nils-herrmann/clean_scirepeval huggingface dataset:
Human Mobility in a Continuum Approach

Human mobility is investigated using a continuum approach that allows to calculate the probability to observe a trip to any arbitrary region, and the fluxes between any two regions. The considered description offers a general and unified framework, in which previously proposed mobility models like the gravity model, the intervening opportunities model, and the recently introduced radiation model are naturally resulting as special cases. A new form of radiation model is derived and its validity is investigated using observational data offered by commuting trips obtained from the United States census data set, and the mobility fluxes extracted from mobile phone data collected in a western European country. The new modeling paradigm offered by this description suggests that the complex topological features observed in large mobility and transportation networks may be the result of a simple stochastic process taking place on an inhomogeneous landscape.
A low-voltage high-PSRR CMOS PTAT & constant-g/sub m/ reference circuit

The design of a good PTAT current reference plays a very important role in the design of bandgap reference circuits. Using a high-swing low-voltage current mirror, a low-voltage high-PSRR current reference circuit is presented. Simulation results of the circuit confirm its proper operation with a low supply voltage of 1.3V within a wide thermal range of -50degC to +130degC. It shows a constant gm value with 0.5% variation in all process corners
A Systematic Mapping Study on Software Reuse

Software reuse is considered as the key to a successful software development because of its potential to reduce the time to market, increase quality and reduce costs. This increase in demand made the software organizations to envision the use of software reusable assets which can also help in solving recurring problems. Till now, software reuse is confined to reuse of source code in the name of code scavenging. Now a day, software organizations are extending the concepts of software reuse to other life cycle objects as they realized that reuse of source code alone does not save money. The academia has put forward some assets as reusable and presented methods or approaches for reusing them. Also, for a successful software reuse the organizations should assess the value of reuse and keep track on their reuse programs. The other area which is vital for software reuse is the maintenance. Maintenance of reusable software has direct impact on the cost of the software. In this regard, academia has presented a number of techniques, methods, metrics and models for assessing the value of reuse and for maintaining the reusable software.  In our thesis, we investigate on the reusable assets and the methods/ approaches that are put forward by the academia for reusing those assets. Also a systematic mapping study is performed to investigate what techniques, methods, models and metrics for assessing the value of reuse and for maintaining the reused software are proposed and we also investigate their validation status as well.  The databases like IEEE Xplore, ACM digital library, Inspec, Springer and Google scholar were used to search for the relevant studies for our systematic mapping study. We followed basic inclusion criteria along with detailed inclusion/exclusion criteria for selecting the appropriate article.  Through our systematic mapping study, we could summarize the list of 14 reusable assets along with the approaches/methods for reusing them. Taxonomy for assessing the value of reuse and taxonomy for maintaining the reusable software are presented. We also presented the methods/metrics/models/techniques for measuring reuse to assess its value and for maintaining the reusable software along with their validation status and areas in focus.  We conclude that, there is a need for defining a standard set of reusable assets that are commonly accepted by the researchers in the field of software reuse. Most metrics/models/methods/approaches presented for assessing the value of reuse and for maintaining the reuse software are academically validated. Efforts have to be put on industrially validating them using the real data.
Natural Language Understanding and Intelligent Applications

A novel word sense disambiguation (WSD) discriminative model is proposed in this paper to handle long distance sense dependency and multireference lexicon dependency (i.e., the sense of a lexicon might depend on several other non-local lexicons under the same subtree) within the sentence. Many WSD systems only adopt local context to independently decide the sense of each lexicon in a sentence. However, the sense of a target word actually also depends on those structure related sense/lexicons that might be far away from it. Therefore, we propose a supervised approach which integrates structural context (for long distance sense dependency and multi-reference lexicon dependency) with the local context (for local dependency) to handle the problems mentioned above. As the result, the sense of each word is decided not only based on the local lexicons, but also based on various reference sense/lexicons (might be nonlocal) specified by all its associated syntactic subtrees. Experimental results show that the proposed approach significantly outperforms other state-of-art WSD systems.
Natural Language Aggregate Query over RDF Data

