of the processes and techniques of information. Computer science, in contrast, is less focused on information and its different states, but more, in a very broad sense, on the use of computers – both in theory and practice – to design and implement algorithms in order to aid the processing of information during the different states described above. It has strong foundations in the field of mathematics, as the very first recognised practitioners of the field were renowned mathematicians such as Alan Turing. Information science and computing began to converge in the 1950s and 1960s, as information scientists started to realize the many ways computers would improve information storage and retrieval. == Terminology == Due to the distinction between computers and computing, some of the research groups refer to computing or datalogy. The French refer to computer science as the term informatique. The term information and communications technology (ICT), refers to how humans communicate with using machines and computers, making a distinction from information and computer science, which is how computers use and gain information. Informatics is also distinct from computer science, which encompasses the study of logic and low-level computing issues. == Education == Universities may confer degrees with a major in computer and information science, not to be confused with a more specific Bachelor of Computer Science or respective graduate computer science degrees. The QS World University Rankings is one of the most widely recognised and distinguished university comparisons. They ranked the top 10 universities for computer science and information systems in 2015. They are: Massachusetts Institute of Technology (MIT) Stanford University University of Oxford Carnegie Mellon University Harvard University University of California, Berkeley (UCB) University of Cambridge The Hong Kong University of Science and Technology Swiss Federal Institute of Technology (ETH Zurich) Princeton University A Computer Information Science degree gives students both network and computing knowledge which is needed to design, develop, and assist information systems which helps to solve business problems and to support business problems and to support business operations and decision making at a managerial level also. == Areas of information and computer science == Due to the nature of this field, many topics are also shared with computer science and information systems. The discipline of Information and Computer Science spans a vast range of areas from basic computer science theory (algorithms and computational logic) to in depth analysis of data manipulation and use within technology. === Programming theory === The process of taking a given algorithm and encoding it into a language that can be understood and executed by a computer. There are many different types of programming languages and various different types of computers, however, they all have the same goal: to turn algorithms into machine code. Popular programming languages used within the academic study of CIS include, but are not limited to: Java, Python, C#, C++, Perl, Ruby, Pascal, Swift, Visual Basic. === Information and information systems === The academic study of software and hardware systems that process large quantities and data, support large scale data management and how data can be used. This is where the field is unique from the standard study of computer science. The area of information systems focuses on the networks of hardware and software that are required to process, manipulate and distribute such data. === Computer systems and organisations === The process of analysing computer architecture and various logic circuits. This involves looking at low level computer processes at bit level computation. This is an in-depth look into the hardware processing of a computational system, involving looking at the basic structure of a computer and designing such systems. This can also involve evaluating complex circuit diagrams, and being able to construct these to solve a main problem. The main purpose behind this area of study is to achieve an understanding of how computers function on a basic level, often through tracing machine operations. === Machines, languages, and computation === This is the study into fundamental computer algorithms, which are the basis to computer programs. Without algorithms, no computer programs would exist. This also involves the process of looking into various mathematical functions behind computational algorithms, basic theory and functional (low level) programming. In an academic setting, this area would introduce the fundamental mathematical theorems and functions behind theoretical computer science which are the building blocks for other areas in the field. Complex topics such as; proofs, algebraic functions and sets will be introduced during studies of CIS. == Developments == Information and computer science is a field that is rapidly developing with job prospects for students being extremely promising with 75.7% of graduates gaining employment. Also the IT industry employs one in twenty of the workforce with it predicted to increase nearly five times faster than the average of the UK and between 2012 and 2017 more than half a million people will be needed within the industry and the fact that nine out of ten tech firms are suffering from candidate shortages which is having a negative impact on their business as it delays the creation and development of new products, and it's predicted in the US that in the next decade there will be more than one million jobs in the technology sector than computer science graduates to fill them. Because of this programming is now being taught at an earlier age with an aim to interest students from a young age into computer and information science hopefully leading more children to study this at a higher level. For example, children in England will now be exposed to computer programming at the age of 5 due to an updated national curriculum. == Employment == Due to the wide variety of jobs that now involve computer and information science related tasks, it is difficult to provide a comprehensive list of possible jobs in this area, but some of the key areas are artificial intelligence, software engineering and computer networking and communication. Work in this area also tends to require sufficient understanding of mathematics and science. Moreover, jobs that having a CIS degree can lead to, include: systems analyst, network administrator, system architect, information systems developer, web programmer, or software developer. The earning potential for CIS graduates is quite promising. A 2013 survey from the National Association of Colleges and Employers (NACE) found that the average starting salary for graduates who earned a degree in a computer related field was $59,977, up 4.3% from the prior year. This is higher than other popular degrees such as business ($54,234), education ($40,480) and math and sciences ($42,724). Furthermore, Payscale ranked 129 college degrees based on their graduates earning potential with engineering, math, science, and technology fields dominating the ranking. With eight computer related degrees appearing among the top 30. With the lowest starting salary for these jobs being $49,900. A Rasmussen College article describes various jobs CIS graduates may obtain with software applications developers at the top making a median income of $98,260. According to the National Careers Service an Information Scientist can expect to earn £24,000+ per year as a starting salary.
