nils-herrmann/clean_scirepeval huggingface dataset:
Exploring the relationship between worry and impulsivity in military recruits: the role of mindfulness and self-compassion as potential mediators.

When military recruits cannot tolerate the stressful environment in the military, they typically become more impulsive. Impulsivity serves to avoid the stress, or, in other words, to release the pressure and cope. Becoming more impulsive, however, is related to damaging behaviours and unsuccessful coping. This research explored the relationship between worry and impulsivity in armed forces, and the possibility of mindfulness and self-compassion mediating this relationship. Participants (n = 166), who were in the second week of their basic military training, completed questionnaires in mindfulness, self-compassion, worry and impulsivity. Results indicated that worry related positively to impulsivity. Further, the negative relationship of worry with mindfulness and self-compassion mediated the relationship between worry and impulsivity. Findings support the notion that lacking mindfulness and self-compassion, in excessive worriers, may lead to impulsivity. This research concluded that mindfulness and self-compassion may assist military personnel who are exposed to highly stressful environments. Alternative explanations and future directions are discussed.
Pointer Networks

We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence  and Neural Turing Machines , because the number of target classes in each step of the output depends on the length of the input, which is variable. Problems such as sorting variable sized sequences, and various combinatorial optimization problems belong to this class. Our model solves the problem of variable size output dictionaries using a recently proposed mechanism of neural attention. It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output. We call this architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn approximate solutions to three challenging geometric problems – finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem – using training examples alone. Ptr-Nets not only improve over sequence-to-sequence with input attention, but also allow us to generalize to variable size output dictionaries. We show that the learnt models generalize beyond the maximum lengths they were trained on. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete problems.
Orthogonal nonnegative matrix t-factorizations for clustering

Currently, most research on nonnegative matrix factorization (NMF)focus on 2-factor $X=FG^T$ factorization. We provide a systematicanalysis of 3-factor $X=FSG^T$ NMF. While it unconstrained 3-factor NMF is equivalent to it unconstrained 2-factor NMF, itconstrained 3-factor NMF brings new features to it constrained 2-factor NMF. We study the orthogonality constraint because it leadsto rigorous clustering interpretation. We provide new rules for updating $F,S, G$ and prove the convergenceof these algorithms. Experiments on 5 datasets and a real world casestudy are performed to show the capability of bi-orthogonal 3-factorNMF on simultaneously clustering rows and columns of the input datamatrix. We provide a new approach of evaluating the quality ofclustering on words using class aggregate distribution andmulti-peak distribution. We also provide an overview of various NMF extensions andexamine their relationships.
Secure Information Sharing among Autonomous Vehicles in NDN

Autonomous vehicles must communicate with each other effectively and securely to make robust decisions. However, today's Internet falls short in supporting efficient data delivery and strong data security, especially in a mobile ad-hoc environment. Named Data Networking (NDN), a new data-centric Internet architecture, provides a better foundation for secure data sharing among autonomous vehicles. We examine two potential threats, false data dissemination and vehicle tracking, in an NDN-based autonomous vehicular network. To detect false data, we propose a four-level hierarchical trust model and the associated naming scheme for vehicular data authentication. Moreover, we address vehicle tracking concerns using a pseudonym scheme to anonymize vehicle names and certificate issuing proxies to further protect vehicle identity. Finally, we implemented and evaluated our AutoNDN application on Raspberry Pi-based mini cars in a wireless environment.
Gait planning and behavior-based control for statically-stable walking robots

A substantial portion of the Earth is inaccessible to any sort of wheeled mechanism— natural obstacles like large rocks, loose soil, deep ravines, and steep slopes conspire to render rolling locomotion ineffective. Hills, mountains, shores, seabeds, as well as the moon and other planets present similar terrain challenges. In many of these natural terrains, legs are well-suited. They can avoid small obstacles by making discrete contacts and passing up undesirable footholds. Legged mechanisms can climb over obstacles and step across ditches, surmounting terrain discontinuities of body-scale while staying level and stable. To achieve their potential, legged robots must coordinate their leg motions to climb over, step across and walk in natural terrain. These coordinated motions, which support and propel the robot, are called a gait. This thesis develops a new method of gait planning and control that enables statically-stable walking robots to produce a gait that is robust and productive in natural terrain. Independent task-achieving processes, called gait behaviors, establish a nominal gait, adapt it to the terrain, and react to disturbances like bumps and slips. Gait controlled in this way enabled the robot Dante II to walk autonomously in natural terrain, including the volcanic crater of Mount Spurr. This method extends to other walking robots as demonstrated by a generalized hexapod that performs the variety of gaits seen in six-legged insects, as well as aperiodic free gaits. The ability to change gait patterns on-the-fly with continuous, stable motion is a new development that enables robots to behave more like animals in adapting their gait to terrain. Finally, this thesis describes why walking robots need predictive plans as well as reflexive behaviors to walk effectively in the real world. It presents a method of guiding the behavior of a walking robot by planning distinct attributes of the desired gait. This partitioning of gait planning avoids the complexity of high degree-of-freedom motion planning. The ability to plan and foresee changes in gait improves performance while maintaining robust safety and stability. Abstract ii iii Acknowledgment
A Review of Wideband Wide-Angle Scanning 2-D Phased Array and Its Applications in Satellite Communication

