Miracle-dz/UniFAR-FTUs huggingface dataset:
In-Store Shopping Activity Modeling Based on Dynamic Bayesian Networks

RFID technology has been recently adopted in retail environments to track consumer in-store movements, bringing about new exciting opportunities for spatial data mining-enabled marketing. In this paper, we propose a Dynamic Bayesian Networks (DBN)-based model of customer in-store shopping trips and activities. This model infers a customer’s product purchase interest given the observations of customer in-store movement data collected through a RFID-based wireless network. We also report a preliminary evaluation of our approach using a real-world dataset. Our proposed approach can be potentially used to help create an intelligent shopping environment, in which store operators can target their marketing efforts at providing effective location-aware real-time product recommendation for individual customers.
One Road to Turnover: An Examination of Work Exhaustion in Technology Professionals

The concept of work exhaustion (or job burnout) from the management and psychology research literature is examined in the context of technology professionals. Data were collected from 270 IT professionals and managers in various industries across the United States. Through structural equation modeling, work exhaustion was shown to partially mediate the effects of workplace factors on turnover intention. In addition, the results of the study revealed that: (1) technology professionals experiencing higher levels of exhaustion reported higher intentions to leave the job and, (2) of the variables expected to influence exhaus
Irregularities and Scaling in Signal and Image Processing: Multifractal Analysis

B. Mandelbrot gave a new birth to the notions of scale invariance, selfsimilarity and non-integer dimensions, gathering them as the founding corner-stones used to build up fractal geometry. The first purpose of the present contribution is to review and relate together these key notions, explore their interplay and show that they are different facets of a same intuition. Second, it will explain how these notions lead to the derivation of the mathematical tools underlying multifractal analysis. Third, it will reformulate these theoretical tools into a wavelet framework, hence enabling their better theoretical understanding as well as their efficient practical implementation. B. Mandelbrot used his concept of fractal geometry to analyze real-world applications of very different natures. As a tribute to his work, applications of various origins, and where multifractal analysis proved fruitful, are revisited to illustrate the theoretical developments proposed here.
Expressive Number Restrictions in Description Logics

Number restrictions are concept constructors that are available in almost all implemented Description Logic systems. However, they are mostly available only in a rather weak form, which considerably restricts their expressive power. On the one hand, the roles that may occur in number restrictions are usually of a very restricted type, namely atomic roles or complex roles built using either intersection or inversion. In the present paper, we increase the expressive power of Description Logics by allowing for more complex roles in number restrictions. As role constructors, we consider composition of roles (which will be present in all our logics) and intersection, union, and inversion of roles in different combinations. We will present two decidability results (for the basic logic that extends ALC by number restrictions on roles with composition, and for one extension of this logic), and three undecidability results for three other extensions of the basic logic. On the other hand, with the rather weak form of number restrictions available in implemented systems, the number of role successors of an individual can only be restricted by a fixed non-negative integer. To overcome this lack of expressiveness, we allow for variables ranging over the non-negative integers in place of the fixed numbers in number restrictions. The expressive power of this constructor is increased even further by introducing explicit quantifiers for the numerical variables. The Description Logic obtained this way turns out to have an undecidable satisfiability problem. For a restricted logic we show that concept satisfiability is decidable.
Change in female adolescent's sex role attitudes as a function of women's study course

Changes in female adolescents's perceptions and attitudes toward their sex roles as a function of participating in a course on women's role in society was assessed, using a pre-post experimental design. As predicted, the students "Ideal Self" sex role concept became more androgynous as compared to the control group not enrolled in the course. The hypothesis that the student's attitudes toward women would become more liberal as a result of the educational experience was also confirmed. Contrary to prediction, the students' "Real Self" sex role concept did not become more androgynous as a result of the course. The relationship between female adolescents's perceptions of their sex roles and successful resolution of the adolescent identity crisis was discussedr as well as the necessity of supportive experiences to aid the adolescent in integrating a more androgynous identity.
Beclin 1 Biology and its Role in Heart Disease

Macroautophagy (hereafter termed autophagy) is a highly evolutionarily conserved pathway that degrades intracellular components such as damaged organelles in lysosome. Autophagy occurs at low basal levels in virtually all types of cells, which is required for the maintenance of cellular homeostasis. Beclin 1 protein, encoded by the beclin 1 gene, plays a central role in the regulation of autophagy. Beclin 1 primarily functions as a scaffolding protein assembling Beclin 1 interactome to regulate Class III PI3K/VPS34 activity, which in turn, tightly controls autophagy at multiple stages. In addition to autophagy, Beclin 1 participates in the regulation of other biological processes such as endocytosis, apoptosis and phagocytosis. Fine-tuning of Beclin 1 protein levels, intracellular localization and the assembly of its interactome is pivotal for the proper execution of these biological functions. Deregulation of Beclin 1 contributes to the pathogenesis of a variety of human diseases. In this review, we summarize biology of Beclin 1 and its role in human pathology, with an emphasis on heart disease.
Tunneling and Decomposition-Based State Reduction for Optimal Planning