Saliency map
In computer vision, a saliency map is an image that highlights either the region on which people's eyes focus first or the most relevant regions for machine learning models. The goal of a saliency map is to reflect the degree of importance of a pixel to the human visual system or an otherwise opaque ML model. For example, in this image, a person first looks at the fort and light clouds, so they should be highlighted on the saliency map. == Application == === Overview === Saliency maps have applications in a variety of different problems. Some general applications: ==== Human eye ==== Image and video compression: The human eye focuses only on a small region of interest in the frame. Therefore, it is not necessary to compress the entire frame with uniform quality. According to the authors, using a salience map reduces the final size of the video with the same visual perception. Image and video quality assessment: The main task for an image or video quality metric is a high correlation with user opinions. Differences in salient regions are given more importance and thus contribute more to the quality score. Image retargeting: It aims at resizing an image by expanding or shrinking the noninformative regions. Therefore, retargeting algorithms rely on the availability of saliency maps that accurately estimate all the salient image details. Object detection and recognition: Instead of applying a computationally complex algorithm to the whole image, we can use it to the most salient regions of an image most likely to contain an object. the primary visual cortex (V1) appears to be responsible for the saliency map, according to the V1 Saliency Hypothesis. ==== Explainable artificial intelligence ==== Saliency maps are a prominent tool in explainable artificial intelligence, providing visual explanations of the decision-making process of machine learning models, particularly deep neural networks. These maps highlight the regions in input data that are most influential on the model's output, effectively indicating where the model is "looking" when making a prediction. In image classification tasks, for example, saliency maps can identify pixels or regions that contribute most to a specific class decision. Developed for convolutional neural networks, saliency mapping techniques range from simply taking the gradient of the class score with respect to the input data to more complex algorithms, such as integrated gradients and class activation mapping. In transformer architecture, attention mechanisms led to analogous saliency maps, such as attention maps, attention rollouts, and class-discriminative attention maps. === Saliency as a segmentation problem === Saliency estimation may be viewed as an instance of image segmentation. In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. == Algorithms == === Overview === There are three forms of classic saliency estimation algorithms implemented in OpenCV: Static saliency: Relies on image features and statistics to localize the regions of interest of an image. Motion saliency: Relies on motion in a video, detected by optical flow. Objects that move are considered salient. Objectness: Objectness reflects how likely an image window covers an object. These algorithms generate a set of bounding boxes of where an object may lie in an image. In addition to classic approaches, neural-network-based are also popular. There are examples of neural networks for motion saliency estimation: TASED-Net: It consists of two building blocks. First, the encoder network extracts low-resolution spatiotemporal features, and then the following prediction network decodes the spatially encoded features while aggregating all the temporal information. STRA-Net: It emphasizes two essential issues. First, spatiotemporal features integrated via appearance and optical flow coupling, and then multi-scale saliency learned via attention mechanism. STAViS: It combines spatiotemporal visual and auditory information. This approach employs a single network that learns to localize sound sources and to fuse the two saliencies to obtain a final saliency map. There's a new static saliency in the literature with name visual distortion sensitivity. It is based on the idea that the true edges, i.e. object contours, are more salient than the other complex textured regions. It detects edges in a different way from the classic edge detection algorithms. It uses a fairly small threshold for the gradient magnitudes to consider the mere presence of the gradients. So, it obtains 4 binary maps for vertical, horizontal and two diagonal directions. The morphological closing and opening are applied to the binary images to close the small gaps. To clear the blob-like shapes, it utilizes the distance transform. After all, the connected pixel groups are individual edges (or contours). A threshold of size of connected pixel set is used to determine whether an image block contains a perceivable edge (salient region) or not. === Example implementation === First, we should calculate the distance of each pixel to the rest of pixels in the same frame: S A L S ( I k ) = ∑ i = 1 N | I k − I i | {\displaystyle \mathrm {SALS} (I_{k})=\sum _{i=1}^{N}|I_{k}-I_{i}|} I i {\displaystyle I_{i}} is the value of pixel i {\displaystyle i} , in the range of [0,255]. The following equation