In this review, research progress on the wideband wide-angle scanning two-dimensional phased arrays is summarized. The importance of the wideband and the wide-angle scanning characteristics for satellite communication is discussed. Issues like grating lobe avoidance, active reflection coefficient suppression and gain fluctuation reduction are emphasized in this review. Besides, techniques to address these issues and methods to realize the wideband wide-angle scanning phased array are reviewed.
A deep neuro-fuzzy method for multi-label malware classification and fuzzy rules extraction

Modern malware represents an emerging threat not only to individual privacy, but also to organizational security. Over the last few decades, a number of malware families have been codified, differentiated by their behaviour and functionality. The majority of machine learning methods work well with the general benign-malicious classification yet fail to distinguish among many classes. Deep learning is known to be successful for such applications, Deep Neural Networks in particular. There is no way however to extract an understandable model in order to characterize each malware family separately. Thus, the goal of this paper is many-fold: we investigated multinomial malware categorization using static and dynamic behavioural features extracted from Windows Portable Executables (PE32) malware collection as of the end of 2015. Furthermore, we proposed a novel Deep Neuro-Fuzzy architecture, a Hybrid Intelligence approach capable of boosting the performance of conventional Neuro-Fuzzy method targeting rules extraction. It proved capable of achieving a 49.335% better result than classical Neuro-Fuzzy and 7.347% improvement over the same complexity Deep Neural Network (DNN). Moreover, we studied the Deep Neural Network behaviour when handling such non-trivial problems as multinomial malware categorization. We believe that this model contributes to Windows PE32 malware categorization by improving the similarity-based detection of zero-days malware. It can serve as a stepping stone for malware analysts to speed up the detection and dissemination.
ICT and OTs: A model of information and communication technology acceptance and utilisation by occupational therapists

There is evidence to suggest that health professionals are reluctant to accept and utilise information and communication technologies (ICT) and concern is growing within health informatics research that this is contributing to the lag in adoption and utilisation of ICT across the health sector. Technology acceptance research within the field of information systems has been limited in its application to health and there is a concurrent need to develop and gain empirical support for models of technology acceptance within health and to examine acceptance and utilisation issues amongst health professionals to improve the success of information system implementation in this arena. This paper outlines a project that examines ICT acceptance and utilisation by Australian occupational therapists. It describes the theoretical basis behind the development of a research model and the methodology being employed to empirically validate the model using substantial quantitative, qualitative and longitudinal data. Preliminary results from Phase II of the study are presented. The theoretical significance of this work is that it uses a thoroughly constructed research model, with potentially the largest sample size ever tested, to extend technology acceptance research into the health sector.
Sensor Data Modeling for Data Trustworthiness

Wireless sensor networks (WSNs) are installed in the terrain for observing the physical and environmental parameters. The nodes in the network are resource constrained in nature and faces several challenges for producing the data from the unfriendly environment. Large amount of data is generated from WSN and suffers from data fault, inaccuracy and inconsistency. To increase the reliability of application, several data trust management schemes are introduced to ensure the trustworthiness of data in decision making process. Apart from these schemes, in the absence of ground truth, sensor data models are used to find the trustiness of the sensor data. The data generated from the simulation of data model is used as a metric to evaluate the degree of trustiness of sensor data. The existing sensor data models suffer from high energy consumption for data trustiness detection and it becomes inaccurate when the data fault rate is high. In this paper, we are proposing an energy efficient sensor data model for evaluating the sensor data trustworthiness and reconstruct the sensor data in case of any data loss and data fault. The proposed data model is hybrid in nature and it works at low level sensor nodes and also at sink node. Results show that the proposed data model is able to detect the untrustworthy data and gives remedy to untrustworthy and missing data with the help of data reconstruction in an energy efficient way and it is able to identify the events in reliable fashion.
Explaining ERP failure in a developing country: a Jordanian case study