Action pruning is one of the most basic techniques for improving a planner's performance. The challenge of preserving optimality while reducing the state space has been addressed by several methods in recent years. In this paper we describe two optimality preserving pruning methods: The first is a generalization of tunnel macros. The second, the main contribution of this paper, is a novel partition-based pruning method. The latter requires the introduction of new automated domain decomposition techniques which are of independent interest. Both methods prune the actions applicable at state s based on the last action leading to s, and both attempt to capture the intuition that, when possible, we should focus on one sub-goal at a time. As we demonstrate, neither method dominates the other, and a combination of both allows us to obtain an even stronger pruning rule. We also introduce a few modifications to A* that utilize properties shared by both methods to find an optimal plan. Our empirical evaluation compares the pruning power of the two methods and their combination, showing good coverage, reduction in running time, and reduction in the number of expansions.
Electricity derivatives and risk management

Electricity spot prices in the emerging power markets are volatile, a consequence of the unique physical attributes of electricity production and distribution. Uncontrolled exposure to market price risks can lead to devastating consequences for market participants in the restructured electricity industry. Lessons learned from the financial markets suggest that financial derivatives, when well understood and properly utilized, are beneficial to the sharing and controlling of undesired risks through properly structured hedging strategies. We review different types of electricity financial instruments and the general methodology for utilizing and pricing such instruments. In particular, we highlight the roles of these electricity derivatives in mitigating market risks and structuring hedging strategies for generators, load serving entities, and power marketers in various risk management applications. Finally, we conclude by pointing out the existing challenges in current electricity markets for increasing the breadth, liquidity and use of electricity derivatives for achieving economic efficiency. q 2005 Elsevier Ltd. All rights reserved.
Tapping Social Capital through E-Mentoring: An Alternative Approach to Women's Career Development

The value of mentoring for career development is widely recognized by researchers and practitioners. For women, being mentored is particularly valuable for their career growth. However, the reality is that professional women continue to encounter challenges men do not, both in finding mentors and in building healthy mentoring relationships. In this chapter, the authors examine how e-mentoring can be used as an alternative tool to address some of the challenges facing women in mentoring relationships and to assist them with their career progression. They conduct an integrative literature review to identify research on e-mentoring in the context of women’s career development, and they identify trends in current e-mentoring research and make recommendations for improving e-mentoring practice. The chapter concludes with directions for future research on e-mentoring.
FPGA Implementation of a Change-Driven Image Processing Architecture for Optical Flow Computation

Optical flow computation has been extensively used for object motion estimation in image sequences. However, the results obtained by most optical flow techniques are as accurate as computationally intensive due to the large amount of data involved. A new strategy for image sequence processing has been developed; pixels of the image sequence that significantly change fire the execution of the operations related to the image processing algorithm. The data reduction achieved with this strategy allows a significant optical flow computation speed up. Furthermore, FPGAs allow the implementation of a custom data-flow architecture specially suited for this strategy. The bases of the change-driven image processing are presented, as well as the hardware custom implementation
Handling very large workloads to effectively partition data warehouses: New approach and experimental study

A data warehouse (DW) is continuously subjected to large and complex workloads of queries. These queries often require very long response time. To reduce the execution cost of these queries, the DW administrator selects optimization structures as horizontal partitioning which offers performances and manageability advantages without consumption of extra storage space. The partitioning selection problem is known NP-Complete. Several approaches have been proposed in the literature to manage the complexity of the problem and generate valuable partitioning schema. The proposed approaches consider a workload of most frequent queries to optimize. However, the majority of them do not take into account the size of the workload which can be very large. A very large workload can drastically increases the time needed to select partitioning schema and the size of the search space. To tackle this problem, we propose a new approach to handle very large workloads in order to partition effectively a DW. We conduct an experimental study on the ABP-1 benchmark to test the effectiveness and scalability of our approach. We have also conducted validation in Oracle 11g.
A UML profile for functional modeling applied to the Molecular Function Ontology