is the expanded form of this equation. SALS(Ik) = |Ik - I1| + |Ik - I2| + ... + |Ik - IN| Where N is the total number of pixels in the current frame. Then we can further restructure our formula. We put the value that has same I together. SALS(Ik) = Σ Fn × |Ik - In| Where Fn is the frequency of In. And the value of n belongs to [0,255]. The frequencies is expressed in the form of histogram, and the computational time of histogram is O ( N ) {\displaystyle O(N)} time complexity. ==== Time complexity ==== This saliency map algorithm has O ( N ) {\displaystyle O(N)} time complexity. Since the computational time of histogram is O ( N ) {\displaystyle O(N)} time complexity which N is the number of pixel's number of a frame. Besides, the minus part and multiply part of this equation need 256 times operation. Consequently, the time complexity of this algorithm is O ( N + 256 ) {\displaystyle O(N+256)} which equals to O ( N ) {\displaystyle O(N)} . ==== Pseudocode ==== All of the following code is pseudo MATLAB code. First, read data from video sequences. After we read data, we do superpixel process to each frame. Spnum1 and Spnum2 represent the pixel number of current frame and previous pixel. Then we calculate the color distance of each pixel, this process we call it contract function. After this two process, we will get a saliency map, and then store all of these maps into a new FileFolder. ==== Difference in algorithms ==== The major difference between function one and two is the difference of contract function. If spnum1 and spnum2 both represent the current frame's pixel number, then this contract function is for the first saliency function. If spnum1 is the current frame's pixel number and spnum2 represent the previous frame's pixel number, then this contract function is for second saliency function. If we use the second contract function which using the pixel of the same frame to get center distance to get a saliency map, then we apply this saliency function to each frame and use current frame's saliency map minus previous frame's saliency map to get a new image which is the new saliency result of the third saliency function. == Datasets == The saliency dataset usually contains human eye movements on some image sequences. It is valuable for new saliency algorithm creation or benchmarking the existing one. The most valuable dataset parameters are spatial resolution, size, and eye-tracking equipment. Here is part of the large datasets table from MIT/Tübingen Saliency Benchmark datasets, for example. To collect a saliency dataset, image or video sequences and eye-tracking equipment must be prepared, and observers must be invited. Observers must have normal or corrected to normal vision and must be at the same distance from the screen. At the beginning of each recording session, the eye-tracker recalibrates. To do this, the observer fixates their gaze on the screen center. The session is then started, and saliency data are collected by showing sequences and recording eye gazes. The eye-tracking device is a high-speed camera, capable of recording eye movements at least 250 fr
Software intelligence
Software intelligence is insight into the inner workings and structural condition of software assets produced by software designed to analyze database structure, software framework and source code to better understand and control complex software systems in information technology environments. Similarly to business intelligence (BI), software intelligence is produced by a set of software tools and techniques for the mining of data and the software's inner-structure. Results are automatically produced and feed a knowledge base containing technical documentation and blueprints of the innerworking of applications, and make it available to all to be used by business and software stakeholders to make informed decisions, measure the efficiency of software development organizations, communicate about the software health, prevent software catastrophes. == History == Software intelligence has been used by Kirk Paul Lafler, an American engineer, entrepreneur, and consultant, and founder of Software Intelligence Corporation in 1979. At that time, it was mainly related to SAS activities, in which he has been an expert since 1979. In the early 1980s, Victor R. Basili participated in different papers detailing a methodology for collecting valid software engineering data relating to software engineering, evaluation of software development, and variations. In 2004, different software vendors in software analysis started using the terms as part of their product naming and marketing strategy. Then in 2010, Ahmed E. Hassan and Tao Xie defined software intelligence as a "practice offering software practitioners up-to-date and pertinent information to support their daily decision-making processes and Software Intelligence should support decision-making processes throughout the lifetime of a software system". They go on by defining software intelligence as a "strong impact on modern software practice" for the upcoming decades. == Capabilities == Because of the complexity and wide range of components and subjects implied in software, software intelligence is derived from different aspects of software: Software composition is the construction of software application components. Components result