Miracle-dz/UniFAR-FTUs huggingface dataset:
Conceptualization of psychosocial functioning: Understanding structure and relations with personality and psychopathology

Psychosocial functioning is a broad construct that encompasses a wide range of behaviors. Impairment in one or more areas of functioning is commonly observed in psychiatric patients. Moreover, an enduring, rigid pattern of maladaptive behaviors (e.g., interpersonal conflict) are frequent manifestations of personality pathology. The purpose of this study was to understand this comprehensive, yet underexamined, construct of psychosocial functioning by studying the construct’s structure and associations with external correlates (e.g., personality traits and pathology, mood symptoms). The study was conducted in two phases. In Phase 1 study, 429 community residents (student N = 218; community sample N = 211) were recruited and administered eight psychosocial functioning measures sampled from three psychosocial functioning domains: Daily functioning measures typically used in Axis I disorders, personality functioning measures developed for use with Axis II disorder, and quality of life/ satisfaction measures. Personality traits—specifically the big five personality traits (Neuroticism, Extraversion, Agreeableness, Conscientiousness, and Openness)—and mood/affect measures also were administered to understand their associations with psychosocial functioning scales. Results suggested that psychosocial functioning could be understood as having a threefactor structure: Positive General Functioning, Poor Personality Functioning, and Poor Basic Functioning. When four of the five personality traits were added as variables (Openness excluded), a four-factor structure emerged—Positive General Functioning, Poor Basic Functioning, Internalizing Dysfunction, and Externalizing Dysfunction—with personality traits strong markers of the psychosocial functioning factors with the exception of Basic Functioning. In Phase 2, psychiatric outpatients were recruited
Error control for the detection of rare and weak signatures in massive data

In this paper, we address the general issue of detecting rare and weak signatures in very noisy data. Multiple hypothe ses testing approaches can be used to extract a list of com ponents of the data that are likely to be contaminated by a source while controlling a global error criterion. However most of efficients methods available in the literature are de rived for independent tests. Based on the work of Benjamini and Yekutieli [1], we show that under some classical positivity assumptions, the Benjamini-Hochberg procedure for False Discovery Rate (FDR) control can be directly applied to the result produced by a very common tool in signal and image processing: the matched filter. This shows that despite the de pendency structure between the components of the matched filter output, the Benjamini-Hochberg procedure still guaran tee the FDR control. This is illustrated on both synthetic and real data.
Efficiency of the mixture model components using fuzzy integrals

In this paper -we describe the fuzzy integral used not only as a fusion operator, but also as a selection/removal/add operator. This operator is able to tell us about the importance of the sources that we merge. In the system modeling context, we have applied this technique to mixture models, where the number of component densities of the mixture model is not known a priori. Given a mixture model, we use the fuzzy integral like selection/removal/add operator of the component densities of this model. Hence, we will ascertain the number of components necessary with a controlled error. In order to obtain these, results, we propose a decision process based on coefficients provided by the fuzzy integral. We have used several data sets to evaluate the accuracy of the method. We show an illustrative example
Crowdsourcing ISP characterization to the network edge

Evaluating and characterizing Internet Service Providers (ISPs) is critical to subscribers shopping for alternative ISPs, companies providing reliable Internet services, and governments surveying the coverage of broadband services to its citizens. Ideally, ISP characterization should be done at scale, continuously, and from end users. While there has been significant progress toward this end, current approaches exhibit apparently unavoidable tradeoffs between coverage, continuous monitoring and capturing user-perceived performance. In this paper, we argue that network-intensive applications running on end systems avoid these tradeoffs, thereby offering an ideal platform for ISP characterization. Based on data collected from 500,000 peer-to-peer BitTorrent users across 3,150 networks, together with the reported results from the U.K. Ofcom/SamKnows studies, we show the feasibility of this approach to characterize the service that subscribers can expect from a particular ISP. We discuss remaining research challenges and design requirements for a solution that enables efficient and accurate ISP characterization at an Internet scale.
Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI

To segment brain tissues in magnetic resonance images of the brain, the authors have implemented a stochastic relaxation method which utilizes partial volume analysis for every brain voxel, and operates on fully three-dimensional (3-D) data. However, there are still problems with automatically or semi-automatically segmenting thick magnetic resonance (MR) slices, particularly when trying to segment the small lesions present in MR images of multiple sclerosis patients. To improve lesion segmentation the authors have extended their method of stochastic relaxation by both pre- and post-processing the MR images. The preprocessing step involves image enhancement using homomorphic filtering to correct for nonhomogeneities in the coil and magnet. Because approximately 95% of all multiple sclerosis lesions occur in the white matter of the brain, the post-processing step involves application of morphological processing and thresholding techniques to the intermediate segmentation in order to develop a mask image containing only white matter and Multiple Sclerosis (MS) lesion. This white/lesion masked image is then segmented by again applying the authors' stochastic relaxation technique. The process has been applied to multispectral MRI scans of multiple sclerosis patients and the results compare favorably to manual segmentations of the same scans obtained independently by radiology health professionals.
TagNet: Using Soft Semantics to Search the Web

