Semantic analysis is a method for eliciting and representing knowledge about organisations. Initially the problem must be defined by domain experts and passed to the project analyst(s). The next step is the generation of candidate affordances. This step will generate a list of semantic units that may be included in the schema. The candidate grouping follows where some of the semantic units that will appear in the schema are placed in simple groups. Finally the groups will be integrated together into an ontology chart. Semantic analysis always starts from the problem definition which if not clear, require the analyst to employ relevant literature, interviews with the stakeholders and other techniques towards collecting supplementary information. All assumptions made must be genuine and not limiting the system.
Latent semantic mapping
Latent semantic mapping (LSM) is a data-driven framework to model globally meaningful relationships implicit in large volumes of (often textual) data. It is a generalization of latent semantic analysis. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. LSM was derived from earlier work on latent semantic analysis. There are 3 main characteristics of latent semantic analysis: Discrete entities, usually in the form of words and documents, are mapped onto continuous vectors, the mapping involves a form of global correlation pattern, and dimensionality reduction is an important aspect of the analysis process. These constitute generic properties, and have been identified as potentially useful in a variety of different contexts. This usefulness has encouraged great interest in LSM. The intended product of latent semantic mapping, is a data-driven framework for modeling relationships in large volumes of data. Mac OS X v10.5 and later includes a framework implementing latent semantic mapping.
Representational harm
Systems cause representational harm when they misrepresent a group of people in a negative manner. Representational harms include perpetuating harmful stereotypes about or minimizing the existence of a social group, such as a racial, ethnic, gender, or religious group. Machine learning algorithms often commit representational harm when they learn patterns from data that have algorithmic bias, and this has been shown to be the case with large language models. While preventing representational harm in models is essential to prevent harmful biases, researchers often lack precise definitions of representational harm and conflate it with allocative harm, an unequal distribution of resources among social groups, which is more widely studied and easier to measure. However, recognition of representational harms is growing and preventing them has become an active research area. Researchers have recently developed methods to effectively quantify representational harm in algorithms, making progress on preventing this harm in the future. == Types == Three prominent types of representational harm include stereotyping, denigration, and misrecognition. These subcategories present many dangers to individuals and groups. Stereotypes are oversimplified and usually undesirable representations of a specific group of people, usually by race and gender. This often leads to the denial of educational, employment, housing, and other opportunities. For example, the model minority stereotype of Asian Americans as highly intelligent and good at mathematics can be damaging professionally and academically. Representational harm happens when the representation of details teams improves damaging stereotypes, developing social exclusion and prejudice. This experience is particularly noticeable in the depiction of marginalised groups, containing people of color, women, LGBTQ+ people, and people with handicaps. Media depictions of these groups generally stop working to catch their array and intricacy. Instead, they are typically reduced to one-dimensional caricatures, which ultimately continue social prejudices. These organised depictions contribute to the help of hazardous stereotypes and the marginalisation of these locations. Denigration is the action of unfairly criticizing individuals. This frequently happens when the demeaning of social groups occurs. For example, when searching for "Black-sounding" names versus "white-sounding" ones, some retrieval systems bolster the false perception of criminality by displaying ads for bail-bonding businesses. A system may shift the representation of a group to be of lower social status, often resulting in a disregard from society. Research shows that hazardous depictions in the media can have substantial emotional and social impacts on both individuals and areas. Lawrence Bobo examined the issue of Ethnic stereotype in film, tv, and marketing. African Americans are commonly received duties specified by features such as "violent tendencies," "laziness," or being "merely for contentment features." While these representations might appear varied externally, they stay to boost underlying frameworks of white prominence and racial inequality. As a circumstances, Black individuals are frequently represented as law offenders or in secondary roles, which adds to the support of Ethnic stereotype and Institutional racism. Misrecognition, or incorrect recognition, can display in many forms, including, but not limited to, erasing and