Miracle-dz/UniFAR-FTUs huggingface dataset:
Retrieving top-k prestige-based relevant spatial web objects

The location-aware keyword query returns ranked objects that are near a query location and that have textual descriptions that match query keywords. This query occurs inherently in many types of mobile and traditional web services and applications, e.g., Yellow Pages and Maps services. Previous work considers the potential results of such a query as being independent when ranking them. However, a relevant result object with nearby objects that are also relevant to the query is likely to be preferable over a relevant object without relevant nearby objects. The paper proposes the concept of prestige-based relevance to capture both the textual relevance of an object to a query and the effects of nearby objects. Based on this, a new type of query, the Location-aware top-k Prestige-based Text retrieval (LkPT) query, is proposed that retrieves the top-k spatial web objects ranked according to both prestige-based relevance and location proximity. We propose two algorithms that compute LkPT queries. Empirical studies with real-world spatial data demonstrate that LkPT queries are more effective in retrieving web objects than a previous approach that does not consider the effects of nearby objects; and they show that the proposed algorithms are scalable and outperform a baseline approach significantly.
EYELID MOVEMENTS , VISUAL ATTENTION , AND THE DROWSY DRIVING PROBLEM

s for SomnoAlert INFLUENCE OF INTER-INDIVIDUAL DIFFERENCES IN CIRCADIAN FLEXIBILITY ON SUBJECTIVE AND OBJECTIVE SLEEPINESS MEASURES DURING 28-HOURS OF SLEEP DEPRIVATION. Nele Marcoen 1,2 , Sanne Vermorgen 1 , Nathalie Pattyn 1,2 Marie Vandekerckhove 1 , & Olivier Mairesse 1,2,3 1 Department of Biological psychology, Free University of Brussels, Brussels, Belgium 2 Royal Military Academy, Brussels, Belgium 3 Brugmann University Hospital, Sleep laboratory and unit for Chronobiology, Brussels, Belgium
Signal reconstruction in the presence of side information: The impact of projection kernel design

This paper investigates the impact of projection design on the reconstruction of high-dimensional signals from low-dimensional measurements in the presence of side information. In particular, we assume that both the signal of interest and the side information are described by a joint Gaussian mixture model (GMM) distribution. Sharp necessary and sufficient conditions on the number of measurements needed to guarantee that the average reconstruction error approaches zero in the low-noise regime are derived, for both cases when the side information is available at the decoder or at the decoder and encoder. Numerical results are also presented to showcase the impact of projection design on applications with real imaging data in the presence of side information.
Classification of DDoS attack tools and its handling techniques and strategy at application layer

Computer networks basically consist of seven layers in all at different levels. The seventh layer i.e. application layer is responsible to fulfill the user's requests. Distributed Denial of Service Attack (DDoS) is a condition in which upper three layers of any computer network generally stop their jobs to fulfill the request of clients. DDoS attacks at seventh layer have become highly complex to solve. Various companies hire attack developers or purchase attacking tools to pull down the business of their competitors. DDoS attack's developers are continuously adding new features in this weapon which makes application detector unable to identify. This paper deals with the classification of DDoS threats based on abnormal behavior at application layer and provides summarized information about various DDoS Tools. Moreover, it categorizes DDoS attack handling techniques based on monitoring, preventing, detecting, and mitigating concepts. Hence this paper aims to handle the DDoS attack issues at application layer and therefore providing information about loopholes of handling techniques, and hence better understanding for future advancements in this area.
The Effects of School Turnaround in Tennessee’s Achievement School District and Innovation Zones

In recent years, the federal government has invested billions of dollars to reform chronically low-performing schools. To fulfill their federal Race to the Top grant agreement, Tennessee implemented three turnaround strategies that adhered to the federal restart and transformation models: (a) placed schools under the auspices of the Achievement School District (ASD), which directly managed them; (b) placed schools under the ASD, which arranged for management by a charter management organization; and (c) placed schools under the management of a district Innovation Zone (iZone) with additional resources and autonomy. We examine the effects of each strategy and find that iZone schools, which were separately managed by three districts, substantially improved student achievement. In schools under the auspices of the ASD, student achievement did not improve or worsen. This suggests that it is possible to improve schools without removing them from the governance of a school district.
Teachers’ Attitudes Towards the Integration of Disabled Children into Regular Schools