Natural language question/answering over RDF data has received widespread attention. Although there have been several studies that have dealt with a small number of aggregate queries, they have many restrictions (i.e., interactive information, controlled question or query template). Thus far, there has been no natural language querying mechanism that can process general aggregate queries over RDF data. Therefore, we propose a framework called NLAQ (Natural Language Aggregate Query). First, we propose a novel algorithm to automatically understand a user’s query intention, which mainly contains semantic relations and aggregations. Second, to build a better bridge between the query intention and RDF data, we propose an extended paraphrase dictionary ED to obtain more candidate mappings for semantic relations, and we introduce a predicate-type adjacent set PT to filter out inappropriate candidate mapping combinations in semantic relations and basic graph patterns. Third, we design a suitable translation plan for each aggregate category and effectively distinguish whether an aggregate item is numeric or not, which will greatly affect the aggregate result. Finally, we conduct extensive experiments over real datasets (QALD benchmark and DBpedia), and the experimental results demonstrate that our solution is effective.
Justice at the millennium: a meta-analytic review of 25 years of organizational justice research.

The field of organizational justice continues to be marked by several important research questions, including the size of relationships among justice dimensions, the relative importance of different justice criteria, and the unique effects of justice dimensions on key outcomes. To address such questions, the authors conducted a meta-analytic review of 183 justice studies. The results suggest that although different justice dimensions are moderately to highly related, they contribute incremental variance explained in fairness perceptions. The results also illustrate the overall and unique relationships among distributive, procedural, interpersonal, and informational justice and several organizational outcomes (e.g., job satisfaction, organizational commitment, evaluation of authority, organizational citizenship behavior, withdrawal, performance). These findings are reviewed in terms of their implications for future research on organizational justice.
Compact Dual-Band Printed Quadrifilar Antennas for UHF RFID/GPS Operations

This letter presents a simple and innovative method for designing and optimizing two circularly polarized printed quadrifilar antennas (PQAs). The PQAs are operated at UHF RFID (902-908 MHz) and GPS (1575 MHz) and intended for use at a portable terminal. This new antenna system comprises two PQAs concentrically arranged, but electrically isolated. Each PQA comprises four inverted-F monopoles arranged orthogonally and excited with a phase difference of 90°.
Smart car parking system prototype utilizing CCTV nodes: A proof of concept prototype of a novel approach towards IoT-concept based smart parking

The use of smart parking systems has become very important especially for metropolitan areas, due to its benefits for the users regarding time, fuel consumption, and frustration when searching for a vacant parking space. This paper offers a review of recent advances in sensing and communication technology concerning smart parking systems. It includes a brief study of the selected topics and provides an implementation process of those selected systems. Moreover, this work proposes a design approach for a smart car parking system prototype based on utilizing Closed Circuit Television (CCTV), and also an algorithm for computer vision detection through simulation environment. The system has been deployed in a simulation environment. Furthermore, the system evaluation demonstrates the effectiveness of our design and implementation of the system and shows promising results. This paper discusses possible issues that might arise when using the system Also, problems and challenges are outlined.
The neuropsychiatry of the cerebellum — insights from the clinic

A central aspect of the cerebellar cognitive affective syndrome is the dysregulation of affect that occurs when lesions involve the ‘limbic cerebellum’ (vermis and fastigial nucleus). In this case series we describe neuropsychiatric disturbances in adults and children with congenital lesions including cerebellar agenesis, dysplasia, and hypoplasia, and acquired conditions including cerebellar stroke, tumor, cerebellitis, trauma, and neurodegenerative disorders. The behaviors that we witnessed and that were described by patients and families included distractibility and hyperactivity, impulsiveness, disinhibition, anxiety, ritualistic and stereotypical behaviors, illogical thought and lack of empathy, as well as aggression and irritability. Ruminative and obsessive behaviors, dysphoria and depression, tactile defensiveness and sensory overload, apathy, childlike behavior, and inability to appreciate social boundaries and assign ulterior motives were also evident. We grouped these disparate neurobehavioral profiles into five major domains, characterized broadly as disorders of attentional control, emotional control, and social skill set as well as autism spectrum disorders, and psychosis spectrum disorders. Drawing on our dysmetria of thought hypothesis, we conceptualized the symptom complexes within each putative domain as reflecting either exaggeration (overshoot, hypermetria) or diminution (hypotonia, or hypometria) of responses to the internal or external environment. Some patients fluctuated between these two states. We consider the implications of these neurobehavioral observations for the care of patients with ataxia, discuss the broader role of the cerebellum in the pathogenesis of these neuropsychiatric symptoms, and revisit the possibility of using cerebellar stimulation to treat psychiatric disorders by enhancing cerebellar modulation of cognition and emotion.
A recommender system based on historical usage data for web service discovery