alienating social groups, and denying people the right to self-identify. Erasing and alienating social groups involves the unequal visibility of certain social groups; specifically, systematic ineligibility in algorithmic systems perpetuates inequality by contributing to the underrepresentation of social groups. Not allowing people to self-identify is closely related as people's identities can be 'erased' or 'alienated' in these algorithms. Misrecognition causes more than surface-level harm to individuals: psychological harm, social isolation, and emotional insecurity can emerge from this subcategory of representational harm. == Quantification == As the dangers of representational harm have become better understood, some researchers have developed methods to measure representational harm in algorithms. Modeling stereotyping is one way to identify representational harm. Representational stereotyping can be quantified by comparing the predicted outcomes for one social group with the ground-truth outcomes for that group observed in real data. For example, if individuals from group A achieve an outcome with a probability of 60%, stereotyping would be observed if it predicted individuals to achieve that outcome with a probability greater than 60%. The group modeled stereotyping in the context of classification, regression, and clustering problems, and developed a set of rules to quantitatively determine if the model predictions exhibit stereotyping in each of these cases. Other attempts to measure representational harms have focused on applications of algorithms in specific domains such as image captioning, the act of an algorithm generating a short description of an image. In a study on image captioning, researchers measured five types of representational harm. To quantify stereotyping, they measured the number of incorrect words included in the model-generated image caption when compared to a gold-standard caption. They manually reviewed each of the incorrectly included words, determining whether the incorrect word reflected a stereotype associated with the image or whether it was an unrelated error, which allowed them to have a proxy measure of the amount of stereotyping occurring in this caption generation. These researchers also attempted to measure demeaning representational harm. To measure this, they analyzed the frequency with which humans in the image were mentioned in the generated caption. It was hypothesized that if the individuals were not mentioned in the caption, then this was a form of dehumanization. == Examples == One of the most notorious examples of representational harm was committed by Google in 2015 when an algorithm in Google Photos classified Black people as gorillas. Developers at Google said that the problem was caused because there were not enough faces of Black people in the training dataset for the algorithm to learn the difference between Black people and gorillas. Google issued an apology and fixed the issue by blocking its algorithms from classifying anything as a primate. In 2023, Google's photos algorithm was still blocked from identifying gorillas in photos. Another prevalent example of representational harm is the possibility of stereotypes being encoded in word embeddings, which are trained using a wide range of text. These word embeddings are the representation of a word as an array of numbers in vector space, which allows an individual to calculate the relationships and similarities between words. However, recent studies have shown that these word embeddings may commonly encode harmful stereotypes, such as the common example that the phrase "computer programmer" is oftentimes more closely related to "man" than it is to "women" in vector space. This could be interpreted as a misrepresentation of computer programming as a profession that is better performed by men, which would be an example of representational harm. == Addressing representational harm == Initiatives to minimise representational harm include advertising for even more inclusive and accurate portrayals of marginalised teams in the media. Scholars and protestors recommend that the method to reducing representational injury depends on raising the selection of voices both behind and before the digital video camera. When marginalized groups are provided the chance to represent themselves, they can check traditional stereotypes and present their experiences additional authentically. Over the last few years, efforts to increase representation of people of color, women, and LGBTQ+ people in conventional media have made some progression. Films such as Selma, routed by Ava DuVernay, and tv series like Pose, developed by Ryan Murphy, have actually been extensively applauded for their nuanced and respectful representations of marginalised communities. These tasks existing complex individualities and stories that move past streamlined stereotypes. Self-representation is one more crucial method to addressing representational harm. By equipping marginalised locations to create their really own tales, media designers can effectively reduce the perpetuation of hazardous stereotypes. This procedure consists of both the manufacturing of media product by participants of these communities and proactively difficult typical media structures that have actually historically omitted them.
