Miracle-dz/UniFAR-FTUs huggingface dataset:
Web Intelligence : Analysis of Unstructured Database of Documents Using KnowItAll

The web is a huge source of information. The ability to mine the web for the exact required information is a huge area for companies today. Many big companies are already working in this area and are searching for automation in this field because in the current situation this work has to be done manually. This paper presents a way to extract the exact required information from any database of documents like resume and present the relevant information to user in desired format with using the KnowItAll algorithm to increase the recall value. It is using the unsupervised learning concept and so it is faster than any manual method. Keywords— KNOWITALL, Information Extraction, Text mining
WHAT SIGNAL ARE YOU SENDING ? HOW WEBSITE QUALITY INFLUENCES PERCEPTIONS OF PRODUCT QUALITY AND PURCHASE INTENTIONS 1

An electronic commerce marketing channel is fully mediated by information technology, stripping away much of a product’s physical informational cues, and creating information asymmetries (i.e., limited information). These asymmetries may impede consumers’ ability to effectively assess certain types of products, thus creating challenges for online sellers. Signaling theory provides a framework for understanding how extrinsic cues— signals—can be used by sellers to convey product quality information to consumers, reducing uncertainty and facilitating a purchase or exchange. This research proposes a model to investigate website quality as a potential signal of product quality and consider the moderating effects of product information asymmetries and signal credibility. Three experiments are reported that examine the efficacy of signaling theory as a basis for predicting online consumer behavior with an experience good. The results indicate that website quality influences consumers’ perceptions of product quality, which subsequently affects online purchase intentions. Additionally, website quality was found to have a greater influence on perceived product quality when consumers had higher information asymmetries. Likewise, signal credibility was found to strengthen the relationship between website quality and product quality perceptions for a high quality website. Implications for future research and website design are examined.
Evaluation of the exertion and motivation factors of a virtual reality exercise game for children with autism

Children with autism experience significant positive behavioral and health benefits from exercise, though many of these children tend to lead sedentary lifestyles. Video games that incorporate physical activity, known as exergames, may help to motivate such children to engage in vigorous exercise, thus leading to more healthy lifestyles and reducing the likelihood of obesity. In this paper, we present a study of physical activity and motivation level for ten children with autism as they played an immersive virtual reality exergame that involved fast-paced full-body movement. Our results showed that most children, including non-verbal participants, were able to achieve vigorous activity levels, with several of them maintaining very high levels of exertion. Furthermore, the children reported high levels of enjoyment and indicated they would exercise more often if such games were routinely available. These encouraging findings suggest that exergames are a promising way to empower the families of children with autism with tools to help improve their child's health and quality of life.
A novel registration and de-noising method for multiple sequence MRI of atherosclerosis

Multiple sequence magnetic resonance imaging (MRI) is often hampered by noise and misalignment. The wavelet de-noising lacks directivity and does not preserve image details, which limits its effectiveness. To solve the problem, an adaptive threshold de-noising method based on wavelet transformation is employed. As to misalignment, a registration method basis of shape context descriptor is proposed to accurately align multiple MR image sequences. In order to achieve better MRI de-noising capabilities, a new threshold criteria that is adaptive to the noise distribution in the high and low sub bands is developed to replace traditional threshold function used in wavelet transform denoising. Multiple sequences MRI registration are performed by identifying edge points with matching shape context firstly. Then, an iterative procedure is invoked to obtain the deformation field, which determines the B-spline interpolated output image. Experimental results show that the de-nosing method removes image noise effectively whilst preserving original information. The registration method can reach an overlap of 96%±0.8 between multi-sequence MR image.
Estimates for representation numbers of quadratic forms

Let −D be the discriminant of an imaginary quadratic field, and let f ∈ Z[x1, x2] be a primitive positive binary quadratic form having discriminant −D. Define rf (n) := 1 w #{x ∈ Z | f(x) = n} to be the number of representations of n by f up to automorphisms of f . In this paper estimates and asymptotics for the quantity ∑ n≤x rf (n) β are given, for all β ≥ 0 and uniformly in D = o(x). MSC (2000) *11E16, 11N56 keywords: binary quadratic forms, uniform asymptotic results, higher moments of arithmetic functions
The Best Kept Secrets with Corpus Linguistics