Enterprise resource planning (ERP) systems are increasingly being adopted by organisations in developing countries. As in industrialised countries, this adoption seems beset by significant rates of failure, leading to a large waste of investment and other resources. This study seeks to understand why such ERP failure occurs.  This study moves beyond factor lists to make use of an overall "design—reality gap" model. The model is applied to a case study of ERP failure in a Jordanian manufacturing firm, analysing the situation both before and during ERP implementation through a mix of interviews, observation and document analysis.  The research finds sizeable gaps between the assumptions and requirements built into ERP system design, and the actual realities of the client organisation. It is these gaps – and the failure to close them during implementation – that underlie ERP project failure.  This study shows the relevance and applicability of the design—reality gap model to understanding ERP failure. Further research can be undertaken applying the model to other ERP cases, including case studies of success.  The paper draws conclusions about good practice in ERP implementation relating to both risk identification and risk mitigation, which must be based on closing design—reality gaps. It offers examples of both specific and generic actions that help to achieve this. But it also notes limitations existing in some developing country contexts that may continue to constrain the effective use of enterprise resource planning systems. 2  This study provides a new model for understanding ERP project success and failure, and for practical risk mitigation. The design—reality gap model aims to be comprehensive but also contingent; sensitive to the specific conditions of any individual client organisation.
Automatic web spreadsheet data extraction

Spreadsheets contain a huge amount of high-value data but do not observe a standard data model and thus are difficult to integrate. A large number of data integration tools exist, but they generally can only work on relational data. Existing systems for extracting relational data from spreadsheets are too labor intensive to support ad-hoc integration tasks, in which the correct extraction target is only learned during the course of user interaction.
 This paper introduces a system that automatically extracts relational data from spreadsheets, thereby enabling relational spreadsheet integration. The resulting integrated relational data can be queried directly or can be translated into RDF triples. When compared to standard techniques for spreadsheet data extraction on a set of 100 random Web spreadsheets, the system reduces the amount of human labor by 72% to 92%. In addition to the system design, we present the results of a general survey of more than 400,000 spreadsheets we downloaded from the Web, giving a novel view of how users organize their data in spreadsheets.
A Fuzzy Similarity Based Concept Mining Model for Text Classification

Text Classification is a challenging and a red hot field in the current scenario and has great importance in text categorization applications. A lot of research work has been done in this field but there is a need to categorize a collection of text documents into mutually exclusive categories by extracting the concepts or features using supervised learning paradigm and different classification algorithms. In this paper, a new Fuzzy Similarity Based Concept Mining Model (FSCMM) is proposed to classify a set of text documents into pre defined Category Groups (CG) by providing them training and preparing on the sentence, document and integrated corpora levels along with feature reduction, ambiguity removal on each level to achieve high system performance. Fuzzy Feature Category Similarity Analyzer (FFCSA) is used to analyze each extracted feature of Integrated Corpora Feature Vector (ICFV) with the corresponding categories or classes. This model uses Support Vector Machine Classifier (SVMC) to classify correctly the training data patterns into two groups; i. e., + 1 and – 1, thereby producing accurate and correct results. The proposed model works efficiently and effectively with great performance and high accuracy results. Text Classification; Natural Language Processing; Feature Extraction; Concept Mining; Fuzzy Similarity Analyzer; Dimensionality Reduction; Sentence Level; Document Level; Integrated Corpora Level Processing.
The CanMEDS initiative: implementing an outcomes-based framework of physician competencies.

Outcomes-based education in the health professions has emerged as a priority for curriculum planners striving to align with societal needs. However, many struggle with effective methods of implementing such an approach. In this narrative, we describe the lessons learned from the implementation of a national, needs-based, outcome-oriented, competency framework called the CanMEDS initiative of The Royal College of Physicians and Surgeons of Canada.



We developed a framework of physician competencies organized around seven physician "Roles": Medical Expert, Communicator, Collaborator, Manager, Health Advocate, Scholar, and Professional. A systematic implementation plan involved: the development of standards for curriculum and assessment, faculty development, educational research and resources, and outreach.



Implementing this competency framework has resulted in successes, challenges, resistance to change, and a list of essential ingredients for outcomes-based medical education.



