Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of sports participation and psychosocial health in children: Results of a cross-sectional study

Abstract Several studies suggest that sports participation is beneficial for psychosocial health. There is, however, only a limited number of studies about the relationship of specific characteristics of sports participation with psychosocial health. The present study investigated associations between characteristics of sports participation and three aspects of psychosocial health, i.e. internalising problems, externalising problems and prosocial behaviour. The examined characteristics of sports participation pertained to individual versus team sports, indoor versus outdoor sports, involvement in competition or not, and contact sports versus non-contact sports. Cross-sectional data were collected from 1768 Dutch children aged 10–12 years who were member of a sports club. These children completed the Movement and Sports Monitor Questionnaire Youth aged 8–12 years (MSMQ) and the Strength and Difficulties Questionnaire (SDQ). Linear multilevel analyses and logistic generalised estimating equation were conducted. Children participating in team sports, outdoor sports, or competition showed fewer internalising problems than children engaged in individual sports, indoor sports, or only training. The associations with internalising problems were stronger for boys than for girls. Children participating in non-contact sports showed fewer externalising problems than children performing non-contact sports as well as contact sports. Children practising indoor sports or non-contact sports showed better prosocial behaviour than children doing outdoor or contact sports. In conclusion, the form of sports participation seemed to matter highly with respect to internalising problems, especially for boys, and, to a lesser extent, with respect to externalising problems and prosocial behaviour. This offers starting points for developing tailor-made sports programmes for children.
Specificity-Aware Ontology Generation for Improving Web Service Clustering

With the expansion of the Internet, the number of available Web services has increased. Web service clustering to identify functionally similar clusters has become a major approach to the efficient discovery of suitable Web services. In this study, we propose a Web service clustering approach that uses novel ontology learning and a similarity calculation method based on the specificity of an ontology in a domain with respect to information theory. Instead of using traditional methods, we generate the ontology using a novel method that considers the specificity and similarity of terms. The specificity of a term describes the amount of domain-specific information contained in that term. Although general terms contain little domain-specific information, specific terms may contain much more domain-related information. The generated ontology is used in the similarity calculations. New logic-based filters are introduced for the similarity-calculation procedure. If similarity calculations using the specified filters fail, then information-retrieval-based methods are applied to the similarity calculations. Finally, an agglomerative clustering algorithm, based on the calculated similarity values, is used for the clustering. We achieved highly efficient and accurate results with this clustering approach, as measured by improved average precision, recall, Fmeasure, purity and entropy values. According to the results, specificity of terms plays a major role when classifying domain information. Our novel ontology-based clustering approach outperforms comparable existing approaches that do not consider the specificity of terms. key words: Web services, Web service clustering, term specificity, ontology learning, service similarity
An investigation of search processes in collaborative exploratory web search

People are often engaged in collaboration in workplaces or daily life due to the complexity of tasks. In the information seeking and retrieval environment, the task can be as simple as fact-finding or a known-item search, or as complex as exploratory search. Given the complex nature of the information needs, exploratory searches may require the collaboration among multiple people who share the same search goal. For instance, students may work together to search for information in a collaborative course project; friends may search together while planning a vacation. There are demands for collaborative search systems that could support this new format of search (Morris, 2013). Despite the recognized importance of understanding search process for designing successful search system (Bates, 1990; M. Hearst, 2009), it is particularly difficult to study collaborative search process because of the complex interactions involved. In this dissertation, I propose and demonstrate a framework of investigating search processes in the collaborative exploratory search. I designed a laboratory-based user study to collect the data, compared two search conditions: individual search and collaborative search as well as two task types through the study. I first applied a novel Hidden Markov Model approach to analyze the search states in the collaborative search process, the results of which provide a holistic picture of the collaborative search process. I then investigated two important components in the collaborative search process – query behaviors and communications. The findings reveal the characteristics of query and communication patterns in the collaborative search. It also suggests that although the collaboration between two people on search did not achieve a higher performance than two individuals, the collaboration indeed make people feel more satisfied with their performance and less stressed. The results of this study not only provide implications for designing effective collaborative search systems, but also show valuable research directions and methodologies for other researchers.
Technical Considerations in Transradial Unprotected Left Main Stem Rotational Atherectomy-Assisted and IVUS-Guided Percutaneous Coronary Intervention Using the 7.5F Eaucath Sheathless Guiding Catheter System