Abstract Regular and resource teachers from New South Wales government and nongovernment schools were surveyed to elicit their attitudes towards the integration of individual disabled chldren, the support services currently provided and the skills considered essential for teachers of mainstreamed atypical students. Differences among sub‐groups of teachers and between teachers and principals who had completed the same questionnaire in a previous study were also examined. Results indicate that teachers’ attitudes to the integration of individual disabled children reflect lack of confidence both in their own instructional skills and in the quality of support personnel currently provided to them. They are positive about integrating only those children whose disabling characteristics are not likely to require extra instructional or management skills on the part of the teacher. However, teachers’ attitudes may be significantly modified by their pre‐service training and the nature of their subsequent professiona.
Cognitive Processes in the Apprehension of Sound as Music

… the psychological study of music involves examination of the relationships between the rules of music and the laws of perception and cognition ins so far as these latter are understood (Davies, 1978: 19) Music comprises a world of human artifacts, auditory objects, tones arranged in various ways. The cognitive psychology of music tries to explain how sequences of sounds are perceived as musical objects. The research presented in Chapter Three established certain reliable relationships between attributes of patterns of tones and the experiences of listeners. However, there remained the question of how these relationships were mediated. The cognitive revolution in general psychology soon found advocates in the psychology of music, as the route to filling this gap in our understanding. There have been several important studies of the cognitive processes involved in the apprehension of sound as music based on the new turn in psychology. Still the most important is the pioneering study by Fred Lerdahl & Ray Jackendoff (1985). There are also several valuable essays by John Sloboda (1983, xxxx). We draw heavily on the work of these authors in this chapter. The method of cognitive psychology is based on the principle that higher order skills are rooted in bodies of knowledge, which can be represented as rules and schemata. They are not responses to stimuli as the behaviourists supposed. Following the well established hypothetico-deductive methodology of the natural sciences, cognitive psychologists propose hypothetical rules, schemata, conventions and so on to explain skilled cognitive and other higher order activities. Cognitive psychologists build up a model to represent unobserved mental processes on the analogy of the cognitive processes of which we are aware, when we are controlling our own actions by reference to rules. This is very like the way that natural scientists, like Darwin, build up models of unobserved processes by 1
Energy-decreasing dynamics in mean-field spin models

We perform a statistical analysis of deterministic energy-decreasing algorithms on mean-field spin models with a complex energy landscape, such as the Sine model and the Sherrington–Kirkpatrick model. We specifically address the following question: in the search for low-energy configurations, which is more favorable (and in which sense)—a quick decrease along the gradient (greedy dynamics) or a slow decrease close to the level curves (reluctant dynamics)? Average time and wideness of the attraction basins are introduced for each algorithm, together with an interpolation among the two, and experimental results are presented for different system sizes. We found that while the reluctant algorithm performs better for a fixed number of trials, the two algorithms become basically equivalent for a given elapsed time due to the fact that the greedy algorithm has a shorter relaxation time which scales linearly with the system size compared to a quadratic dependence for the reluctant algorithm.
Carceral Citizenship: Race, Rights and Responsibility in the Age of Mass Supervision

Scholars have shown how formal processes of legal exclusion coupled with ubiquitous criminal justice contact relegate the largely black poor targets of the carceral state to second-class citizenship. Building upon but departing from this work, we reveal how carceral expansion has not just produced new forms of second-class citizenship for poor black Americans, but an alternate citizenship category and a distinct form of political membership—what we call carceral citizenship. The criminal record does this work through a process we call translation, marking the conventional citizen and making them legible, as a carceral citizen, for governance through institutions of coercion and care. We delineate the features of carceral citizenship and discuss its implications for how we understand the role, force, and consequence of the state in the lives of the raced and criminalized poor.
Hajj crowd simulation based on intelligent agent

Hajj ritual involves many pilgrims from more than one hundred countries with more than two million people every year. This paper presents simulation of the hajj crowd based on the development on intelligent agent. The intelligent agent is able to adapt and show rational behavior, to recognize the environment, and to do action. The concept of intelligent agent is applied to the individual pilgrim to build the crowd behavior. The results showed that hajj crowd simulation is able to demonstrate more realistic pilgrims' behavior for three hajj activities, i.e. thawaf, sa'i, and jumrah. Moreover, subjective evaluation revealed that the developed system can be used for training the pilgrim before they perform actual activities.
Tat Protein Induces Human Immunodeficiency Virus Type 1 (HIV-1) Coreceptors and Promotes Infection with both Macrophage-Tropic and T-Lymphotropic HIV-1 Strains