The tremendous growth in the amount of available web services impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, these text-based recommendation approaches depend mainly on user’s perspective, languages, and notations, which easily decrease recommendation’s efficiency. In this paper, we present an approach in which we take into account historical usage data instead of the text-based analysis. We apply collaborative filtering technique on user’s interactions. We propose and implement four algorithms to validate our approach. We also provide evaluation methods based on the precision and recall in order to assert the efficiency of our algorithms.
Construct validity of the Self-Compassion Scale-Short Form among psychotherapy clients

Construct validity of the Self-Compassion ScaleShort Form among psychotherapy clients Jeffrey A. Hayes, Allison J. Lockard, Rebecca A. Janis & Benjamin D. Locke To cite this article: Jeffrey A. Hayes, Allison J. Lockard, Rebecca A. Janis & Benjamin D. Locke (2016): Construct validity of the Self-Compassion Scale-Short Form among psychotherapy clients, Counselling Psychology Quarterly, DOI: 10.1080/09515070.2016.1138397 To link to this article: http://dx.doi.org/10.1080/09515070.2016.1138397
The Business Model as a Tool of Improving Value Creation in Complex Private Service System-Case : Value Network of Electric Mobility

This paper shows how the concept of business model can be used as an analysis tool when attempting to understand and describe the complexity of the value creation of a developing industry, namely electric mobility. The business model concept is applied as a theoretical framework in action research based and facilitated workshops with nine case companies operating in the field of electric mobility. The concept turned out to be a promising tool in creating a better understanding of the value creation in electric mobility, and thus being a potential framework for the development work for the actors in that field. The concept can help companies to improve cooperation with other players by being a common terminology.
Updating P300: An integrative theory of P3a and P3b

The empirical and theoretical development of the P300 event-related brain potential (ERP) is reviewed by considering factors that contribute to its amplitude, latency, and general characteristics. The neuropsychological origins of the P3a and P3b subcomponents are detailed, and how target/standard discrimination difficulty modulates scalp topography is discussed. The neural loci of P3a and P3b generation are outlined, and a cognitive model is proffered: P3a originates from stimulus-driven frontal attention mechanisms during task processing, whereas P3b originates from temporal-parietal activity associated with attention and appears related to subsequent memory processing. Neurotransmitter actions associating P3a to frontal/dopaminergic and P3b to parietal/norepinephrine pathways are highlighted. Neuroinhibition is suggested as an overarching theoretical mechanism for P300, which is elicited when stimulus detection engages memory operations.
Robot navigation: Review of techniques and research challenges

Robot navigation has multi-spectral applications across a plethora of industries. Being one of the fastest evolving field of Artificial intelligence, it has garnered a lot of speculation and the problem of efficient Robot navigation has become a talking point in the “Artificial Intelligence research community”. Robot Navigation can be thought of as a collection of sub-problems that when solved efficiently will (most likely) give the complete and the most efficient solution. While the main Navigation problem can be divided into numerous smaller ones some of the prevalent (sub)-tasks associated with Robot Navigation are Path Planning, Collision Prevention, Search Algorithm and an appropriate pictorial (graphical) representation of the given environment. Path Planning gives us the most optimal path finding approach while keeping the dynamism of the environment and the objects in mind. Collision Prevention ensures the Robot reaches its goal without colliding with any of the objects present in the environment. Collision Prevention techniques' vitality increases considerably when the number of objects in the input space is large (>10, approximately). Search Algorithms are the functional units of this problem as they determine the path the Robot follows while traveling to its destination. Since the environment greatly affects the choice of technique for all the other tasks it is vital to have a clear, concise and a non-ambiguous representation of the same. The main aim of the paper is to summarise the development of various techniques in the multiple domains of the Robot Navigation problem in the last fifteen years (2000-2015) while stating the scope and the restriction of each of them.
AdaBoost-based face detection for embedded systems