The Emperor's New Mind
The Emperor's New Mind: Concerning Computers, Minds and The Laws of Physics is a 1989 book by the mathematical physicist Roger Penrose that posits a quantum mind theory. Penrose argues that human consciousness is non-algorithmic, and thus is not capable of being modeled by a conventional Turing machine, which includes a digital computer. Penrose hypothesizes that quantum mechanics plays an essential role in the understanding of human consciousness. The collapse of the quantum wavefunction is seen as playing an important role in brain function. Most of the book is spent reviewing, for the scientifically-minded lay-reader, a plethora of interrelated subjects such as Newtonian physics, special and general relativity, the philosophy and limitations of mathematics, quantum physics, cosmology, and the nature of time. Penrose intermittently describes how each of these bears on his developing theme: that consciousness is not "algorithmic". Only the later portions of the book address the thesis directly. == Overview == Penrose states that his ideas on the nature of consciousness are speculative, and his thesis is considered erroneous by some experts in the fields of philosophy, computer science, and robotics. The Emperor's New Mind attacks the claims of artificial intelligence using the physics of computing: Penrose notes that the present home of computing lies more in the tangible world of classical mechanics than in the imponderable realm of quantum mechanics. The modern computer is a deterministic system that for the most part simply executes algorithms. Penrose shows that, by reconfiguring the boundaries of a billiard table, one might make a computer in which the billiard balls act as message carriers and their interactions act as logical decisions. The billiard-ball computer was first designed some years ago by Edward Fredkin and Tommaso Toffoli of the Massachusetts Institute of Technology. == Reception == Following the publication of the book, Penrose began to collaborate with Stuart Hameroff on a biological analog to quantum computation involving microtubules, which became the foundation for his subsequent book, Shadows of the Mind: A Search for the Missing Science of Consciousness. Penrose won the Science Book Prize in 1990 for The Emperor's New Mind. According to an article in the American Journal of Physics, Penrose incorrectly claims a barrier far away from a localized particle can affect the particle.
Learning rule
An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network. It is done by updating the weight and bias levels of a network when it is simulated in a specific data environment. A learning rule may accept existing conditions (weights and biases) of the network, and will compare the expected result and actual result of the network to give new and improved values for the weights and biases. Depending on the complexity of the model being simulated, the learning rule of the network can be as simple as an XOR gate or mean squared error, or as complex as the result of a system of differential equations. The learning rule is one of the factors which decides how fast or how accurately the neural network can be developed. Depending on the process to develop the network, there are three main paradigms of machine learning: supervised learning, unsupervised learning, and reinforcement learning. == Background == A lot of the learning methods in machine learning work similar to each other, and are based on each other, which makes it difficult to classify them in clear categories. But they can be broadly understood in 4 categories of learning methods, though these categories don't have clear boundaries and they tend to belong to multiple categories of learning methods - Hebbian - Neocognitron, Brain-state-in-a-box Gradient Descent - ADALINE, Hopfield Network, Recurrent Neural Network Competitive - Learning Vector Quantisation, Self-Organising Feature Map, Adaptive Resonance Theory Stochastic - Boltzmann Machine, Cauchy Machine Though these learning rules might appear to be based on similar ideas, they do have subtle differences, as they are a generalisation or application over the previous rule, and hence it makes sense to study them separately based on their origins and intents. === Hebbian Learning === Developed by Donald Hebb in 1949 to describe biological neuron firing. In the mid-1950s it was also applied to computer simulations of neural networks. Δ w i = η x i y {\displaystyle \Delta w_{i}=\eta x_{i}y} Where η {\displaystyle \eta } represents the learning rate, x i {\displaystyle x_{i}} represents the input of neuron i, and y is the output of the neuron. It has been shown that Hebb's rule in its basic form is unstable. Oja's Rule, BCM Theory are other learning rules built on top of or alongside Hebb's Rule in the study of biological neurons. ==== Perceptron Learning Rule (PLR) ==== The perceptron learning rule originates from the Hebbian assumption, and was used by Frank Rosenblatt in his perceptron in 1958. The net is passed to the activation (transfer) function and the function's output is used for adjusting the weights. The learning signal is the difference between the desired response and the actual response of a neuron. The step function is often used as an activation function, and the outputs are generally restricted to -1, 0, or 1. The weights are updated with w new = w old + η ( t − o ) x i {\displaystyle w_{\text{new}}=w_{\text{old}}+\eta (t-o)x_{i}} where "t" is the target value and "o" is the output of the perceptron, and η {\displaystyle \eta } is called the learning rate. The algorithm