ABSTRACT Chemokine receptors CCR5 and CXCR4 are the primary fusion coreceptors utilized for CD4-mediated entry by macrophage (M)- and T-cell line (T)-tropic human immunodeficiency virus type 1 (HIV-1) strains, respectively. Here we demonstrate that HIV-1 Tat protein, a potent viral transactivator shown to be released as a soluble protein by infected cells, differentially induced CXCR4 and CCR5 expression in peripheral blood mononuclear cells. CCR3, a less frequently used coreceptor for certain M-tropic strains, was also induced. CXCR4 was induced on both lymphocytes and monocytes/macrophages, whereas CCR5 and CCR3 were induced on monocytes/macrophages but not on lymphocytes. The pattern of chemokine receptor induction by Tat was distinct from that by phytohemagglutinin. Moreover, Tat-induced CXCR4 and CCR5 expression was dose dependent. Monocytes/macrophages were more susceptible to Tat-mediated induction of CXCR4 and CCR5 than lymphocytes, and CCR5 was more readily induced than CXCR4. The concentrations of Tat effective in inducing CXCR4 and CCR5 expression were within the picomolar range and close to the range of extracellular Tat observed in sera from HIV-1-infected individuals. The induction of CCR5 and CXCR4 expression correlated with Tat-enhanced infectivity of M- and T-tropic viruses, respectively. Taken together, our results define a novel role for Tat in HIV-1 pathogenesis that promotes the infectivity of both M- and T-tropic HIV-1 strains in primary human leukocytes, notably in monocytes/macrophages.
Stochastic particle acceleration and synchrotron self-Compton radiation in TeV blazars.

Aims. We analyse the influence of the stochastic particle accelera tion for the evolution of the electron spectrum. We assume that all investigated spectra are generated inside a spherical, homogeneous source and also analyse the synchrotron and inverse Compton emission generated by such an object. Methods. The stochastic acceleration is treated as the diffusion of the particle momentum and is described by the momentum‐diffusion equation. We investigate the stationary and time dependent solutions of the equation for several different evolutionary scenarios. The scenarios are divided into two general classes. First, we analyse a few cases without injection or escape of the particles during th e evolution. Then we investigate the scenarios where we assume continuous injection and simultaneous escape of the particles. Results. In the case of no injection and escape the acceleration process, competing with the radiative cooling, only modifies the i nitial particle spectrum. The competition leads to a thermal or quasi‐thermal distribution of the particle energy. In the case of the inj ection and simultaneous escape the resulting spectra depend mostly on the energy distribution of the injected particles. In the simplest case, w here the particles are injected at the lowest possible energies, the competition b etween the acceleration and the escape forms a power‐law energy distribution. We apply our modeling to the high energy activity of the blazar Mrk 501 observed in April 1997. Calculating the evolution of the electron spectrum self‐consistently we can reproduce the observed spectra well with a number of free parameters that is comparable to or less than in the “classic stationary” one‐zone synchrotron self‐Compton scenario.
Approximate Series and Claim Replicating Problems for a Market with Time Varying Random Volatility

The paper investigates a contingent claim replicating problem for a diiusion market model. It is proposed an approach which does not call to solve the backward parabolic equation, unlike for the classical method, and this approach is applied for a case when the volatility coeecient is random and depends on time. The replicating strategy is decomposed to series of explicit strategies which does not use current estimations of volatility. A mean variance convergence of the series is proved.
Indexing challenges in work place information retrieval

Workplace environments are characterised by fluid collection definition, diversity in document types, and the need to search information from diverse repositories and file systems. Workplace retrieval systems cover a large variety of document formats, document genres, and languages, such as English and Danish and professional jargons (Abrol et al., 2001). Most workplace search technologies offer advanced indexing algorithms and search facilities, and there is an attempt to automate as much as possible, including indexing (Mahon, Hourican, Gilchrist, 2001). When indexing is carried out manually, it is often the document producers that perform the indexing task rather than professional indexers such as information specialists and librarians. Altogether, these characteristics provide another framework for indexing and information retrieval compared to traditional bibliographical retrieval systems in library settings from where we have most knowledge about the indexing process.
Detection of flock movement in spatio-temporal database using clustering techniques - An experience

In this paper, moving flock patterns are mined from spatio- temporal datasets by incorporating a clustering algorithm. A flock is defined as the set of data that move together for a certain continuous amount of time. Finding out moving flock patterns using clustering algorithms is a potential method to find out frequent patterns of movement in large trajectory datasets. In this approach, SPatial clusteRing algoRithm thrOugh sWarm intelligence (SPARROW) is the clustering algorithm used. The advantage of using SPARROW algorithm is that it can effectively discover clusters of widely varying sizes and shapes from large databases. Variations of the proposed method are addressed and also the experimental results show that the problem of scalability and duplicate pattern formation is addressed. This method also reduces the number of patterns produced.
Toward a more accurate estimate of the prevalence of hepatitis C in the United States

