Miracle-dz/UniFAR-FTUs huggingface dataset:
Neighbourhood monitoring for decentralised coordination in multi-agent systems: A case-study

Decentralized coordination of multi-agents requires that every agent reliably and efficiently disseminates its state to neighbours through a wireless network. If dissemination is unreliable, safety issues may ensue. Unfortunately, the broadcast service of wireless network is efficient but unreliable (e.g., IEEE 802.11). The Neighbourhood Monitoring Protocol (NMP) is an efficient and scalable protocol that assures a reliable state dissemination between mobile agents, under some conditions of channel utilization. NMP runs on top of IEEE 802.11. In this paper we evaluate NMP with a specific decentralized collision avoidance algorithm based on the GRP policy. The algorithm is particularly challenging because it accommodates an arbitrary number non-holonomic agents. We show that NMP allows the system to scale well and provides a very high state delivery ratio even if it operates on the unreliable broadcast service like 802.11. Doing so, NMP assures the correct state information to the collision avoidance algorithm.
Artificial neural networks: an emerging new technique

The artificial neural network is at the heart of an emerging technique, which many academicians and practitioners are using very productively due to its high performance in addressing complex problems. Although the ANN has not yet reached its full potential, the technique has demonstrated the capability of enhancing performance in a broad range of problems. This article presents an overview of the artificial neural network, a taxonomy of its learning paradigm, and its application areas.
Considering a Voice of the Body for Adult Transformative Learning Theory

Unknowingly, much of the population of the Western World are thinking machines who live and learn isolated from somatic experiences. They distrust their bodies in the learning process and are stuck living out unquestioned realities of embodied socioculturalism and rationalism which guide decision making, learning and ways of being. Considering a voice of the body involved delving into the physical dimension of somatic transformative learning from the bodily-lived experiences of seven women who were in a first-level experiential Nia® dancing and movement training program. Together, these women offered a voice of the body offering that phases of transformative learning was moving through processes of being stuck, self-allowing, coming to awareness and being connected within the context of learning by experience. Transformative learning processes were (re)living and trying-on felt-meanings of the lifeworld mirrored within personal experiencing spaces and the social experiencing space of the training. Engaging the human body in transformative
Insights into finding a mismatch through the structure of a mispaired DNA bound by a rhodium intercalator

We report the 1.1-Å resolution crystal structure of a bulky rhodium complex bound to two different DNA sites, mismatched and matched in the oligonucleotide 5′-(dCGGAAATTCCCG)2-3′. At the AC mismatch site, the structure reveals ligand insertion from the minor groove with ejection of both mismatched bases and elucidates how destabilized mispairs in DNA may be recognized. This unique binding mode contrasts with major groove intercalation, observed at a matched site, where doubling of the base pair rise accommodates stacking of the intercalator. Mass spectral analysis reveals different photocleavage products associated with the two binding modes in the crystal, with only products characteristic of mismatch binding in solution. This structure, illustrating two clearly distinct binding modes for a molecule with DNA, provides a rationale for the interrogation and detection of mismatches.
Crises and Recoveries in an Empirical Model of Consumption Disasters

We estimate an empirical model of consumption disasters using a new panel data set on personal consumer expenditure for 24 countries and more than 100 years, and study its implications for asset prices. The model allows for permanent and transitory effects of disasters that unfold over multiple years. It also allows the timing of disasters to be correlated across countries. Our estimates imply that the average disaster reaches its trough after 6 years, with a peak-to-trough drop in consumption of about 30%, but that roughly half of this decline is reversed in a subsequent recovery. Uncertainty about consumption growth increases dramatically during disasters. Our estimated model generates a sizable equity premium from disaster risk, but one that is substantially smaller than in models in which disasters are permanent and instantaneous. It yields new predictions for the dynamics of risk-free interest rates, the term structure of interest rates, and the pricing of short-term versus long-term risky assets. The persistence of consumption declines in our model implies that a large value of the intertemporal elasticity of substitution is necessary to explain stock-market crashes at the onset of disasters.
Efficient Multicast Routing on BCube-Based Data Centers