Face detection is a widely studied topic in computer vision, and recent advances in algorithms, low cost processing, and CMOS imagers make it practical for embedded consumer applications. As with graphics, the best cost-performance ratio is achieved with dedicated hardware. In this paper, we design an embedded face detection system for handheld digital cameras or camera phones. The challenges of face detection in embedded environments include an efficient pipeline design, bandwidth constraints set by low cost memory, a need to find parallelism, and how to utilize the available hardware resources efficiently. In addition, consumer applications require reliability which calls for a hard real-time approach to guarantee that processing deadlines are met. Specifically, the main contributions of the paper include: (1) incorporation of a Genetic Algorithm in the AdaBoost training to optimize the detection performance given the number of Haar features; (2) a complexity control scheme to meet hard real-time deadlines; (3) a hardware pipeline design for Haar-like feature calculation and a system design exploiting several levels of parallelism. The proposed architecture is verified by synthesis to Altera’s low cost Cyclone II FPGA. Simulation results show the system can achieve about 75–80% detection rate for group portraits.
MonoPerfCap : Human Performance Capture from Monocular Video WEIPENGXU , AVISHEKCHATTERJEE , MICHAELZOLLHÖFER , HELGERHODIN , DUSHYANTMEHTA

We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.
A comparative analysis of techniques for predicting academic performance

This paper compares the accuracy of decision tree and Bayesian network algorithms for predicting the academic performance of undergraduate and postgraduate students at two very different academic institutes: Can Tho University (CTU), a large national university in Viet Nam; and the Asian Institute of Technology (AIT), a small international postgraduate institute in Thailand that draws students from 86 different countries. Although the diversity of these two student populations is very different, the data-mining tools were able to achieve similar levels of accuracy for predicting student performance: 73/71% for {fail, fair, good, very good} and 94/93% for {fail, pass} at the CTU/AIT respectively. These predictions are most useful for identifying and assisting failing students at CTU (64% accurate), and for selecting very good students for scholarships at the AIT (82% accurate). In this analysis, the decision tree was consistently 3-12% more accurate than the Bayesian network. The results of these case studies give insight into techniques for accurately predicting student performance, compare the accuracy of data mining algorithms, and demonstrate the maturity of open source tools.
Developing an Air Hockey Game in LabVIEW

Air Hockey consists of two players volleying a puck, using mallets, across a low friction table surface. Points are scored when a player lands the puck in the opponent's goal. The game is won by the player with the higher score at the end of the match. Virtual versions of air hockey have been implemented using various game development software. This paper details the implementation of the air hockey game in LabVIEW. This version of the game uses a physics engine and an Artificial Intelligence (AI) player. This effort discusses the steps followed to design the game and the effectiveness of the AI player versus a human player.
Random Forest variable importance with missing data

Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values. Possible solutions are given by imputation methods, complete case analysis and a newly suggested importance measure. However, it is unknown to what extend these approaches are able to provide a reliable estimate of a variables relevance. An extensive simulation study was performed to investigate this property for a variety of missing data generating processes.  Complete case analysis should not be applied as it inappropriately penalized variables that were completely observed. The new importance measure is much more capable to reflect decreased information exclusively for variables with missing values and should therefore be used to evaluate actual data situations. By contrast, multiple imputation allows for an estimation of importances one would potentially observe in complete data situations.
Power Amplifiers and Transmitters for RF and Microwave

The generation of RF/microwave power is required not only in wireless communications, but also in applications such as jamming, imaging, RF heating, and miniature dc/dc converters. Each application has its own unique requirements for frequency, bandwidth, load, power, efficiency, linearity, and cost. RF power is generated by a wide variety of techniques, implementations, and active devices. Power amplifiers are incorporated into transmitters in a similarly wide variety of architectures, including linear, Kahn, envelope tracking, outphasing, and Doherty. Linearity can be improved through techniques such as feedback, feedforward, and predistortion.