Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-drone control and network self-recovery for flying Ad Hoc Networks

Drones can play important roles in various applications, such as search and rescue, situation awareness in natural disasters, environmental monitoring, and perimeter surveillance. Using a group of small drones has many advantages compared with developing and operating one large drone. In this paper, we propose a multi-drone real-time control scheme based on the Flying Ad-Hoc Network (FANET) for controlling drones that fall outside the communication range of the ground control system. We also propose a fast recovery scheme with neighbor location information in highly dynamic networks. We verified the operation of the proposed schemes using a FANET test bed implementation.
Redefining Therapeutic Success with Virtual Reality Exposure Therapy

The success rate for treating phobias with in vivo exposure is high. Unfortunately, few phobics (less than 15-20%) ever seek treatment. Virtual reality (VR) exposure therapy is also proving to be highly effective. The present surveys assessed 162 students high in fear of spiders. In Study 1, when asked to choose between multisession in vivo exposure vs. multisession VR exposure therapy, 81% chose VR. In Study 2, comparing one-session in vivo versus multisession VR exposure therapy, 89% chose VR. Results suggest that VR exposure therapy may prove valuable for increasing the number of phobics who seek treatment.
wsrbench: An On-Line Tool for Robustness Benchmarking

Testing Web services for robustness is a difficult task. In fact, existing development support tools do not provide any practical mean to assess Web services robustness in the presence of erroneous inputs. Previous works proposed that Web services robustness testing should be based on a set of robustness tests (i.e., invalid Web services call parameters) that are applied in order to discover both programming and design errors. Web services can be classified based on the failure modes observed. In this paper we present and discuss the architecture and use of an on-line tool that provides an easy interface for Web services robustness testing. This tool is publicly available and can be used by both web services providers (to assess the robustness of their Web services code) and consumers (to select the services that best fit their requirements). The tool is demonstrated by testing several Web services available in the Internet.
GenomicScape: An Easy-to-Use Web Tool for Gene Expression Data Analysis. Application to Investigate the Molecular Events in the Differentiation of B Cells into Plasma Cells

DNA microarrays have considerably helped to improve the understanding of biological processes and diseases. Large amounts of publicly available microarray data are accumulating, but are poorly exploited due to a lack of easy-to-use bioinformatics resources. The aim of this study is to build a free and convenient data-mining web site (www.genomicscape.com). GenomicScape allows mining dataset from various microarray platforms, identifying genes differentially expressed between populations, clustering populations, visualizing expression profiles of large sets of genes, and exporting results and figures. We show how easily GenomicScape makes it possible to construct a molecular atlas of the B cell differentiation using publicly available transcriptome data of naïve B cells, centroblasts, centrocytes, memory B cells, preplasmablasts, plasmablasts, early plasma cells and bone marrow plasma cells. Genes overexpressed in each population and the pathways encoded by these genes are provided as well as how the populations cluster together. All the analyses, tables and figures can be easily done and exported using GenomicScape and this B cell to plasma cell atlas is freely available online. Beyond this B cell to plasma cell atlas, the molecular characteristics of any biological process can be easily and freely investigated by uploading the corresponding transcriptome files into GenomicScape.
Are You an Opinion Giver, Seeker, or Both? Re-Examining Political Opinion Leadership in the New Communication Environment

This study revisits concepts of opinion leadership and followership in the current social and media environment and proposes that the two concepts should be reexamined as complementary and interactional rather than as competing. Based on survey data collected in Seoul, Korea, the study proposes four types of individuals depending on how they manage and form opinions on issues: opinion givers/seekers, opinion givers, opinion seekers, and nondiscussants. The study result indicates that those who both give and seek opinions are more connected to various types of media, have higher curating skills, and are more likely to participate in offline and online political activities than others. The implication of opinion givers/seekers in relation to the traditional meaning of opinion leadership will be further discussed.
Interestingness -- Directing Analyst Focus to Significant Data

Faced with a deluge of data, an analyst must ask ``what data records are important? '' This paper answers that question by first defining a continuous spectrum of data record significance: ``known'', ``anomalous'', ``interesting'', ``novel'', and ``noise''. The definition has a geometric interpretation in that the significance of a data record in a predictor system is inversely proportional to it's distance from the decision boundary of the predictor. Meta-analysis of data means that the performance of the predictor is constantly evaluated to detect cues that the model still valid for the current reality of the data it processes. A principled approach to the meta-analysis of data using the preceding definition was outlined and implemented using a predictor scenario. Support vector machine ensembles were used as novelty, prediction and interestingness models. The system was successfully used to rank the significance of data records and to assess the performance of the predictor for increasingly complex toy and real-world data. A ``NOVINT'' plot was introduced as a means of visualizing data record significance and drawing an analyst's attention to significant information. The plot was also shown to be equally useful in providing insight in to both the nature of the data and the performance of the predictor.
Working with compassion : the impact of loving kindness meditation on compassion satisfaction and compassion fatigue among social workers and other mental health workers

