Miracle-dz/UniFAR-FTUs huggingface dataset:
Guiding robots through wireless location positioning

Indoor Positioning Systems (IPS) track objects in buildings. Examples of tagged objects are patients or equipment in a hospital whilst discovered objects could be people in burning buildings or soldiers on a battlefield. Ultra Wide Band (UWB) location determination aims at delivering high positional accuracy in harsh industrial environments that cause problems for traditional systems due to electromagnetic interference. UWB systems can calculate the location of tags which are designed to be mounted on assets or worn by a person. They transmit UWB signals that are received by sensors which contain an array of antenna and ultra-wideband radio receivers. The data from these sensors combined with dedicated software uses algorithms to work out the angle of arrival (AOA) of the UWB signal from the tag which is then compared to the time difference of arrival (TDOA). The robotics industry can benefit from advanced location detection systems. We examine here the use of ultra wide band technology in tracking items such as mobile robots.
Reference-Dependent Stochastic User Equilibrium with Endogenous Reference Points

We consider the application of reference-dependent consumer choice theory to traffic assignment on transportation networks. Route choice is modelled based on random utility maximisation with systematic utility embodying loss aversion for the travel time and money expenditure attributes. Stochastic user equilibrium models found in the literature have considered exogenously given reference points. The paper proposes a model where reference points are determined consistently with the equilibrium flows and travel times. The referencedependent stochastic user equilibrium (RDSUE) is defined as the condition where (i) no user can improve her utility by unilaterally changing path, (ii) each user has as reference point the current travel time and the money expenditure of one of the available paths, and (iii) if each user updates the reference point to her current path the observed path flows do not change. These conditions are formally equivalent to a multi-class stochastic equilibrium where each class is associated with a path and has as reference point the current state on the path, and the number of users in each class equals the current flow on the path. The RDSUE is formulated as a fixed point problem in the path flows. Existence of RDSUE is guaranteed under usual assumptions. A heuristic algorithm based on the method of successive averages is proposed to solve the problem. The model is illustrated by two numerical examples, one relates to a two-link network and another to the Nguyen-Dupuit network. A reference-dependent route choice model calibrated on stated preference data is used. The second example serves also to demonstrate the algorithm. The impact on the equilibrium of different assumptions on the degree of loss aversion with respect to the travel time attribute are investigated.
Design Tradeoffs for Radiation Detection Sensor Networks

The detection of hazardous radiation sources is difficult for several reasons. Sensors detect when photons, generated randomly by sources, strike crystals and so sensors don’t get continuous information streams. Radiation emanates from threats but also from walls, the ground and medical patients undergoing radiation treatment. There may be multiple, mobile, evasive sources. The number of parameters is so large that fundamental tradeoffs in designing sensor networks for the problem are unclear. This paper reports on analyses of basic tradeoffs such as mobile versus static sensors; few large sensors versus several small ones; unlimited communication range and bandwidth versus limited range and bandwidth between sensors. The paper restricts attention to detecting sources in a two-dimensional area. Understanding basic tradeoffs such as the reduction in detection time that accrues from increased speed of mobile sensors within a 2D field helps in designing systems for more complex settings such as urban areas with buildings and roads.
University of Southern Denmark Development of a novel enzyme-linked immunosorbent assay targeting a neo-epitope generated by cathepsin-mediated turnover of type III collagen and its application in chronic obstructive pulmonary disease

A high level of extracellular matrix (ECM) turnover characterizes several lung diseases with fibrotic features. Type III collagen is one of the most abundant collagens in lung parenchyma, and cathepsins play a role in lung pathology, being responsible for tissue remodeling. In this study, we explore the diagnostic features of neo-epitope fragments of type III collagen generated by cathepsins that could reflect the pathological tissue turnover in patients with different diseases. A novel enzyme-linked immunosorbent assay (ELISA) measuring cathepsins B, L, S and K -generated type III collagen fragments (C3C) was developed for assessment in serum and plasma. The assay was biologically validated in serum from patients with chronic obstructive pulmonary disease (COPD). Serological levels of C3C were significantly elevated in patients with COPD compared to healthy controls (p = 0.0006). Levels of C3C in serum and heparin plasma of COPD patients had a highly significant correlation (R = 0.86, p<0.0001). The data suggests that the C3C fragment is elevated in patients with COPD compared to healthy controls.
A framework for visual saliency detection with applications to image thumbnailing