Multicast group communication has many advantages in data centers and thus is widely used by many applications. It can efficiently reduce the network traffic and improve the application throughput. For the multicast application in data centers, an essential problem is how to find a minimal multicast tree, which has been proved to be NP-hard. In this paper, we propose an approximation tree-building method for the minimal multicast problem, named HD （Hamming Distance）-based multicast tree. Consider that many new network structures have been proposed for data centers. We choose three representative ones, including BCube, FBFLY, and HyperX, whose topological structures can be regarded as the generalized hypercube. Given a multicast group in BCube, the HD-based method can jointly schedule the path from each of receiver to the only sender among multiple disjoint paths; hence, it can quickly construct an efficient multicast tree with the low cost. The experimental results demonstrate that our method consumes less time to construct an efficient multicast tree, while considerably reduces the cost of the multicast tree compared to the representative methods. Our approach for BCube can also be adapted to other generalized hypercube network structures for data centers after minimal modifications.
INRIA LEAR-TEXMEX: Video Copy Detection Task

In this paper we present the results of our experiments in the Trecvid'10 copy detection task and introduce the components of our system. In particular, we describe the recent approximate nearest neighbor search method we used to index the hundreds millions of audio descriptors. Our system obtained excellent accuracy and localization results, achieving the best performance on a few transformations, and this with a single kind of image descriptor. Moreover, the analysis evidences that our system can be significantly improved.
THE EFFECTIVENESS OF COMPUTER-BASED INSTRUCTION IN TEACHING COMMUNITY SIGNS TO STUDENTS WITH INTELLECTUAL DISABILITY

This study examined whether the effectiveness of computer-based instruction-which includes peer tutoring-in teaching community signs to students with intellectual disability. Four students (one male and three female) with intellectual disability, from eight to thirteen years of age, participated in this study. The results indicated that CBI was effective in teaching community signs to students with intellectual disability.
Glass ceilings, glass cliffs or new worlds?: Revisiting gender and accounting

Purpose - The purpose of this paper is to set out some contextual issues in relation to the achievements of women accountants and academics. Design/methodology/approach - This is a discussion paper that uses academic and professional experiences as a basis from which to draw out an agenda for further study. Findings - It is found that, despite changes over the last 30 years, the lived experience of women in accounting and academia is evidence that their academic and professional opportunities are still constrained. Two papers in this special issue offer feminist approaches to enrich study in the area, and it is argued that gender is an element that should be embedded in research in accounting. Research limitations/implications - This editorial is a selective review and is not intended as a comprehensive review of research in the field. Practical implications - The paper seeks to re-ignite more research in the field, using a richer range of approaches. Originality/value - The paper sets an agenda for research and provides an argument as to why the agenda needs to be pursued.
Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level, the output feature representation from the top level of the network can be directly fed into a subsequent classifier for pixel-based classification. As a matter of fact, by carefully addressing the practical demands in RS applications and designing the input? output levels of the whole network, we have found that DL is actually everywhere in RS data analysis: from the traditional topics of image preprocessing, pixel-based classification, and target recognition, to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding.In this technical tutorial, a general framework of DL for RS data is provided, and the state-of-the-art DL methods in RS are regarded as special cases of input-output data combined with various deep networks and tuning tricks. Although extensive experimental results confirm the excellent performance of the DL-based algorithms in RS big data analysis, even more exciting prospects can be expected for DL in RS. Key bottlenecks and potential directions are also indicated in this article, guiding further research into DL for RS data.
Discovering anomaly on the basis of flow estimation of alert feature distribution

A challenge faced by many system administrators in utilizing the intrusion detection system IDS is to sift out genuine alerts buried with overwhelming alerts of benign activities generated by the IDS, especially for IDS deployed in large networks. Existing methods propose to identify the real alerts to aid the administrators. In our paper, we extend the idea of filtering irrelevant alerts based on alert volumes. And we formulate the flow estimation of abrupt changes in feature distribution caused by anomalies, by computing Kullback-Leibler distance of alert feature values under observation in comparison with a reference distribution, which is the mixture of a distribution drawing a tread from historical alerts, and a distribution derived from expertise provided by administrators. Experimental studies on the Defense Advanced Research Projects Agency dataset as well as real-life data gathered from the IDS of a large network show that our method is able to distinguish and highlight genuine anomalies arising from the tremendous number of intrusion alerts, including different kinds of attacks and network failures. Application of this technique to alerts greatly helps the administrators in identifying real alerts and then reduces the alert load in the future. Copyright © 2013 John Wiley & Sons, Ltd.
Ef?cient Query Evaluation over Temporally Correlated Probabilistic Streams