Loving kindness meditation is a practice that involves actively cultivating compassion and intentionally directing compassion towards one’s self and towards others. Compassion fatigue refers to burnout and secondary traumatic stress that may result from caregiving, while compassion satisfaction refers to the enjoyment and sense of fulfillment that may come from caregiving. This mixed-methods study explored the impact of one week of a daily ten minute loving kindness meditation practice on mental health clinicians’ levels of compassion satisfaction and compassion fatigue. Eleven participants committed to a daily ten minute loving kindness meditation practice for one week during which they worked with psychotherapy clients. Participants took a pre-test and post-test to measure their levels of compassion satisfaction and compassion fatigue before and after the intervention. Study results indicate that one week of a daily ten minute loving kindness meditation practice yields a statistically significant decrease in compassion fatigue, and has no statistically significant impact compassion satisfaction. While the results of this study are not generalizable due to the small sample size, these findings provide a reference point for future studies. WORKING WITH COMPASSION: THE IMPACT OF LOVING KINDNESS MEDITATION ON COMPASSION SATISFACTION AND COMPASSION FATIGUE AMONG SOCIAL WORKERS AND OTHER MENTAL HEALTH WORKERS A project based upon an independent investigation, submitted in partial fulfillment of the requirements for the degree of Master of Social Work. Dianne Gallo Smith College School for Social Work Northampton, Massachusetts 2016
Adaptive Proactive Reconfiguration: A Technique for Process-Variability- and Aging-Aware SRAM Cache Design

Nanoscale circuits are subject to a wide range of new limiting phenomena making essential to investigate new design strategies at the circuit and architecture level to improve its performance and reliability. Proactive reconfiguration is an emerging technique oriented to extend the system lifetime of memories affected by aging. In this brief, we present a new approach for static random access memory (SRAM) design that extends the cache lifetime when considering process variation and aging in the memory cells using an adaptive strategy. To track the aging in the SRAM cells we propose an on-chip monitoring technique. Our results show the technique as a feasible way to extend the cache lifetime up to 5X.
Regularized Color Demosaicing via Luminance Approximation

In single-sensor digital imaging a color filter array, that is overlaid onto the image sensor, makes color images possible. Incident light rays become band-limited and each sensor element captures either red, green or blue light. Interpolating the missing two color components for each pixel location is known as demosaicing. This paper proposes to firstly derive an estimated luminance image by low-pass filtering the original mosaiced sensor image. In a second step a deconvolution technique re-sharpenes the blurred luminance approximation, so that it has the same spatial resolution as the original - but bandpassed - sensor image. Using the high-resolution luminance approximation the partial RGB colors from the mosaiced sensor image are transformed into a different color space that is more suitable for color interpolation. The new color space consists of least correlated color data, so that intra-channel interpolation errors have a reduced impact on inter-channel alignment, and therefore result into less prominent interpolation artifacts. Demosaicing is performed on the transformed color data separately for each plane, whereby again the luminance approximation, which encodes the aligned gradient direction of all color channels, regularizes the bilinear interpolation. Finally, the result is remapped into the RGB color space to obtain the demosaiced color image. Additionally, correlated multi-channel anisotropic diffusion is applied onto the demosaiced color image to further reduce interpolation artifacts and enable denoising. The proposed algorithm is evaluated and it is concluded that - although the image formation model could be verified - its performance heavily depends on the quality of the luminance approximation, i.e. the deconvolution method.
Connected Dominating Sets in Wireless Sensor Networks

Since there is no fixed infrastructure or centralized management in wireless sensor networks, a Connected Dominating Set(CDS) has been proposed to serve as a virtual backbone. The CDS of a graph representing a network has a significant impact on the efficient design of routing protocols in wireless sensor networks. This problem has been studied extensively, in which all nodes have the same transmission ranges and the size of the dominating sets are considered. In this paper, by the method of constructing the steiner tree, we model a network as an undirected graph and present the CDS which has smaller size. The performance ratio of these algorithms is constant. Furthermore, we show a size relationship between a maximal independent set and a CDS as well as a bound of the maximum number of independent neighbors of a node. The theoretical analysis and simulation results are also presented to verify our approaches.
Wave Propagation Algorithms for Multidimensional Hyperbolic Systems