We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first retrieve the most similar images to the target image; secondly, we build a simple classifier and we use it to generate saliency maps. Finally, we refine the maps and we extract thumbnails. We show that in spite of its simplicity, our framework outperforms state-of-the-art approaches. Another advantage is its ability to deal with visual pop-up and application/task-driven saliency, if appropriately annotated images are available.
Homeostatic dysregulation proceeds in parallel in multiple physiological systems

An increasing number of aging researchers believes that multi‐system physiological dysregulation may be a key biological mechanism of aging, but evidence of this has been sparse. Here, we used biomarker data on nearly 33 000 individuals from four large datasets to test for the presence of multi‐system dysregulation. We grouped 37 biomarkers into six a priori groupings representing physiological systems (lipids, immune, oxygen transport, liver function, vitamins, and electrolytes), then calculated dysregulation scores for each system in each individual using statistical distance. Correlations among dysregulation levels across systems were generally weak but significant. Comparison of these results to dysregulation in arbitrary ‘systems’ generated by random grouping of biomarkers showed that a priori knowledge effectively distinguished the true systems in which dysregulation proceeds most independently. In other words, correlations among dysregulation levels were higher using arbitrary systems, indicating that only a priori systems identified distinct dysregulation processes. Additionally, dysregulation of most systems increased with age and significantly predicted multiple health outcomes including mortality, frailty, diabetes, heart disease, and number of chronic diseases. The six systems differed in how well their dysregulation scores predicted health outcomes and age. These findings present the first unequivocal demonstration of integrated multi‐system physiological dysregulation during aging, demonstrating that physiological dysregulation proceeds neither as a single global process nor as a completely independent process in different systems, but rather as a set of system‐specific processes likely linked through weak feedback effects. These processes – probably many more than the six measured here – are implicated in aging.
On Secure Data Management in Health-Care Environment

Health-care management systems are of great relevance because they provide an easy and quick management of all aspects regarding a patient, not necessarily medical. Furthermore, there are more and more cases of pathologies in which diagnosis and treatment can be only carried out by using medical imaging techniques. With an ever-increasing prevalence, medical images are directly acquired in or converted into digital form, for their storage as well as subsequent retrieval and processing. Similarly, text interpreting medical image with other associated patient information, are included into or acquired directly in digital form and stored. It is easy to note that greater is the amount of clinical data available to the specialists, acquired for example on previously treated cases, and better is the quality and the rapidity of diagnosis and treatment. It is therefore important, if not essential, to share easily and quickly data and clinical experiences. However, such sharing among different entities, whether public or private, is a non-trivial task, since such information are always subjected to restrictions related to privacy laws and to health insurance regulations. To the best of our knowledge, in literature there are no systems which allows the rapid and secure sharing of clinical experiences among different entities. We propose a fully distributed system which allows secure sharing of all information (both medical and not) necessary for patients management. With our system, each node belonging to it, can access quickly, easily and in a secure way to a considerable amount of data, thereby improving the speed and quality of clinical evaluation performed by the physician.
Barriers to Teachers' Adoption and Use of Technology-Supported Learner-Centered Pedagogies

Problem-based learning (PBL), a teaching/learning approach that relies on ill-structured problems to provide opportunities for student learning, can involve technology as a tool for acquiring, manipulating, and communicating information. In this session, we outline the professional development activities we have used over the last three years to support teachers’ implementation of problem-based learning in their classrooms. Based on teacher interviews and PBL implementation data, we describe strategies that have worked, as well as those that haven’t, and outline steps taken to confront the various barriers teachers encounter as they attempt to incorporate learnercentered pedagogies, supported by technology, within traditional classroom practice.
Combined low level and high level features for out-of-vocabulary word detection

This paper addresses the issue of Out-Of-Vocabulary (OOV) word detection in Large Vocabulary Continuous Speech Recognition (LVCSR) systems. We propose a method inspired by confidence measures, that consists in analyzing the recognition system outputs in order to automatically detect errors due to OOV words. This method combines various features based on acoustic, linguistic, decoding graph and semantics. We evaluate separately each feature and we estimate their complementarity. Experiments are conducted on a large French broadcast news corpus from the ESTER evaluation campaign. Results show good performance in real conditions: the method obtains an OOV word detection rate of 43%-90% with 2.5%-17.5% of false detection. Index Terms: OOV word detection, confidence measures, speech recognition
Inferring lexical and grammatical structure from sequences