Rotational atherectomy-assisted percutaneous coronary intervention (PCI) on unprotected left main stem (LMS) bifurcation lesions is technically challenging. Intravascular ultrasound (IVUS) has become a standard part of the PCI procedure for the treatment of LMS disease. There is limited experience in performing these cases via a transradial approach using a sheathless guiding catheter (SGC) system. We report a case of a symptomatic octogenarian patient with restrictive angina and significant LMS bifurcation disease, who was successfully treated transradially with the use of the 7.5F Eaucath SGC system and we describe the technical challenges encountered with this strategy.
Are we finally making progress with suicide and self-harm? An overview of the history, epidemiology and evidence for prevention

Ireland’s suicide rate is falling, but suicide and self-harm remain substantial societal problems. It is not possible to predict suicide at the level of the individual but good primary care, good mental health care and good social care all likely reduce risk. In terms of targeted interventions, public health measures, such as paracetamol pack size regulations, have the best evidence base to support them. Despite increased public and professional discussion, much remains to be done to provide effective, coordinated support to those at risk of suicide and those bereaved. In 2016, there were up to 300 different groups providing support. Clearly, a coordinated, effective and compassionate approach is needed, linking community and state resources meaningfully with each other. Approaches rooted outside of core mental health services will be vital: addressing alcohol problems and other addictions, reducing homelessness, reforming the criminal justice system and improving social care.
Wavelet Transform Analysis of Temperature Modulated Gas Sensor Response

The aim of the study was to evaluate whether it is possible to extract the information about the gas concentration despite the influence of humidity. Commercial semiconductor sensor response was examined under the application of a periodic temperature change. The data was collected using measurement protocol for different concentrations of ammonia at specified levels of relative humidity. In this work we focused on the evaluation of the Discrete Wavelet Transform analysis results obtained during experimental investigation of the single semiconductor gas sensor in the presence of humidified atmosphere containing ammonia. The results of the analysis show that proposed method enable the possibility of determine the low concentrations of ammonia.
ECN Marking With Micro-Burst Traffic: Problem, Analysis, and Improvement

In data centers, batching schemes in end hosts can introduce micro-burst traffic into the network. The packet dropping caused by micro-bursts usually leads to severe performance degradations. Therefore, much attention has been paid to avoiding buffer overflow caused by micro-burst traffic. In particular, ECN is widely used in data centers to keep persistent queue occupancy low, so that enough buffer space can be available as headroom to absorb micro-burst traffic. However, we find that current instantaneous-queue-length-based ECN marking scheme may cause problems in another direction — buffer underflow. Specifically, current ECN marking scheme in data centers is easy to trigger spurious congestion signals, which may result in the overreaction of senders and queue length oscillations in switches. Since ECN threshold is low, the buffer may underflow and link capacity is not fully used. In this paper, we reveal this problem by experiments. Besides, we theoretically deduce the amplitude of queue length oscillations. The analysis results indicate that the overreaction of senders is caused by ECN mismarking. Therefore, we propose combined enqueue and dequeue marking (CEDM), which can mark packets more accurately. Through test bed experiments and extensive ns-2 simulations, we show that CEDM can significantly reduce throughput loss and improve the flow completion time.
Computing Word Classes Using Spectral Clustering

Clustering a lexicon of words is a well-studied problem in natural language processing (NLP). Word clusters are used to deal with sparse data in statistical language processing, as well as features for solving various NLP tasks (text categorization, question answering, named entity recognition and others). Spectral clustering is a widely used technique in the field of image processing and speech recognition. However, it has scarcely been explored in the context of NLP; specifically, the method used in this (Meila and Shi, 2001) has never been used to cluster a general word lexicon. We apply spectral clustering to a lexicon of words, evaluating the resulting clusters by using them as features for solving two classical NLP tasks: semantic role labeling and dependency parsing. We compare performance with Brown clustering, a widely-used technique for word clustering, as well as with other clustering methods. We show that spectral clusters produce similar results to Brown clusters, and outperform other clustering methods. In addition, we quantify the overlap between spectral and Brown clusters, showing that each model captures some information which is uncaptured by the other.
An Achievable Rate Region for the K-Receiver Two Nested Groupcast DM Broadcast Channel and a Capacity Result for the Combination Network