A class of high resolution multidimensional wave-propagation algorithms is described for general time-dependent hyperbolic systems. The methods are based on solving Riemann problems and applying limiter functions to the resulting waves, which are then propagated in a multidimensional manner. For nonlinear systems of conservation laws the methods are conservative and yield good shock resolution. The methods are generalized to hyperbolic systems that are not in conservation form and to problems that include a “capacity function. ” Several examples are included for gas dynamics, acoustics in a heterogeneous medium, and advection in a stratified flow on curvilinear grids. The software packageCLAWPACKimplements these algorithms in Fortran and is freely available on the Web. One and two space dimensions are discussed here, although the algorithms and software have also been extended to three dimensions.
H∞ control for a class of uncertain Delta operator-formulated systems with pole-assignment constraints and feedback gain variations

Based on linear matrix inequality (LMI), a design method of Hinfin state feedback controller for a class of uncertain delta-operator formulated systems with pole-assignment constraints and feedback gain variations is introduced in this paper. The existing condition of the controller is translated into LMI form. The controller parameters can be obtained by solving the LMIs. When the controller parameters are excursive, the designed controller ensures the resulted closed-loop delta operator-formulated systems asymptotically stable and have the expected Hinfin performance, also the poles of the resulted closed-loop systems are located in the assigned circular disk to satisfy the dynamic characteristics. A numerical example is given to illustrate the effectiveness of the design procedures.
Robust Mixture Modeling with Pearson Type VII Distribution

Mixture of Student t-distribution (MoT) has been widely used to model multivariate data sets with atypical observations, or outliers for robust clustering. In this paper, we developed a novel robust clustering approach by modeling the data sets with mixture of Pearson type VII distribution (MoP). An EM algorithm is developed for the maximum likelihood estimation of the model parameters. Outlier detection criterion is derived from the EM solution. Controlled experimental results on synthetic datasets show that the MoP performs comparably, on average, with the MoT in terms of outlier detection accuracy and out-of-sample log-likelihood, but the MoP is more stable. Furthermore, we compared the performances of the Pearson type VII and the student t mixtures on the classification of several benchmark pattern recognition data sets. The comparison favors the developed Pearson type VII mixtures.
The potential use of online tools for scientific collaboration by biology researchers

Purpose – The purpose of this paper is to discover the research practices of biology researchers and to assess the suitability of the OJAX++ Virtual Research Environment (VRE) for these researchers. Design/methodology/approach – Usability testing was used to evaluate the usability of OJAX++ in relation to biology researchers. Interviews with biology researchers in a large Irish university were conducted to investigate their research information behaviour, to establish user requirements in their discipline and to evaluate the feasibility of using OJAX++ in their research. Findings – The results show that biology researchers used online tools extensively in their research but do not use social networking tools. E-mail and phone conversations are the preferred methods of collaborating with colleagues. The biology researchers found that OJAX++ was easy to use, intuitive and professionally presented but in its present format, OJAX++ does not fit in with current research practices as they do not use Web 2.0 too.
Aspects of Karitiâna Vowels

This article represents a quantitatively oriented study of aspects of oral vowels in Karitiâna, an endangered Amazonian language. First, the basic formant characteristics of oral vowels are described, with attention paid to influences such as preceding place of articulation. Second, the effect of word-level stress on the spectral tilt of oral vowels is discussed. It is also demonstrated that stressed vowels are more displaced in the F1-F2 plane than their unstressed counterparts. Finally, the normalized vowel spaces of eight speakers are presented. These normalized spaces are suggestive of a sociolinguistic pattern meriting further investigation.
Pathology: is it still necessary?

One may wonder what may be the reasons to ask this apparently odd question. Undoubtedly, the main concern is that we are still far away from being able to precisely identify the target population of patients for tailored interventions. It has been repeatedly shown by the results of large clinical trials that the benefit of new therapeutic approaches is relatively small, and that it is most likely confined to defined subgroups of patients, which should be properly identified for the adoption of the new treatment to be truly rewarding in clinical practice [1–4]. What is needed is an accurate assessment of the likelihood of individual patients to benefit from the different therapeutic options that are currently available. Traditionally, we have relied on the morphological and biological features of the tumor to identify patients at different risk of disease progression and for predicting the efficacy of different therapeutic approaches [5]. It is undisputable that this approach is not entirely satisfactory, because it only allows identification of a very limited number of subgroups of patients, characterized by a different risk of tumor progression (low, intermediate and high) and a different responsiveness to endocrine treatments (highly responsive, non-responsive and incompletely responsive) or to targeted interventions (HER2 positive and negative) [6]. Accordingly, we are still very far from a truly individualized therapeutic approach, most likely due to the relative paucity of the available prognostic and predictive parameters, which do not allow a more specific portrait of the individual patient to be drafted. Furthermore, we must admit that the pathological evaluation of breast cancer has shown additional drawbacks, mainly related to the lack of consistency and reproducibility in the assessment of at least some of the prognostic/predictive parameters. This is particularly true for the evaluation of hormone receptor status [4] and of the overexpression/amplification of the HER2 gene [7], where the rate of false-negative and false-positive results is exceedingly high. As a result, a growing sense of distrust towards the role of the traditional pathological characterization of breast cancer has emerged, and pathology is now viewed as a finite and possibly exhausted source of clinically important pieces of information.
Closely related languages, different ways of realizing focus