Many real world applications such as sensor networks and other monitoring applications naturally generate probabilistic streams that are highly correlated in both time and space. Query processing over such streaming data must be cognizant of these correlations, since they can significantly alter the final query results. Several prior works have suggested approaches to handling correlations in probabilistic databases. However those approaches are either unable to represent the types of correlations that probabilistic streams exhibit, or can not be applied directly because of their complexity. In this paper, we develop a system for managing and querying such streams by exploiting the fact that most real-world probabilistic streams exhibit highly structured Markovian correlations. Our approach is based on the previously proposed framework of viewing probabilistic query evaluation as inference over graphical models; we show how to efficiently construct graphical models for the common stream processing operators, and how to efficiently perform inference over them in an incremental fashion. We also present an algorithm for operator ordering that judiciously rearranges the query operators to make the query evaluation tractable, if possible given the query. Our extensive experimental evaluation illustrates the advantages of exploiting the structured nature of correlations in probabilistic streams.
Query expansion using gaze-based feedback on the subdocument level

We examine the effect of incorporating gaze-based attention feedback from the user on personalizing the search process. Employing eye tracking data, we keep track of document parts the user read in some way. We use this information on the subdocument level as implicit feedback for query expansion and reranking. We evaluated three different variants incorporating gaze data on the subdocument level and compared them against a baseline based on context on the document level. Our results show that considering reading behavior as feedback yields powerful improvements of the search result accuracy of ca. 32% in the general case. However, the extent of the improvements varies depending on the internal structure of the viewed documents and the type of the current information need.
Excess healthcare costs of a large waterborne outbreak in Finland

Background: The economic effects of waterborne outbreaks have rarely been reported. A large waterborne outbreak occurred in the town of Nokia in Finland in 2007 with half of the population in the contaminated area suffering from gastroenteritis. We studied the healthcare costs of this outbreak. Methods: Healthcare costs were studied using register data from the Nokia Health Care Centre, data collected in the regional university hospital, and data from laboratory register on stool samples. Results: Total excess healthcare costs were EUR 354,496, which is approximately EUR 10 per resident of Nokia. There were 2052 excess visits because of gastroenteritis in Nokia Health Care Centre, 403 excess episodes in the university hospital, and altogether over 2000 excess stool samples due to the outbreak. Care in the Nokia Health Care Centre accounted for 44% and care in the university hospital for 42% of the excess healthcare costs while stool samples accounted for only 10%. Conclusions: Despite the high morbidity, the total cost was low because most patients had a relatively mild illness. The situation would have been worse if the microbes involved had been more hazardous or if the financial situation of the community had been worse. Prevention of waterborne outbreaks is important, as there is a risk of severe short- and long-term health effects and substantial health-economic costs.
Distal balloon deflation technique: A new method to facilitate entry of balloon catheter, stent, and guiding catheter to distal lesion

We occasionally encountered difficult cases of balloon and stent delivery to the distal lesion because of narrow stent strut, severe calcification, or tortuosity of the proximal section. We describe a new technique to deliver balloon, stent, and guiding catheter to the distal lesion using a distal balloon deflation. This technique offers a potential alternative for cases in which the delivery of balloon and stent is difficult. © 2009 Wiley‐Liss, Inc.
Music Therapy, Acquired Brain Injury and Interpersonal Communication Competencies

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SecVLC: Secure Visible Light Communication for Military Vehicular Networks

Technology coined as the vehicular ad hoc network (VANET) is harmonizing with Intelligent Transportation System (ITS) and Intelligent Traffic System (ITF). An application scenario of VANET is the military communication where vehicles move as a convoy on roadways, requiring secure and reliable communication. However, utilization of radio frequency (RF) communication in VANET limits its usage in military applications, due to the scarce frequency band and its vulnerability to security attacks. Visible Light Communication (VLC) has been recently introduced as a more secure alternative, limiting the reception of neighboring nodes with its directional transmission. However, secure vehicular VLC that ensures confidential data transfer among the participating vehicles, is an open problem. In this paper, we propose a secure military light communication protocol (SecVLC) for enabling efficient and secure data sharing. We use the directionality property of VLC to ensure that only target vehicles participate in the communication. Vehicles use full-duplex communication where infra-red (IR) is utilized to share a secret key and VLC is used to receive encrypted data. We experimentally demonstrate the suitability of SecVLC in outdoor scenarios at varying inter-vehicular distances with key metrics of interest, including the security, data packet delivery ratio and delay.
On a Molecular Based Q-Tensor Model for Liquid Crystals with Density Variations