A multifaceted implementation strategy has enabled this large-scale curriculum change for outcomes-based education.
Sentiment analysis using sentence minimization with natural language generation (NLG)

The analysis of feeling is used to define the attitude of a writer in relation to a subject or the appropriate global polarity of a document. The proposed work is to provide a platform in order to visualize the relative analysis of feedback for some particular product. In doing so, instead of the basic truthful information, the analysis will be done based on comments and comments developed from various sources. In this approach, the analysis of feeling at the document level will be carried out taking into account all aspects in the same way using natural language processing techniques. The present unsupervised method is used for sentence minimization that relies on a Stanford-type dependency for extracting information elements and compressed sentences are generated via a Natural language generation engine (NLG). An automatic evaluation of the same is done and F-scores of about 87.51 is achieved.
The Wind Farm Layout Optimization Problem

An important phase of a wind farm design is solving the Wind Farm Layout Optimization Problem (WFLOP), which consists in optimally positioning the turbines within the wind farm so that the wake effects are minimized and therefore the expected power production maximized. Although this problem has been receiving increasing attention from the scientific community, the existing approaches do not completely respond to the needs of a wind farm developer, mainly because they do not address construction and logistical issues. This chapter describes the WFLOP, gives an overview on the existing work, and discusses the challenges that may be overcome by future research.
Simulation of phasor measurement unit (PMU) in MATLAB

The advent of phasor measurement unit in power system is revolutionizing the conventional grid towards smart grid. Phasor measurement units are extremely expensive devices which takes into account many aspects of the power system that are not disclosed by the manufacturer while estimating phasors of currents and voltage. This paper aims to build a laboratory prototype of PMU which can estimate the phasor updating process of a commercial PMU at the benefit of improved measurement accuracy, reduced manufacturing cost and increased timely information.
Sexual Conflict in Human Mating

Despite interdependent reproductive fates that favor cooperation, males and females exhibit many psychological and behavioral footprints of sexually antagonistic coevolution. These include strategies of deception, sexual exploitation, and sexual infidelity as well as anti-exploitation defenses such as commitment skepticism and emotions such as sexual regret and jealousy. Sexual conflict pervades the mating arena prior to sexual consummation, after a mating relationship has formed, and in the aftermath of a breakup. It also permeates many other social relationships in forms such as daughter-guarding, conflict in opposite-sex friendships, and workplace sexual harassment. As such, sexual conflict constitutes not a narrow or occasional flashpoint but rather persistent threads that run through our intensely group-living species.
Using linked data to mine RDF from wikipedia's tables

The tables embedded in Wikipedia articles contain rich, semi-structured encyclopaedic content. However, the cumulative content of these tables cannot be queried against. We thus propose methods to recover the semantics of Wikipedia tables and, in particular, to extract facts from them in the form of RDF triples. Our core method uses an existing Linked Data knowledge-base to find pre-existing relations between entities in Wikipedia tables, suggesting the same relations as holding for other entities in analogous columns on different rows. We find that such an approach extracts RDF triples from Wikipedia's tables at a raw precision of 40%. To improve the raw precision, we define a set of features for extracted triples that are tracked during the extraction phase. Using a manually labelled gold standard, we then test a variety of machine learning methods for classifying correct/incorrect triples. One such method extracts 7.9 million unique and novel RDF triples from over one million Wikipedia tables at an estimated precision of 81.5%.
Cooperation under the Shadow of the Future : Experimental Evidence from Infinitely Repeated Games

While there is an extensive literature on the theory of infinitely repeated games, empirical evidence on how “the shadow of the future” affects behavior is scarce and inconclusive. I simulate infinitely repeated prisoner’s dilemma games in the lab with a random continuation rule. The experimental design represents an improvement over the existing literature by including sessions with finite repeated games as controls and a large number of players per session (which allows for learning without contagion effects). I find that the shadow of the future matters not only by significantly reducing opportunistic behavior, but also because its impact closely follows theoretical predictions.
Underwater image enhancement via dark channel prior and luminance adjustment

Underwater images are degraded mainly by light scattering and color distortion. Light scattering occurs because the light is reflected and deflected multiple times by the suspended particles in the water before reaching the camera. Color distortion is caused due to the absorption degrees vary according to the light wavelength. At last, the underwater images are dominated by a bluish tone. In this paper, we propose a novel underwater image enhancement approach based on dark channel prior and luminance adjustment. The dehazed underwater image obtained by dark channel prior characterizes with low brightness. Then, we utilize a novel luminance adjustment method to enhance image contrast. The enhanced image shows improved global contrast and better preserved image details and edges. Experimental results demonstrate the effectiveness of our method compared to the state-of-the-art methods.