Using an order-theoretic approach, a novel achievable rate region is obtained for the $K$-receiver discrete memoryless broadcast channel with two nested messages, one message desired by all receivers and the other desired by a subset of the receivers. When specialized to the combination network this inner bound is shown to achieve the capacity region when all but two receivers desire both messages.
High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields

In this work, we present an open access database for surface and vacancy-formation energies using classical force-fields (FFs). These quantities are essential in understanding diffusion behavior, nanoparticle formation and catalytic activities. FFs are often designed for a specific application, hence, this database allows the user to understand whether a FF is suitable for investigating particular defect and surface-related material properties. The FF results are compared to density functional theory and experimental data whenever applicable for validation. At present, we have 17 506 surface energies and 1000 vacancy formation energies calculation in our database and the database is still growing. All the data generated, and the computational tools used, are shared publicly at the following websites: www.ctcms. nist.gov/~knc6/periodic.html, https://jarvis.nist.gov and https://github.com/usnistgov/jarvis. Approximations used during the high-throughput calculations are clearly mentioned. Using some of the example cases, we show how our data can be used to directly compare different FFs for a material and to interpret experimental findings such as using Wulff construction for predicting equilibrium shape of nanoparticles. Similarly, the vacancy formation energies data can be useful in understanding diffusion related properties.
Object Detection Implementation and Optimization on Embedded GPU System

With the development of deep convolutional neural networks (CNNs), object detection task has achieved great improvement. However, most current CNN based object detection models take huge computation and memory resource and are hard to deploy on the mobile device with limited resource. Therefore, in this paper, we propose a light and fast object detection algorithm running on NVIDIA Jetson TX2, an GPU platform targeted at power constrained mobile applications. Firstly, we mainly use depthwise separable convolution to replace traditional convolution to design a new network architecture. Then, we prune filters from the network we design. By removing whole filters in the network together with their connecting feature maps, the computation and memory are reduced significantly. Afterwards, compared to other methods implemented on the embedded GPU system, we can get higher FPS and use less memory with little accuracy decreased on PASCAL VOC 2007 task.
Vitamin D and Influenza—Prevention or Therapy?

Vitamin D generates many extraskeletal effects due to the vitamin D receptor (VDR) which is present in most tissues throughout the body. The possible role of vitamin D in infections is implied from its impact on the innate and adaptive immune responses. A significant effect is also the suppression of inflammatory processes. Because vitamin D could be acknowledged as a “seasonal stimulus”, as defined by R. Edgar Hope-Simpson, it would be crucial to prove it from a potential easy and cheap prophylaxis or therapy support perspective as far as influenza infections are concerned. The survey of the literature data generates some controversies and doubts about the possible role of vitamin D in the prevention of influenza virus. The most important point is to realise that the broad spectrum of this vitamin’s activity does not exclude such a possibility. According to most of the authors, more randomized controlled trials with effective, large populations are needed to explore the preventive effect of vitamin D supplementation on viral influenza infections.
Free-Viewpoint Relationship Description Based Streaming Systems for Arbitrary View Switching

Recent advances in video processing and transmitting technologies have increased user interest and demand for realistic services. Among realistic services, the free-viewpoint video (FVV) streaming service has been studied. In order to provide the FVV streaming service, it is necessary to distinguish the number of videos and provide adjacent views for view switching. In this paper, we design and propose a structure that enables the client to know the relationship between views in free-viewpoint relationship description (FRD) architecture. The proposed FRD is used to implement the FVV streaming system, and the possibility of rapid new switching is verified.
Structured Knowledge Base as Prior Knowledge to Improve Urban Data Analysis

Urban computing at present often relies on a large number of manually extracted features. This may require a considerable amount of feature engineering, and the procedure may miss certain hidden features and relationships among data items. In this paper, we propose a method to use structured prior knowledge in the form of knowledge graphs to improve the precision and interpretability in applications such as optimal store placement and traffic accident inference. Specifically, we integrate sub-graph feature extraction, sub-knowledge graph gated neural networks, and kernel-based knowledge graph convolutional neural networks as ways of incorporating large urban knowledge graphs into a fully end-to-end learning system. Experiments using data from several large cities showed that our method outperforms the baseline methods.
Done Yet? A Critical Introspective of the Cloud Management Toolbox