This paper presents the use of corpus linguistics techniques on supposedly “clean” corpora and identifies potential pitfalls. Our work relates to the task of filtering sensitive content, in which data security is strategically important for the protection of government and military information, and of growing importance in combating identity fraud. A naïve keyword filtering approach produces a large proportion of false positives, and the need for more fine-grained approaches, suggests the consideration of using corpus linguistics for such content filtering. We present work undertaken on the Enron corpus, a collection of emails that has had various tasks undertaken on various portions and versions of it by other researchers. We have made some efforts to reconcile differences between the different versions by considering what impact some of these versions have on our results. Our anticipated efforts in using automatic ontology learning [Gillam, Tariq and Ahmad 2005; Gillam and Ahmad 2005], and local grammars [Ahmad, Gillam and Cheng 2006] to discover points of interest within the Enron corpus have been reoriented to the problem of discovering “confidentiality banners” with a view to removing them from the collection. Our work to date makes strong use of collocation patterns [Smada 1993] to identify the signatures of these banners, and to use the banners themselves to better understand the different versions of the Enron corpus. We further consider the use of extended collocation patterns to identify text “zones”, following [Teufel and Moens 2000], and the subsequent potential for sentiment analysis [Klimt and Yang 2004]; [Pang, Lee and Vaithyanathan 2002]
Regret-based online ranking for a growing digital library

The most common environment in which ranking is used takes a very specific form. Users sequentially generate queries in a digital library. For each query, ranking is applied to order a set of relevant items from which the user selects his favorite. This is the case when ranking search results for pages on the World Wide Web or for merchandize on an e-commerce site. In this work, we present a new online ranking algorithm, called NoRegret KLRank. Our algorithm is designed to use "clickthrough" information as it is provided by the users to improve future ranking decisions. More importantly, we show that its long term average performance will converge to the best rate achievable by any competing fixed ranking policy selected with the benefit of hindsight. We show how to ensure that this property continues to hold as new items are added to the set thus requiring a richer class of ranking policies. Finally, our empirical results show that, while in some context NoRegret KLRank might be considered conservative, a greedy variant of this algorithm actually outperforms many popular ranking algorithms.
On pragmatic institutional economics ( Paper for the EAEPE

Usually mainstream economics is criticised for its economic concepts and assumptions. Much less attention of criticisers is drawn to the philosophical roots (Cartesianism and positivism) of the failure of this economics to capture economic realities and of its autistic character. Pragmatist philosophy was born at the end of the 19 th century to oppose Cartesian and positivist practices in scientific research. Pragmatism's based flows of social science thought (institutionalism in economics and Chicago school in sociology) were very influential in the United States in the first quarter of the 20 th century. Later their influence has fallen and pragmatism's based empirical research in economics has disappeared. In contrast with economics, the pragmatically oriented sociology continued to develop its methodology (Blumer, 1969) and techniques (Denzin and Lincoln, 2000) of empirical research. The aim of this paper is to attract the attention of economists to the tremendous unused potential of application of the pragmatist methodology to move economics from imaginary to real worlds. This paper is based on a personal research experience of the author in application of this methodology to the study of economic problems Descartes was very suspicious to results of observations. He was sure that the senses deceive us and all the things that we see are false. He appealed to doubt in everything. At the same time he was convinced that where the senses fail, the mind triumphs. " Cartesianism destroyed the balance which underlies true science: the balance between thinking and observing, deduction and induction, imagination and common sense, reflection and action, reason and passion, abstract thinking and realism, the world within and the world without the mind. Under the impact of Cartesianism the second element of the equation was sacrificed to the first [. ] Descartes' epistemological reflections opened an era of axiomatic, unhistorical, deductive thinking broadly called the Enlightenment. " (Hodgson, Samuels and Tool, 1994 p. 39) The Cartesian dualism with its separation of knowing from doing, object from subject, fact from value, theory from practice serves an epistemological foundation for neoclassical economics (Bush, 1993, p. 65). Founder of Pragmatism, Charles S. Peirce, understood the historical context of appearance of Cartesianism. Descartes revolted against authority as the ultimate source of truth and allowed theoretically scepticism. " That done, he sought a more natural fountain of true principles, and professed to find it in the human mind. " (Peirce, 1878, p. 125) In this way he underestimated …
Using Grammatical Roles to Improve Statistical Machine Translation

Statistical machine translation systems often struggle to preserve predicateargument structure. We present a new hierarchical machine translation model that explicitly captures the grammatical roles taken on by the words and phrases being translated (e.g., subject, object, and indirect object). Although existing hierarchical and syntax-based grammars can capture how many arguments a predicate takes, they have limited awareness of what should fill the argument slots. This results in difficult to interpret translations with scrambled predicate argument relationships. Our model adds grammatical role typing to the translation rules that helps preserve and correctly order predicate-argument structures. We find that grammatical typing systematically outperforms both traditional hierarchical and syntax augmented models on Chineseto-English translation across a variety of NIST MT evaluation sets.
CONVERGENCE TESTING FOR MCNP5 MONTE CARLO EIGENVALUE CALCULATIONS