from software coding, as well as the integration of the source code from external components: Open source, 3rd party components, or frameworks. Other components can be integrated using application programming interface call to libraries or services. Software architecture refers to the structure and organization of elements of a system, relations, and properties among them. Software flaws designate problems that can cause security, stability, resiliency, and unexpected results. There is no standard definition of software flaws but the most accepted is from The MITRE Corporation where common flaws are cataloged as Common Weakness Enumeration. Software grades assess attributes of the software. Historically, the classification and terminology of attributes have been derived from the ISO 9126-3 and the subsequent ISO 25000:2005 quality model. Software economics refers to the resource evaluation of software in the past, present, or future to make decisions and to govern. == Components == The capabilities of software intelligence platforms include an increasing number of components: Code analyzer to serve as an information basis for other software intelligence components identifying objects created by the programming language, external objects from Open source, third parties objects, frameworks, API, or services Graphical visualization and blueprinting of the inner structure of the software product or application considered including dependencies, from data acquisition (automated and real-time data capture, end-user entries) up to data storage, the different layers within the software, and the coupling between all elements. Navigation capabilities within components and impact analysis features List of flaws, architectural and coding violations, against standardized best practices, cloud blocker preventing migration to a Cloud environment, and rogue data-call entailing the security and integrity of software Grades or scores of the structural and software quality aligned with industry-standard like OMG, CISQ or SEI assessing the reliability, security, efficiency, maintainability, and scalability to cloud or other systems. Metrics quantifying and estimating software economics including work effort, sizing, and technical debt Industry references and benchmarking allowing comparisons between outputs of analysis and industry standards == User aspect == Some considerations must be made in order to successfully integrate the usage of software Intelligence systems in a company. Ultimately the software intelligence system must be accepted and utilized by the users in order for it to add value to the organization. If the system does not add value to the users' mission, they simply don't use it as stated by M. Storey in 2003. At the code level and system representation, software intelligence systems must provide a different level of abstractions: an abstract view for designing, explaining and documenting and a detailed view for understanding and analyzing the software system. At the governance level, the user acceptance for software intelligence covers different areas related to the inner functioning of the system as well as the output of the system. It encompasses these requirements: Comprehensive: missing information may lead to a wrong or inappropriate decision, as well as it is a factor influencing the user acceptance of a system. Accurate: accuracy depends on how the data is collected to ensure fair and indisputable opinion and judgment. Precise: precision is usually judged by comparing several measurements from the same or different sources. Scalable: lack of scalability in the software industry is a critical factor leading to failure. Credible: outputs must be trusted and believed. Deploy-able and usable. == Applications == Software intelligence has many applications in all businesses relating to the software environment, whether it is software for professionals, individuals, or embedded software. Depending on the association and the usage of the components, applications will relate to: Change and modernization: uniform documentation and blueprinting on all inner components, external code integrated, or call to internal or external components of the software Resiliency and security: measuring against industry standards to diagnose structural flaws in an IT environment. Compliance validation regarding security, specific regulations or technical matters. Decisions making and governance: Providing analytics about the software itself or stakeholders involved in the development of the software, e.g. productivity measurement to inform business and IT leaders about progress towards business goals. Assessment and Benchmarking to help business and IT leaders to make informed, fact-based decision about software. == Marketplace == Software intelligence is a high-level discipline and has been gradually growing covering the applications listed above. There are several markets driving the need for it: Application Portfolio Analysis (APA) aiming at improving the enterprise performance. Software Assessment for producing the software KPI and improving quality and productivity. Software security and resiliency measures and validation. Software evolution or legacy modernization, for which blueprinting the software systems are needed nor tools improving and facilitating modifications.