In a wide variety of sequences from various sources, from music and text to DNA and computer programs, two different but related kinds of structure can be discerned. First, some segments tend to be repeated exactly, such as motifs in music, words or phrases in text, identifiers and syntactic idioms in computer programs. Second, these segments interact with each other in variable but constrained ways. For example, in English text only certain syntactic word classes can appear after the word 'the' many parts of speech (such as verbs) are necessarily excluded. This paper shows how these kinds of structure can be inferred automatically from sequences. We begin with an example that both illustrates the utility of inferring the kinds of structure we seek and shows what our techniques can do. Next we present an efficient and non-obvious algorithm for identifying exact repetitions-including nested repetitions-in time which is linear with the length of the sequence. Then we describe a very simple algorithm for identifying interactions between sequence elements. The focus of this paper is on how these two algorithms can work together, for their combination is far more powerful than either alone. We show how they combine to generate the kind of structure sought in the original motivating example. Although the two methods work well together on many simple examples, the results frequently conflict with intuition in the inference of branching structure. The minimum description length principle seems to provide the only satisfactory general approach.
All Frames Created Equal are Not Identical: On the Structure of Kahneman and Tversky's Framing Effects

This paper revisits Daniel Kahneman and Amos Tversky’s work on framing. It shows how various conventions involved in economic theory allow for the establishment of different equivalence relations between pairs of problems in framing experiments. Following on from this, an exegesis of their comments on these experiments is conducted. This concerns the relation between their theoretical explanation using prospect theory and the positive/normative distinction involved in models of individual behaviors. Throughout, a methodological framework is developed with a distinction between identity, equivalence and equality (borrowed from philosopher Craig Dilworth) for a critical analysis of the relation between external frames (the empirical structure of a decision problem) and internal frames (the psychological representation of the decision problem by decision makers).
Numerical Flow Optimization in Dense Wireless Sensor Networks

We use a vector field model to formulate flow of information at every point of a dense wireless sensor network. The magnitude of this vector field represents the density of communication activity and its direction is toward the flow of information at each point. We present a general method for flow optimization in a wireless sensor network by minimizing the p-norm of the information flow vector field subject to the basic flow constraints, that is the flow conservation law and boundary constraints. We have called this problem the p-norm flow optimization, and use Sequential Quadratic Programming to solve it. The p-norm flow optimization shows interesting properties for different values of p. For p close to 1, the information routes resemble the geometric shortest paths while as p gets larger, there will be more load balancing effects in the flow. In this work we focus on the numerical analysis of the p-norm flow problem. For this we have to approximate the continuous flow problem by a discrete problem which adds some inaccuracy to the solutions. Therefore, we propose an algorithm to continuously compensate the error in the information flow vector field and avoid error accumulation in the system. We also discuss ways with which we can support different network geometries in the numerical analysis.
Poverty Persistence in Sweden

This paper analyzes the persistence of poverty in Sweden using a hazard rate model based on multiple spells. The model also accounts for unobserved heterogeneity and possibly endogenous initial conditions. We estimate the model on a large representative Swedish panel data set, LINDA, for the years 1991 to 2001. The data contains precise information on household disposable income obtained from individual tax files. Poverty is defined using information on annual minimum needs standards determined by the Swedish National Board of Health and Welfare. The data indicates that poverty rates are highest for immigrants, especially refugee immigrants, and for households with children. Further, poverty rates declined, both for natives and for immigrants, between 1991 and 2001, partly as a result of improved labor market conditions. The empirical results suggest that there is significant negative duration dependence in both exit and entry hazard rates. Moreover, the transition rates are significantly affected by immigrant status, educational attainment, labor market conditions, age, and family status. Accounting for multiple spells shows that for two-parent families with two children who are represented by a male person, 44 percent of native households that falls into poverty at any given point in time remain poor in five or more out of the next ten years. For refugee and non-refugee households, the figures are 62 percent and 50 percent, respectively.
Dependency Parser for Telugu Language