Determining convergence of Monte Carlo criticality problems is complicated by the statistical noise inherent in the random walks of the neutrons in each generation. The latest version of MCNP5 incorporates an important new tool for assessing convergence: the Shannon entropy of the fission source distribution, Hsrc. Shannon entropy is a well-known concept from information theory and provides a single number for each iteration to help characterize convergence trends for the fission source distribution. MCNP5 computes Hsrc for each iteration, and these values may be plotted to examine convergence trends. Convergence testing should include both keff and Hsrc, since the fission distribution will converge more slowly than keff, especially when the dominance ratio is close to 1.0.
Geometric Structure of the Adaptive Controller of the Human Arm

The objects with which the hand interacts with may significantly change the dynamics of the arm. How does the brain adapt control of arm movements to this new dynamics? We show that adaptation is via composition of a model of the task''s dynamics. By exploring generalization capabilities of this adaptation we infer some of the properties of the computational elements with which the brain formed this model: the elements have broad receptive fields and encode the learned dynamics as a map structured in an intrinsic coordinate system closely related to the geometry of the skeletomusculature. The low--level nature of these elements suggests that they may represent a set of primitives with which a movement is represented in the CNS.
Competition in LNG shipping 1 THE LNG MARKET AND A GAME THEORY APPROACH TO COMPETITION IN LNG SHIPPING

The LNG (Liquefied Natural Gas) trade is one of the most promising sectors in energy shipping. It is expected that competition will increasingly develop in the shipping segment of the LNG chain, which at least in its first phases will have the characteristics of an oligopolistic market. The LNG shipping market context is appropriate for the adoption of a (non-cooperative) game theoretic analysis framework to support decision-making. This paper focuses on oligopolistic competition in LNG shipping over the transportation capacity supplied to a trade route by competing shipping companies. It also examines the possibility of non-cooperative collusion among the competing parties, in order for them to share higher profits. The conclusions concern the optimal level of capacity supply by the competitors, under certain interaction settings, and the conditions under which they can sustain Pareto efficient equilibria.
Stereo Seam Carving a Geometrically Consistent Approach

Image retargeting algorithms attempt to adapt the image content to the screen without distorting the important objects in the scene. Existing methods address retargeting of a single image. In this paper, we propose a novel method for retargeting a pair of stereo images. Naively retargeting each image independently will distort the geometric structure and hence will impair the perception of the 3D structure of the scene. We show how to extend a single image seam carving to work on a pair of images. Our method minimizes the visual distortion in each of the images as well as the depth distortion. A key property of the proposed method is that it takes into account the visibility relations between pixels in the image pair (occluded and occluding pixels). As a result, our method guarantees, as we formally prove, that the retargeted pair is geometrically consistent with a feasible 3D scene, similar to the original one. Hence, the retargeted stereo pair can be viewed on a stereoscopic display or further processed by any computer vision algorithm. We demonstrate our method on a number of challenging indoor and outdoor stereo images.
Learning to Attend: A Connectionist Model of Situated Language Comprehension

Evidence from numerous studies using the visual world paradigm has revealed both that spoken language can rapidly guide attention in a related visual scene and that scene information can immediately influence comprehension processes. These findings motivated the coordinated interplay account (Knoeferle & Crocker, 2006) of situated comprehension, which claims that utterance-mediated attention crucially underlies this closely coordinated interaction of language and scene processing. We present a recurrent sigma-pi neural network that models the rapid use of scene information, exploiting an utterance-mediated attentional mechanism that directly instantiates the CIA. The model is shown to achieve high levels of performance (both with and without scene contexts), while also exhibiting hallmark behaviors of situated comprehension, such as incremental processing, anticipation of appropriate role fillers, as well as the immediate use, and priority, of depicted event information through the coordinated use of utterance-mediated attention to the scene.
A Combined Memory Compression And Hierarchical Motion Estimation Architecture For Video Encoding In Embedded Systems

In this paper a new technique is presented that combines memory compression in video encoders with fast and efficient motion estimation (ME). This technique is mainly oriented to embedded systems, which demand simple and power aware algorithms. Video encoding needs increasing amounts of memory for storing reference pictures. Memory compression allows reducing the footprint of the application, lowering the total implementation cost. In this paper, we combine memory compression and hierarchical ME so that the overhead associated to implement both techniques is shared. Thus, a net gain in processing speed is obtained, while reducing costs and power consumption
ACE: adaptive cluster expansion for maximum entropy graphical model inference