converges to the correct classification if: the training data is linearly separable η {\displaystyle \eta } is sufficiently small (though smaller η {\displaystyle \eta } generally means a longer learning time and more epochs) It should also be noted that a single layer perceptron with this learning rule is incapable of working on linearly non-separable inputs, and hence the XOR problem cannot be solved using this rule alone === Backpropagation === Seppo Linnainmaa in 1970 is said to have developed the Backpropagation Algorithm but the origins of the algorithm go back to the 1960s with many contributors. It is a generalisation of the least mean squares algorithm in the linear perceptron and the Delta Learning Rule. It implements gradient descent search through the space possible network weights, iteratively reducing the error, between the target values and the network outputs. ==== Widrow-Hoff Learning (Delta Learning Rule) ==== Similar to the perceptron learning rule but with different origin. It was developed for use in the ADALINE network, which differs from the Perceptron mainly in terms of the training. The weights are adjusted according to the weighted sum of the inputs (the net), whereas in perceptron the sign of the weighted sum was useful for determining the output as the threshold was set to 0, -1, or +1. This makes ADALINE different from the normal perceptron. Delta rule (DR) is similar to the Perceptron Learning Rule (PLR), with some differences: Error (δ) in DR is not restricted to having values of 0, 1, or -1 (as in PLR), but may have any value DR can be derived for any differentiable output/activation function f, whereas in PLR only works for threshold output function Sometimes only when the Widrow-Hoff is applied to binary targets specifically, it is referred to as Delta Rule, but the terms seem to be used often interchangeably. The delta rule is considered to a special case of the back-propagation algorithm. Delta rule also closely resembles the Rescorla-Wagner model under which Pavlovian conditioning occurs. === Competitive Learning === Competitive learning is considered a variant of Hebbian learning, but it is special enough to be discussed separately. Competitive learning works by increasing the specialization of each node in the network. It is well suited to finding clusters within data. Models and algorithms based on the principle of competitive learning include vector quantization and self-organizing maps (Kohonen maps).
WinFIG
WinFIG is a proprietary shareware vector graphics editor application. The file format and rendering are as close to Xfig as possible, but the program takes advantage of Windows features like clipboard, printer preview, multiple documents etc. As of 2011, WinFIG is under active development, with new features being added regularly. == History == The first release was in March 2003 and based on the Amiga program AmiFIG by the same author, which is also an Xfig compatible vector drawing application. WinFIG was not created by porting the Xfig source code to Windows. It is an independent implementation. Starting with release 4.0 WinFIG was ported from MFC to the Qt toolkit as the application framework and thereby enabling the first release of a Linux version. After Version 7.8 the Version scheme changes to years with version 2021.1. == Interface and usability == WinFIG is designed to provide a clear, efficient and convenient graphical user interface. It allows working on multiple documents using an MDI user interface and provides unlimited undo and redo of actions. == Features == === Object creation === The basic types of objects in WinFIG are: Open and closed Splines Ellipses Polylines and Polygons Texts LaTeX formatted texts Arcs Images: PNG, GIF, JPEG, EPS and more Compound objects, which are hierarchical compositions of objects Objects can have several attributes, which depend on the object type: Line width Line style Line cap style Line join style Arrows Outline color, fill color and fill pattern === Object manipulation === move copy scale rotate align add/delete points from lines or splines copy object attributes Numerical input of point coordinates === Exports === WinFIG can export into various formats: Raster formats: GIF, JPEG, PNG, PPM, XBM, XPM, PCX, TIFF, SLD Formats for printed documents: PostScript, PDF, LaTeX, HP-GL (printer control language used by Hewlett-Packard plotters), Vector graphics formats: EPS, SVG, PSTricks, TPIC, PIC, CGM, Metafont, MetaPost, EMF, Tk. === Miscellaneous === Winfig can handle smart links. A smart link is a moving connection from a source to a target object. It is established by connecting the end point of a line or spline to another object. The connecting line or spline segment follows the movements of the target object. Smart links are useful for diagrams, graphs etc. WinFIG can show a grid and provides several magnet modes for constraining editing operations to discrete coordinates. Objects can be organized in layers to control their Z-order. This is important to control overlapping of filled shapes. Object library: drawings can be stored in a special sub-folder in the program installation directory, which makes them available in the library dialog for easy reuse.
ITools Resourceome
iTools is a distributed infrastructure for managing, discovery, comparison and integration of computational biology resources. iTools employs Biositemap technology to retrieve and service meta-data about diverse bioinformatics data services, tools, and web-services. iTools is developed by the National Centers for Biomedical Computing as part of the NIH Road Map Initiative.