Semantic annotations are key to efficiently retrieve resources on the Web. On the one hand, ontologies underlying Web resources give rise to linked data. On the other hand, tagging has become increasingly popular to bring order in data across Web applications and social networks. While taxonomies and folksonomies serve the same purpose (i.e. classification), there is a large gap in semantics between uncontrolled keywords used for tagging and hierarchical concepts found in a taxonomy. In this paper we introduce sematags as light-weight ‘soft semantics’ which aim to bridge the gap between ‘no semantics’ and ‘hard semantics’. Sematags define aliases (synonyms) and isas (hypernyms) which overcome the typical issues conventional tags cope with such as ambiguity. Furthermore, we present TagNet, a framework that extracts sematags from lexicons and existing knowledge bases and exploits them to annotate, link and retrieve resources on the Web. We evaluate how soft semantics can be used to semi-automatically map tagged photos in Flickr on DBpedia concepts and vice versa.
Regularized Nonlinear Discriminant Analysis - An Approach to Robust Dimensionality Reduction for Data Visualization

We present a novel approach to dimensionality reduction for data visualization that is a combination of two deep neural networks (DNNs) with different objectives. One is a nonlinear generalization of Fisher’s linear discriminant analysis (LDA). It seeks to improve the class separability in the desired feature space, which is a natural strategy to obtain well-clustered visualizations. The other DNN is a deep autoencoder. Here, an encoding and a decoding DNN are optimized simultaneously with respect to the decodability of the features obtained by encoding the data. The idea behind the combined DNN is to use the generalized discriminant analysis as an encoding DNN and to equip it with a regularizing decoding DNN. Regarding data visualization, a well-regularized DNN guarantees to learn sufficiently similar data visualizations for different sets of samples that represent the data approximately equally good. Clearly, such a robustness against fluctuations in the data is essential for real-world applications. We therefore designed two extensive experiments that involve simulated fluctuations in the data. Our results show that the combined DNN is considerably more robust than the generalized discriminant analysis alone. Moreover, we present reconstructions that reveal how the visualizable features look like back in the original data space.
An Examination of Reliability and Validity Claims of a Foreign Language Proficiency Test.

AN EXAMINATION OF RELIABILITY AND VALIDITY CLAIMS OF A FOREIGN LANGUAGE PROFICIENCY TEST Walter J. Mircea-Pines, PhD George Mason University, 2009 Dissertation Director: Anthony E. Kelly This dissertation study examined the reliability and validity claims of a modified version of the Spanish Modern Language Association Foreign Language Proficiency Test for Teachers and Advanced Students administered at George Mason University (GMU). The study used the 1999 computerized GMU version that was administered to 277 testtakers via WebCT, (a web course management tool), and not the original 1961 penciland-paper edition. The GMU examination retained five sections from the test’s original seven, namely Speaking, Listening, Reading, Writing, and Culture and Civilization. The original response formats, i.e. multiple-choice and true-false, were preserved for the most part. The Writing section was changed from the original error correction and text editing format to a fill-in-the-blanks format. The Culture and Civilization section was re-written due to obsolescence; however it retained the original answer format, to which a matching component was added. The study’s design was framed by the investigative steps proposed by Messick (1989) and Nitko and Brookhart (2007). An ex post facto approach was used, which mixed the confirmatory factor analysis and multiple regression analyses with descriptive interpretations. The quantitative analyses examined the impact of nationality, gender and average time spent on completing the tasks on the five subscales of the Spanish GMU MLA examinations. The analyses provided mixed results related to the predictors’ ability to explain the subscale scores. The descriptive analyses revealed numerous validity flaws and gaps in the test construction. Such inadequacies overshadowed the positive aspects and eventually led to my questioning of the hypothesis that the judgments based on data from the Spanish GMU MLA examinations are reliable and valid. Recommendations for possible revisions were made, along with suggestions for future research avenues.
Alpha-Stable Matrix Factorization