Motivation Graphical models are often employed to interpret patterns of correlations observed in data through a network of interactions between the variables. Recently, Ising/Potts models, also known as Markov random fields, have been productively applied to diverse problems in biology, including the prediction of structural contacts from protein sequence data and the description of neural activity patterns. However, inference of such models is a challenging computational problem that cannot be solved exactly. Here we describe the adaptive cluster expansion (ACE) method to quickly and accurately infer Ising or Potts models based on correlation data. ACE avoids overfitting by constructing a sparse network of interactions sufficient to reproduce the observed correlation data within the statistical error expected due to finite sampling. When convergence of the ACE algorithm is slow, we combine it with a Boltzmann Machine Learning algorithm (BML). We illustrate this method on a variety of biological and artificial data sets and compare it to state-of-the-art approximate methods such as Gaussian and pseudo-likelihood inference. Results We show that ACE accurately reproduces the true parameters of the underlying model when they are known, and yields accurate statistical descriptions of both biological and artificial data. Models inferred by ACE have substantially better statistical performance compared to those obtained from faster Gaussian and pseudo-likelihood methods, which only precisely recover the structure of the interaction network. Availability The ACE source code, user manual, and tutorials with example data are freely available on GitHub at https://github.com/johnbarton/ACE. Contacts jpbarton@gmail.com, cocco@lps.ens. fr Supplementary information Supplementary data are available
ElrA binding to the 3′UTR of cyclin E1 mRNA requires polyadenylation elements

The early cell divisions of Xenopus laevis and other metazoan embryos occur in the presence of constitutively high levels of the cell cycle regulator cyclin E1. Upon completion of the 12th cell division, a time at which many maternal proteins are downregulated by deadenylation and destabilization of their encoding mRNAs, maternal cyclin E1 protein is downregulated while its mRNA is polyadenylated and stable. We report here that stable polyadenylation of cyclin E1 mRNA requires three cis-acting elements in the 3′ untranslated region; the nuclear polyadenylation sequence, a contiguous cytoplasmic polyadenylation element and an upstream AU-rich element. ElrA, the Xenopus homolog of HuR and a member of the ELAV gene family binds the cyclin E1 3′UTR with high affinity. Deletion of these elements dramatically reduces the affinity of ElrA for the cyclin E1 3′UTR, abolishes polyadenylation and destabilizes the mRNA. Together, these findings provide compelling evidence that ElrA functions in polyadenylation and stabilization of cyclin E1 mRNA via binding these elements.
Electronic System of an Intelligent Machine: the Case of an Assistive Bed Device

This work presents the electronic design of an intelligent device that includes a monitor system for automatic movements of a robotic hospital bed based on posture classication and identication. This feature was carried out in response to the necessities dened by the application of a diagnostic identication methodology. This method was successfully applied to a public Mexican hospital and the issue identied was the mobility of elderly people and physically challenged individuals. The movement of these patients can be performed routinely or sporadically during their stay in a hospital. For patients who require a particular routine application of this action, the system includes an intelligent monitor system. This intelligent system allows medical experts to program the movements of the robotic bed considering the posture of the patients and the time in bed. This paper shows the hardware and software design of the electronic system and the physicals results.
An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications

A self-constructing neural fuzzy inference network (SONFIN) with on-line learning ability is proposed in this paper. The SONFIN is inherently a modified Takagi–Sugeno–Kang (TSK)-type fuzzy rule-based model possessing neural network’s learning ability. There are no rules initially in the SONFIN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. In the structure identification of the precondition part, the input space is partitioned in a flexible way according to a aligned clustering-based algorithm. As to the structure identification of the consequent part, only a singleton value selected by a clustering method is assigned to each rule initially. Afterwards, some additional significant terms (input variables) selected via a projection-based correlation measure for each rule will be added to the consequent part (forming a linear equation of input variables) incrementally as learning proceeds. The combined precondition and consequent structure identification scheme can set up an economic and dynamically growing network, a main feature of the SONFIN. In the parameter identification, the consequent parameters are tuned optimally by either least mean squares (LMS) or recursive least squares (RLS) algorithms and the precondition parameters are tuned by backpropagation algorithm. Both the structure and parameter identification are done simultaneously to form a fast learning scheme, which is another feature of the SONFIN. Furthermore, to enhance the knowledge representation ability of the SONFIN, a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved. Proper linear transformations are also learned dynamically in the parameter identification phase of the SONFIN. To demonstrate the capability of the proposed SONFIN, simulations in different areas including control, communication, and signal processing are done. Effectiveness of the SONFIN is verified from these simulations.