Gene Ontology (GO) is the largest, and steadily growing, resource for cataloging gene products. Naturally, its growth raises issues regarding its structure. Modeling and refactoring big ontologies such as GO is far from being simple. It seems that human-friendly graphical modeling languages, such as the Unified Modeling Language (UML) could be helpful for that task. In the current paper we investigate if UML can be utilized for making the structural organization of the Molecular Function Ontology (MFO), a sub-ontology of GO, more explicit. In addition, we examine if and how using UML can support the refactoring of MFO. We utilize UML and its extension mechanism for the definition of a UML dialect, which is suited for modeling functions and is called Function Modeling Language (FuML). Next, we use FuML for capturing the structure of molecular functions. Finally, we propose and demonstrate some refactoring options for MFO.
A Evolutionary Multiobjective Genetic Algorithm to Solve 0 / 1 Knapsack Problem

The 0/1 Knapsack Problem is very well known and it appears in many real life world with different application. The solution to the multi objective 0/1 Knapsack problem can be viewed as the result of a sequence of decisions. The problem is NP complete and it also generalization of the 0/1 Knapsack problem in which many Knapsack are considered. A evolutionary algorithm for solving multi objective 0/1 Knapsack Problem is introduced in this paper. This algorithm used a genetic algorithm for direct comparison of two solutions. Our motivation for developing a genetic algorithm in this paper in a multi objective optimization framework was that ( a) GA is robust search method. ( b) GA performs the global search. ( c ) GA already work with a population of candidate solutions, which makes them naturally suitable for multi objective problem solving, where this algorithm is used to consider a set of optimal solutions at each iteration. Few numerical experiments are realized using the best and recent algorithm in this paper. Experimental outcome show that the new proposed algorithm performs better.
Digital Dermatitis in Cattle: Current Bacterial and Immunological Findings

Simple Summary Digital dermatitis causes lameness in cattle. Numerous studies have identified multiple bacteria associated with these painful lesions. Several types of a spiral shaped bacteria, Treponema species, are thought to play a role in disease development. Little is known about the immune response to bacteria involved in digital dermatitis. Local inflammatory cells can contribute to the non-healing nature of the disease. Animal models of infection are required to develop effective vaccines and treatments. Abstract Globally; digital dermatitis is a leading form of lameness observed in production dairy cattle. While the precise etiology remains to be determined; the disease is clearly associated with infection by numerous species of treponemes; in addition to other anaerobic bacteria. The goal of this review article is to provide an overview of the current literature; focusing on discussion of the polybacterial nature of the digital dermatitis disease complex and host immune response. Several phylotypes of treponemes have been identified; some of which correlate with location in the lesion and some with stages of lesion development. Local innate immune responses may contribute to the proliferative, inflammatory conditions that perpetuate digital dermatitis lesions. While serum antibody is produced to bacterial antigens in the lesions, little is known about cellular-based immunity. Studies are still required to delineate the pathogenic traits of treponemes associated with digital dermatitis; and other host factors that mediate pathology and protection of digital dermatitis lesions.
The Huashan risk calculators performed better in prediction of prostate cancer in Chinese population: a training study followed by a validation study

The performances of the Prostate Cancer Prevention Trial (PCPT) risk calculator and other risk calculators for prostate cancer (PCa) prediction in Chinese populations were poorly understood. We performed this study to build risk calculators (Huashan risk calculators) based on Chinese population and validated the performance of prostate-specific antigen (PSA), PCPT risk calculator, and Huashan risk calculators in a validation cohort. We built Huashan risk calculators based on data from 1059 men who underwent initial prostate biopsy from January 2006 to December 2010 in a training cohort. Then, we validated the performance of PSA, PCPT risk calculator, and Huashan risk calculators in an observational validation study from January 2011 to December 2014. All necessary clinical information were collected before the biopsy. The results showed that Huashan risk calculators 1 and 2 outperformed the PCPT risk calculator for predicting PCa in both entire training cohort and stratified population (with PSA from 2.0 ng ml−1 to 20.0 ng m). In the validation study, Huashan risk calculator 1 still outperformed the PCPT risk calculator in the entire validation cohort (0.849 vs 0.779 in area under the receiver operating characteristic curve [AUC] and stratified population. A considerable reduction of unnecessary biopsies (approximately 30%) was also observed when the Huashan risk calculators were used. Thus, we believe that the Huashan risk calculators (especially Huashan risk calculator 1) may have added value for predicting PCa in Chinese population. However, these results still needed further evaluation in larger populations.
Cancelling convolutive and additive coloured noises for speech enhancement

In the framework of speech enhancement, many approaches have been developed when the speech signal is only corrupted by additive noise. However, in an auditorium, when echoes appear, spatial transformations between the sources and the microphones must be considered. For this reason, we propose to deal with speech contaminated by both convolutive and additive coloured noise. The two-microphone based noise canceller we present operates as follows: firstly, a prewhitening step is carried out. Secondly, the blind deconvolution method we use makes it possible to estimate the finite impulse responses (FIR), their orders and the variances of the additive noise, which is a great advantage. Then, the filtered versions of speech, estimated by means of a subspace method, are used to retrieve the original speech.
DAVAST: data-centric system level activity visualization