With the rapid rise of the cloud computing paradigm, the manual maintenance and provisioning of the technological layers behind it, both in their hardware and virtualized form, became cumbersome and error-prone. This has opened up the need for automated capacity planning strategies in heterogeneous cloud computing environments. However, even with mechanisms to fully accommodate customers and fulfill service-level agreements, providers often tend to over-provision their hardware and virtual resources. A proliferation of unused capacity leads to higher energy costs, and correspondingly, the price for cloud technology services. Capacity planning algorithms rely on data collected from the utilized resources. Yet, the amount of data aggregated through the monitoring of hardware and virtual instances does not allow for a manual supervision, much less data analysis or a correlation and anomaly detection. Current data science advancements enable the assistance of efficient automation, scheduling and provisioning of cloud computing resources based on supervised and unsupervised machine learning techniques. In this work, we present the current state of the art in monitoring, storage, analysis and adaptation approaches for the data produced by cloud computing environments, to enable proactive, dynamic resource provisioning.
Viewport Prediction Method of 360 VR Video Using Sound Localization Information

For 360 VR video, the user's viewport is portion, but the video needs to be sent to 360° spheres. So 360 VR video requires large bandwidth. In this paper, we propose a viewport prediction method to solve this problem. The proposed method predicts the user's viewport by utilizing the location information of the sound sources in 360 VR video. Especially, the proposed method is considered based on MPEG-DASH, and its feasibility is also shown by our head tracking simulation.
Robust Output Tracking Control for Van der Pol Oscillator: A Sliding-Mode Differentiator Approach

Robust output tracking control of Van der Pol oscillator is an important practical problem from an application point of view. An output feedback based robust tracking control strategy is proposed in this work for that purpose. First, a High-Order Sliding-Mode (HOSM) differentiator is used to estimate the oscillator states from the measurable output. Then, the Continuous Singular Terminal Sliding Mode (CSTSM) algorithm is used as a robust tracking conrcoller based on the estimated states. Through numerical simulation, it is shown that the proposed control strategy outperforms a passivity based robust nonlinear tracking controller. Finally, experimental validations are also provided to show the feasibility of the proposed control in real-time.
Acceptance and Commitment Therapy for Trichotillomania: A Randomized Controlled Trial of Adults and Adolescents

The purpose of this study was to examine acceptance and commitment therapy (ACT) as a standalone treatment for trichotillomania in a randomized controlled trial of adults and adolescents. Participants consisted of a community sample of treatment seeking adults and adolescents with trichotillomania. Of the eligible 39 participants randomized into treatment and waitlist groups, 25 completed treatment and were included in the final analysis. Treatment consisted of a 10-session ACT protocol. Multiple mixed models repeated measures analyses were utilized to evaluate changes in trichotillomania symptom severity, daily number of hairs pulled and urges experienced, and experiential avoidance from pretreatment to posttreatment. Findings indicated significant changes in symptom severity and daily hairs pulled, but not daily urges experienced or psychological flexibility. However, psychological flexibility saw a 24.5% decrease in the treatment group and reduced from clinical to subclinical levels on average. This study suggests that ACT alone is an effective treatment for adults and adolescents with trichotillomania. Outcomes appear to be similar to trials that combined ACT and habit reversal training (HRT).
Design of cognitive fog computing for intrusion detection in Internet of Things

Internet of things (IoT) is penetrating into every aspect of our lives including our body, our home and our living environment along with numerous security challenges. With rapidly growing number of connected devices in IoT, the scope for cyber-attack also increases exponentially. Therefore an effective intrusion detection system (IDS) is needed to efficiently detect the attack at faster rate in highly scalable and dynamic IoT environment. In this paper, a novel intrusion detection technique is proposed based on fog computing using Online Sequential Extreme Learning Machine (OS-ELM) which can intelligently interpret the attacks from the IoT traffic. In the proposed system, the existing centralized cloud intelligence in detecting the attack is distributed to local fog nodes to detect the attack at faster rate for IoT application. The distributed architecture of fog computing enables distributed intrusion detection mechanism with scalability, flexibility and interoperability. The analysis of the proposed system proves to be efficient in terms of response time and detection accuracy.