Matrix factorization (MF) models have been widely used in data analysis. Even though they have been shown to be useful in many applications, classical MF models often fall short when the observed data are impulsive and contain outliers. In this study, we present αMF, a MF model with α-stable observations. Stable distributions are a family of heavy-tailed distributions that is particularly suited for such impulsive data. We develop a Markov Chain Monte Carlo method, namely a Gibbs sampler, for making inference in the model. We evaluate our model on both synthetic and real audio applications. Our experiments on speech enhancement show that αMF yields superior performance to a popular audio processing model in terms of objective measures. Furthermore, αMF provides a theoretically sound justification for recent empirical results obtained in audio processing.
Method Effects in IS Survey Research: An Assessment and Recommendations

Issues involving method effects are routinely taught to PhD students in Information Systems (IS). Unfortunately, the results of an assessment of a population of 128 survey-based studies published in three top IS journals over a seven-year period (1999-2005) reveal that relatively little attention is being paid to method bias and that the threat of serious method bias is great in many of the published studies. For instance, even the best-understood variety of method bias— common source bias—is found to have gone unnoted in over one-third of the papers that used a single respondent for all construct measures for reasons other than necessity. This study was motivated by studies in other areas of the social sciences which have resulted in calls for areas such as IS to conduct empirical assessments of the frequency of the various forms of method bias. Here, the myriad sources of method bias are reviewed and methods for minimizing or eliminating method effects, both in the design of a study and in the subsequent analysis of the data, are discussed. Data on the frequency of appearance of a wide variety of potential sources of method bias are provided and conclusions are drawn. A series of recommendations is made concerning creating greater awareness of method bias on the part of the IS research community and greater use of method bias evaluation criteria in the screening reviews of papers that are done by IS journals.
A Free–Wilson‐like Approach to Analyze QSAR Models Based on Graph Decomposition Kernels

The high-dimensional, implicit feature space in which a kernel-based machine learning model is defined has many beneficial properties like the possible non-linearity of the model in the input space. However, this implicit mapping is also the cause for one of the key drawbacks of kernel-based approaches compared to other machine learning approaches. Machine learning is not only about the development of a model, which can be used to predict a property of unknown data, but also to gain insight in the cause of that property. This becomes more apparent in the alternative term information retrieval that, though not fully equivalent, describes a task closely related to machine learning, but more focused on the causality of the pattern-property relationship contained in the data. This informational retrieval aspect of data mining is not trivial to realize using kernel-based techniques.
Augmentation techniques for efficient exploration in head-mounted display environments

Physical characteristics and constraints of today's head-mounted displays (HMDs) often impair interaction in immersive virtual environments (VEs). For instance, due to the limited field of view (FOV) subtended by the display units in front of the user's eyes more effort is required to explore a VE by head rotations than for exploration in the real world. In this paper we propose a combination of two augmentation techniques that have the potential to make exploration of VEs more efficient: (1) augmenting the geometric FOV (GFOV) used for rendering the VE, and (2) amplifying head rotations while the user changes her head orientation. In order to identify how much manipulation can be applied without users noticing, we conducted two psychophysical experiments in which we analyzed subjects' ability to discriminate between virtual and real head pitch and roll rotations while three different geometric FOVs were used. Our results show that the combination of both techniques has great potential to support efficient exploration of VEs. We found that virtual pitch and roll rotations can be amplified by 30% and 44% respectively, when the GFOV matches the subject's estimation of the most natural FOV. This leads to a possible reduction of the user's effort required to explore the VE using a combination of both techniques by approximately 25%.
Symmetry Groups for the Decomposition of Reversible Computers, Quantum Computers, and Computers in between

Whereas quantum computing circuits follow the symmetries of the unitary Lie group, classical reversible computation circuits follow the symmetries of a finite group, i.e., the symmetric group. We confront the decomposition of an arbitrary classical reversible circuit with w bits and the decomposition of an arbitrary quantum circuit with w qubits. Both decompositions use the control gate as building block, i.e., a circuit transforming only one (qu)bit, the transformation being controlled by the other w−1 (qu)bits. We explain why the former circuit can be decomposed into 2w − 1 control gates, whereas the latter circuit needs 2w − 1 control gates. We investigate whether computer circuits, not based on the full unitary group but instead on a subgroup of the unitary group, may be decomposable either into 2w − 1 or into 2w − 1 control gates.
The Sleuth Approach to Aerospace Software