In Telugu language sentence if we change the word order its meaning was not changed whereas in English if we change the word order the meaning was changed. So Telugu is morphologically rich so it is very difficult to develop syntactic parsers for these types of languages. To construct a dependency parser for such type of languages it will require a good morphology based Parts of Speech (POS) tagger. We used good POS tagger it has a good performance on Telugu sentences. The present work describes the steps to developing the dependency parser for the Telugu language. Bottom up approach was applied to a sentence for developing a dependency parser. Using this approach most of words were correctly assigned to its karakas and sentences were parsed correctly. The syntactic parser has many applications they are question-answering system, information retrieval, information extraction and language translation.
Statistical approach towards malware classification and detection

Anti-virus companies receive extensive quantities of malware variants daily; therefore, it is essential to automatically classify them into their corresponding malware family. Here, we apply an efficient statistical approach to identify and render critical malicious patterns into malware families, which are essential elements of automated classification of known and unknown malware variants in large quantities. Critical malicious patterns are the most frequent basic blocks, which are present most often in one specific malware family, and comparatively less in all other malware families. By computing the distribution frequency of each distinct basic block residing in all the malware families, the importance of being a potential representative of a critical malicious pattern for a specific malware family is measured. This value is carefully computed by considering the population of each malware family, and the distribution frequency ratio of every distinct basic block among the different malware families. The results show that known and unknown malware variants can be effectively and accurately classified into their related malware family using this approach.
Diffusion Tensor Magnetic Resonance Imaging in Multiple Sclerosis

Multiple sclerosis (MS), a demyelinating disease, occurs principally in the white matter (WM) of the central nervous system. Conventional magnetic resonance imaging (MRI) is sensitive to some, but not all, brain changes associated with MS. Diffusionweighted imaging (DWI) provides information about water diffusion in tissue and diffusion tensor MRI (DT‐MRI) about fiber direction, allowing for the identification of WMabnormalities that are not apparent on conventional MRI images. These techniques can quantitatively characterize the local microstructure of tissues. MS‐associated disease processes lead to regions characterized by an increased amount of water diffusion and a decrease in the anisotropy of diffusion direction. These changes have been found to produce different patterns in MS patients presenting different courses of the disease. Changes in water diffusion may allow examination of the type, appearance, enhancement, and location of lesions not readily visible by other means. Ongoing studies of MSare integrating conventional MRI and DT‐MRI measures with connectivity‐based regional assessment, aiming to provide a better understanding of the nature and the location of WM lesions. This integration and the development of novel image‐processing and visualization techniques may improve the understanding of WM architecture and its disruption in MS. This article presents a brief history of DWI, its basic principles and applications in the study of MS, a review of the properties and applications of DT‐MRI, and their use in the study of MS. In addition, this article illustrates the methodology for the analysis of DT‐MRI in ongoing studies of MS.
The WaveVideo system and network architecture: design and implementation

Wireless video in acceptable quality is only possible by following an end-to-end approach. WaveVideo is an integrated, adaptive video coding architecture designed for heterogeneous wireless networks. It includes basic video compression algorithms based on wavelet transformations, an efficient channel coding, a filter architecture for receiver-based media scaling, and error-control methods to adapt video transmissions to the wireless environment. Using a joint source/channel coding approach, WaveVideo offers a high degree of error tolerance on noisy channels, still being competitive in terms of compression. Adaptation to channel conditions and user requirements is implemented on three levels. The coding itself features spatial and temporal measures to conceal transmission errors. Additionally, the amount of introduced error-control information is controlled by feedback. The video stream coding, applied to multicast capable networks, can serve different user needs efficiently at the same time by scaling the video stream in the network according to receivers’ quality requirements. The WaveVideo architecture is unique in terms of its capability to use QoS mapping and adaptation functions across all network nodes providing the same uniform interface.
Evaluating the Relationships between Transportation Infrastructure and Regional Economic Activity : Evidence from the Port of Abidjan in West Africa

This research paper aims to study the correlation between the transportation infrastructure and regional economic activity. It emphasizes on the seaport activity and the activities of the different services sectors. By comparing trends between them and analyzing the causality relationships between the port traffic and the other economic sectors, our study tends to present how the activity of the Port of Abidjan could have a decisive effect not only on the local economy but also on West Africa regional economy especially in the WAEMU zone. To meet our objectives, correlation analysis and statistical test tools Eviews and other techniques have been run with data provided by local transport agencies and port authority. By doing so, our research study found that there is existing correlation between port activity as an economic infrastructure and the activities generated by the other services sectors and its contribution can accelerate the regional economic growth.