In this article, we study the new Q-tensor model previously derived from Onsager's molecular theory by Han \textit{et al.} [Arch. Rational Mech. Anal. , 215.3 (2014), pp. 741-809] for static liquid crystal modeling. Taking density and Q-tensor as order parameters, the new Q-tensor model not only characterizes important phases while capturing density variation effects, but also remains computationally tractable and efficient. We report the results of two numerical applications of the model, namely the isotropic--nematic--smectic-A--smectic-C phase transitions and the isotropic--nematic interface problem, in which density variations are indispensable. Meanwhile, we show the connections of the new Q-tensor model with classical models including generalized Landau-de Gennes models, generalized McMillan models, and the Chen-Lubensky model. The new Q-tensor model is the pivot and an appropriate trade-off between the classical models in three scales.
Interference alignment for MIMO power line communications

Interference alignment (IA) is a multiplexing gain optimal linear precoding strategy that has been extensively studied in the context of multi-input multi-output (MIMO) wireless interference networks. In this paper, we study the applicability of IA for MIMO power line communication (PLC) in interference scenarios. In particular, we consider different MIMO PLC networks to study the feasibility of IA as well as evaluate the performance in terms of sum-rate for these setups. To this end, we exploit minimum interference leakage (Min-IL) and maximum signal to interference plus noise ratio (Max-SINR) algorithms that were earlier proposed for IA in the context of MIMO wireless interference networks. Our results show that IA can be equally applicable to the MIMO PLC in interference scenarios as well as the sum-rate of the PLC networks can be significantly improved through the exploitation of Max-SINR algorithm. It is found that at high signal to noise ratio (SNR) the performance gain in terms of sum-rate with IA over orthogonal transmission techniques is around 30% for a 3-user 2 × 2 MIMO PLC network.
Dissociation Is Associated with Emotional Maltreatment in a Sample of Traumatized Women with a History of Child Abuse

Theories of dissociation emphasize that symptoms of dissociation are correlated with traumatic events. Although the association of dissociative symptoms and retrospective reports of child abuse with a focus on mainly sexual and physical abuse has been well documented, investigation of the contribution of emotional or psychological types of maltreatment to the prediction of dissociation has been neglected to a great extent. The aim of this study was to determine the differential impact of different types of maltreatment on dissociative symptoms in a sample of 203 female residential patients treated for posttraumatic stress disorder linked to child maltreatment. Moreover, it was examined whether the link between dissociation and child maltreatment is direct or indirect. Subjects completed questionnaires on child maltreatment, posttraumatic stress, and dissociative symptoms. Although all types of maltreatment were related to dissociative symptoms, emotional abuse was the strongest and most direct predictor of dissociation in multivariate hierarchical analyses with the influence of other trauma types being confounded by emotional abuse. This study highlights the importance of emotional types of maltreatment for the genesis of dissociative symptoms in patients with posttraumatic stress disorder.
Prevalence, associated factors, and control level of asthma symptoms among adolescents in Northern Jordan

Abstract Objective To investigate the prevalence, associated factors, and control level of asthma in Jordanian high school students. Methods A descriptive, comparative, cross sectional design was used and a cluster sample of 2691 students (mean age = 14.5 years, 51.0% girls), drawn from eight randomly selected public high schools in Northern Jordan, participated in the study. Each student had the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire completed in the classroom between February and May, 2015. Results The prevalence of recent wheezing in the last 12 months was 11.7% whereas the prevalence of diagnosed asthma was 6.2%. About 49% of students experienced cough all the time, and 33.5% experienced waking up at night due to wheezing some of the time. About 30.9% of students reported moderate to big exercise-induced asthma. The mean total Asthma Control Test (ACT) score was 5.85 (SD = 3.56) with all of students reporting uncontrolled asthma during the last month. Higher rates of asthma symptoms were reported by females, 10th graders, and students with negative family history. Importantly, students with diagnosed asthma or recent wheezing reported higher rates of ever smoking tobacco vs. non-asthmatics (p < 0.000); dual (18.6% vs. 9.8%), cigarettes only (11.2% vs. 7.3%), and waterpipe only (18.0% vs. 14.7%). Conclusion Overall, students with asthma or wheezing had increased rates of tobacco smoking. Policies need to be set and enforced to provide a better environment for these youth, especially making all schools smoke-free zones. A multifaceted, comprehensive awareness and management program is required in schools to control and manage asthma symptoms.