Domain Based Testing (DBT) is a test generation method based on two concepts from software reuse, domain analysis and domain modeling. Instead of using domain models for reuse, they serve as a structure to generate tests. In general, DBT forms a family of test generation methods. Each member of the family deenes a domain analysis and a domain model for each problem domain or class of software. To show these principles, we deene a domain analysis and domain model for systems with a command-language interface. The analysis exploits information from the command language, and the domain model stores syntax and semantic information needed for test case generation. We develop a four-part Test Generation Process Model to support automated test generation. We also describe an interactive test generation tool called Sleuth. Sleuth supports domain based testing, follows our test process model, and provides utilities to reuse test cases.
2016 Ieee International Conference on Big Data (big Data) Company Recommendation for New Graduates via Implicit Feedback Multiple Matrix Factorization with Bayesian Optimization

—We have developed a recommendation system of companies for new graduates. In this paper, we defined high/low-browsed companies and constructed the recruitment navigation system of the low-browsed companies suitable for each student from the browsing data. Different from traditional recommendations, we need to deal with the problems that the entry (application) period is limited. The methods applicable to our problem tend to involve tuning many hyper parameters. We solved this problem by using Bayesian optimization. We empirically evaluated the system by using the entry data, which indicates which company a student has applied for. When we recommended 100 companies, our method covered over 45% of the companies that students applied to, while the existing methods covered only about 25%. Moreover, we found that although we did not validate our model using the entry data in the early stage of the recruitment activities, we can substitute the validation by using the browsing data in the Bayesian optimization.
End-to-End Restorable Oblivious Routing of Hose Model Traffic

Two-phase routing, where traffic is first distributed to intermediate nodes before being routed to the final destination, has been recently proposed for handling widely fluctuating traffic without the need to adapt network routing to changing traffic. Preconfiguring the network in a traffic-independent manner using two-phase routing simplifies network operation considerably. In this paper, we extend this routing scheme by providing resiliency against link failures through end-to-end shared backup path restoration. We view this as important progress toward adding carrier-class reliability to the robustness of the scheme so as to facilitate its future deployment in Internet service provider (ISP) networks. In shared backup path restoration, each connection consists of a link-disjoint primary and backup path pair; two backup paths can share bandwidth on their common links if their primary paths are link-disjoint. We show that the optimization problem for maximum throughput two-phase routing with shared backup path restoration is NP-hard. Assuming an approximation oracle for a certain disjoint paths problem (called SBPR-DISJOINT-PATHS, which is also NP-hard) involving the dual variables of a path indexed linear programming formulation for the problem, we design a combinatorial algorithm with provable guarantees. We also provide heuristics for finding approximating solutions to the SBPR-DISJOINT-PATHS problem. We evaluate the throughput performance and number of intermediate nodes in two-phase routing for the above and other restoration mechanisms for two-phase routing on actual ISP topologies collected for the Rocketfuel project and three research network topologies.
An Automatic Software Requirement Analysis Model based on Planning and Machine Learning Techniques

In the past years, the scale of software is growing quickly as more and more organizations begin to deploy their business on Internet. As a result, requirement analysis becomes a challenging issue and conventional approaches might significantly increase the costs of software development. Therefore, automatic requirement analysis techniques have attract more and more attentions, which allows for modeling and analyzing requirements formally, rapidly and automatically, avoiding mistakes made by misunderstanding between engineers and users, and saving lots of time and manpower. In this paper, we propose an approach to acquiring requirements automatically, which adopts automated planning techniques and machine learning methods to convert software requirement into an incomplete planning domain. By this approach, we design an algorithm called Intelligent Planning based Requirement Analysis (IPRA), to learn action models with uncertain effects. A concrete experiment is conducted to investigate the proposed algorithm, and the results indicate that it can obtain a complete planning domain and convert it into software requirement specification.
A quasi-resonant soft switching 48-pulse PWM inverter with closed loop current control for the realization of FACTS devices

This paper discusses a 48-pulse H-bridge PWM inverter which utilizes a quasi-resonant topology that is comprised of a clamping circuit and a resonant circuit, in order to achieve soft switching. The use of a multi-pulse inverter increases the quality of the output whereas the implementation of soft switching technique increases the overall efficiency of the inverter by decreasing switching losses that reach considerable values with the abundant number of switches and a switching frequency of the order of KHz. Furthermore, in order to be able to respond load variations a feedback loop is constructed with a PI controller to realize closed loop current control. The overall system is characterized by a multi-pulse inverter the input voltage of which is not a pure DC but a waveform that occasionally reaches zero level with the help of a quasi-resonant circuit at the source side, and which can keep its current output at a predetermined level by varying the PWM gating signal's duty cycle with the help of a closed loop current control. This system is aimed to be a building block for FACTS devices such as static synchronous series compensator (SSSC). The motivation of designing a more efficient and dynamic inverter with high power quality is to construct more efficient and effective FACTS devices.