We investigated how focus was prosodically realized in Taiwanese, Taiwan Mandarin and Beijing Mandarin by monolingual and bilingual speakers. Acoustic analyses showed that all speakers raised pitch and intensity of focused words, but only Beijing Mandarin speakers lowered pitch and intensity of post-focus words. Cross-group differences in duration were mixed. When listening to stimuli from their own language groups, subjects from Beijing had over 80% focus recognition rate, while those from Taiwan had less than 70% recognition rate. This difference is mainly due to presence/absence of post-focus compression. These findings have implications for prosodic typology, language contact and bilingualism.
Uses of Monolingual In-Domain Corpora for Cross-Domain Adaptation with Hybrid MT Approaches

Resource limitation is challenging for crossdomain adaption. This paper employs patterns identified from a monolingual in-domain corpus and patterns learned from the post-edited translation results, and translation model as well as language model learned from pseudo bilingual corpora produced by a baseline MT system. The adaptation from a government document domain to a medical record domain shows the rules mined from the monolingual in-domain corpus are useful, and the effect of using the selected pseudo bilingual corpus is significant.
Amenities, Local Conditions, and Fiscal Determinants of Factor Growth in Rural America

This paper examines how amenities, asset indicators, and fiscal factors influence the growth in factors of production from 1972 to 1999 in the 466 non-metropolitan labor market areas in the continental United States. In developing our model of non-metropolitan factor markets, we combine the emphasis of Brown et al. (2003) on the affect of taxes and public expenditure policy on labor and capital formation with the emphasis of Beeson et al. (2001) on the importance of climate and natural features on localized population growth. We develop our own measure of capital stock in non-metropolitan areas using data from the Census of Manufacturing for 1967, 1972, 1977, 1982, 1987, and 1992. Results indicate that local taxes discourage both employment growth and manufacturing capital formation, but that local public infrastructure investment and the level of local entrepreneurship encourages employment growth. Amenities such as a favorable climate and the presence of surface water encourage the growth of employment, and greater local wealth, as measured by dividend, interest, and rent income, encourages the formation of manufacturing capital stock. Results fail to support an “export base” approach for rural economies where greater manufacturing capital stock encourages greater employment in a region.
The Economics and Law of Sovereign Debt and Default

This paper surveys the recent literature on sovereign debt and relates it to the evolution of the legal principles underlying the sovereign debt market and the experience of the most recent debt crises and defaults. It finds limited support for theories that explain the feasibility of sovereign debt based on either external sanctions or exclusion from the international capital market and more support for explanations that emphasize domestic costs of default. The paper concludes that there remains a case for establishing institutions that reduce the cost of default but the design of such institutions is not a trivial task.
Receivers for Digital Communication in Symmetric Alpha Stable Noise

Wireless communication devices in embedded computational platforms have become increasingly common. As these device become more compact, radio frequency interference (RFI) from other subsystems, such as clocks and buses, becomes more severe and degrades the performance of the communication systems. The interference from these components is impulsive in nature, therefore standard symbol detection techniques based on Gaussian noise statistics are not optimal. In this paper, we investigate methods of mitigating this interference, modeled as symmetric α-stable noise. We propose an approximation to the optimal Bayesian detector and then compare it to suboptimum, but lowercomplexity, pre-filtering techniques. These approaches are seen to provide great benefits over the standard matched filter correlation receiver.
TURKISH STUDENTS’ IMAGES OF SCIENTISTS

This study analyses the drawings of scientists done by Turkish students, primarily to learn how the students have represented scientists and to find out the stereotypical images that they use. The drawings are compared with the results of a similar study conducted twelve years earlier by the same author. Gender characteristics and differences are a focus of interest. The Draw-a-Scientist Test (DAST) was used to record and analyse the images. The sample for the study consisted of 520 students from various grade levels. Their drawings are discussed in terms of three themes: the general use of standard indicators, gender differences in stereotypical images, and implications of the results for teaching and learning science. Key words: Draw-A-Scientist Test (DAST), images of scientists, scientific literacy, values in science education.