Host-based intrusion detection systems need to be complemented by analysis tools that help understand if malware or attackers have indeed intruded, what they have done, and what the consequences are. We present a tool that visualizes system activities as data flow graphs: nodes are operating system entities such as processes, files, and sockets; edges are data flows between the nodes. Pattern matching identifies structures that correspond to (suspected) malicious and (suspected) normal behaviors. Matches are highlighted in slices of the data flow graph. As a proof of concept, we show how email worm attacks, drive-by downloads, and data leakage are detected, visualized, and analyzed.
Volatility, Jumps, and Predictability of Returns: A Sequential Analysis

In this article we propose a Monte Carlo algorithm for sequential parameter learning for a stochastic volatility model with leverage, nonconstant conditional mean and jumps. We are interested in estimating the time invariant parameters and the nonobservable dynamics involved in the model. Our simple but effective idea relies on the auxiliary particle filter algorithm mixed together with the Markov Chain Monte Carlo (MCMC) methodology. Adding an MCMC step to the auxiliary particle filter prevents numerical degeneracies in the sequential algorithm and allows sequential evaluation of the fixed parameters and the latent processes. Empirical evaluation on simulated and real data is presented to assess the performance of the algorithm. A numerical comparison with a full MCMC procedure is also provided. We also extend our methodology to superposition models in which volatility is obtained by a linear combination of independent processes.
Lymphokine-mediated regulation of the proliferative response of clones of T helper 1 and T helper 2 cells

Murine Th1 and Th2 subsets differ not only in the lymphokines they produce, but also functionally. It is not clear what factors influence the preferential activation of one subset versus the other and what regulatory interactions exist between them. The purpose of this study was to examine the effect of lymphokines produced by clones of Th1 cells (IL-2 and IFN-gamma), Th2 cells (IL-4), and APC (IL-1) on the proliferative response of Th1 and Th2 cells after antigenic stimulation. Activation of both types of clones in the presence of antigen and APC resulted in the acquisition of responsiveness to the proliferative effects of both IL-2 and IL-4, although Th2 cells were more responsive to IL-4 than Th1 cells. Responsiveness of Th1 and Th2 cells to both lymphokines decreased with time after initial antigenic activation; Th1 cells lost their responsiveness to IL-4 more rapidly and to IL-2 more slowly than Th2 cells. IFN-gamma partially inhibited the IL-2 and IL-4-mediated proliferation of Th2, but not Th1 cells. Although the presence of IL-1 was not required for the response of Th1 or Th2 cells to IL-4, its presence resulted in a synergistic effect with IL-2 or IL-4 in Th2 but not in Th1 cells. Both subsets responded to a mixture of IL-2 and IL-4 in synergistic fashion. Delayed addition and wash-out experiments indicated that both IL-2 and IL-4 had to be present simultaneously in order for synergy to occur. These results suggest that Th cell subsets might regulate each other via the lymphokines that they secrete and that the pathways of IL-2 and IL-4 mediated proliferation are interrelated.
Teaching of image synthesis in functional style

We have taught the 3D modelling and image synthesis for computer science students (Master level), exploiting very intensely the functional style of programming/scene description. Although no pure functional language was used, since we wanted to use popular programmable packages, such as POV-Ray, or the interactive modeller/renderer Blender, scriptable in Python, we succeded in showing that typical functional tools, such as higher-order functional objects, compositions and recursive combinations are useful, easy to grasp and to implement. We constructed implicit and parametric surfaces in a generic way, we have shown how to transform (deform) and blend surfaces using functional methods, and we have even found a case where the laziness, implemented through Python generators, turned to be useful.We exploited also some functional methods for the image processing: creation of procedural textures and their transformation.
Spatial-temporal data mining procedure: LASR

This paper is concerned with the statistical development of our spatial-temporal data mining procedure, LASR (pronounced "laser"). LASR is the abbreviation for Longitudinal Analysis with Self-Registration of large- p-small-n data. It was motivated by a study of "Neuromuscular Electrical Stimulation" experiments, where the data are noisy and heterogeneous, might not align from one session to another, and involve a large number of mul- tiple comparisons. The three main components of LASR are: (1) data seg- mentation for separating heterogeneous data and for distinguishing outliers, (2) automatic approaches for spatial and temporal data registration, and (3) statistical smoothing mapping for identifying "activated" regions based on false-discovery-rate controlled p-maps and movies. Each of the components is of interest in its own right. As a statistical ensemble, the idea of LASR is applicable to other types of spatial-temporal data sets beyond those from the NMES experiments.