Data from the 2003‐2010 National Health and Nutrition Examination Survey (NHANES) indicate that about 3.6 million people in the United States have antibodies to the hepatitis C virus, of whom 2.7 million are currently infected. NHANES, however, excludes several high‐risk populations from its sampling frame, including people who are incarcerated, homeless, or hospitalized; nursing home residents; active‐duty military personnel; and people living on Indian reservations. We undertook a systematic review of peer‐reviewed literature and sought out unpublished presentations and data to estimate the prevalence of hepatitis C in these excluded populations and in turn improve the estimate of the number of people with hepatitis C in the United States. The available data do not support a precise result, but we estimated that 1.0 million (range 0.4 million‐1.8 million) persons excluded from the NHANES sampling frame have hepatitis C virus antibody, including 500,000 incarcerated people, 220,000 homeless people, 120,000 people living on Indian reservations, and 75,000 people in hospitals. Most are men. An estimated 0.8 million (range 0.3 million‐1.5 million) are currently infected. Several additional sources of underestimation, including nonresponse bias and the underrepresentation of other groups at increased risk of hepatitis C that are not excluded from the NHANES sampling frame, were not addressed in this study. Conclusion: The number of US residents who have been infected with hepatitis C is unknown but is probably at least 4.6 million (range 3.4 million‐6.0 million), and of these, at least 3.5 million (range 2.5 million‐4.7 million) are currently infected; additional sources of potential underestimation suggest that the true prevalence could well be higher. (Hepatology 2015;62:1353–1363)
Who Blocks Who: Simultaneous clothing segmentation for grouping images

Clothing is one of the most informative cues of human appearance. In this paper, we propose a novel multi-person clothing segmentation algorithm for highly occluded images. The key idea is combining blocking models to address the person-wise occlusions. In contrary to the traditional layered model that tries to solve the full layer ranking problem, the proposed blocking model partitions the problem into a series of pair-wise ones and then determines the local blocking relationship based on individual and contextual information. Thus, it is capable of dealing with cases with a large number of people. Additionally, we propose a layout model formulated as Markov Network which incorporates the blocking relationship to pursue an approximately optimal clothing layout for group people. Experiments demonstrated on a group images dataset show the effectiveness of our algorithm.
Phase-optimized K-SVD for signal extraction from underdetermined multichannel sparse mixtures

We propose a novel sparse representation for heavily underdetermined multichannel sound mixtures, i.e., with much more sources than microphones. The proposed approach operates in the complex Fourier domain, thus preserving spatial characteristics carried by phase differences. We derive a generalization of K-SVD which jointly estimates a dictionary capturing both spectral and spatial features, a sparse activation matrix, and all instantaneous source phases from a set of signal examples. This dictionary can be used to extract the learned signal from a new input mixture. The method is applied to the challenging problem of ego-noise reduction for robot audition. We demonstrate its superiority relative to conventional dictionary-based techniques using real-room recordings.
Extraction of Informative Expressions from Domain-specific Documents

What kinds of lexical resources are helpful for extracting useful information from domain-specific documents? Although domain-specific documents contain much useful knowledge, it is not obvious how to extract such knowledge efficiently from the documents. We need to develop techniques for extracting hidden information from such domain-specific documents. These techniques do not necessarily use state-of-the-art technologies and achieve deep and accurate language understanding, but are based on huge amounts of linguistic resources, such as domain-specific lexical databases. In this paper, we introduce two techniques for extracting informative expressions from documents: the extraction of related words that are not only taxonomically related but also thematically related, and the acquisition of salient terms and phrases. With these techniques we then attempt to automatically and statistically extract domain-specific informative expressions in aviation documents as an example and evaluate the results.
Displacement prediction of landslide based on PSOGSA-ELM with mixed kernel

The prediction of landslide displacement is essential for carrying out to improve the disaster warning system and reduce casualties and property losses. This study applies a novel neural network technique, extreme learning machine (ELM) with kernel function, to landslide displacement prediction problem. However, the generalization performance of ELM with kernel function depends closely on the kernel types and the kernel parameters. In this paper, we use a convex combination of Gaussian kernel function and polynomial kernel function in ELM, which may use these two types of kernel functions' advantages. In order to avoid blindness and inaccuracy in parameter selection, a novel hybrid optimization algorithm based on the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) is used to optimize the regularization parameter C, the Gaussian kernel parameter γ, the polynomial kernel parameter q and the mixing weight coefficient η. The performance of our model is verified through two case studies in Baishuihe landslide and Yuhuangge landslide.