nils-herrmann/clean_scirepeval huggingface dataset:
Maximization of the Traction Forces in a 2WD Electric Vehicle

A new control strategy for obtaining the maximum traction force of electric vehicles with individual rear-wheel drive is presented. A sliding-mode observer is proposed to estimate the wheel slip and vehicle velocity under unknown road conditions by measuring only the wheel speeds. The proposed observer is based on the LuGre dynamic friction model and allows the maximum transmissible torque for each driven wheel to be obtained instantaneously. The maximum torque can be determined at any operating point and road condition, thus avoiding wheel skid. The proposed strategy maximizes the traction force while avoiding tire skid by controlling the torque of each traction motor. Simulation results using a complete vehicle model under different road conditions are presented to validate the proposed strategy.
Why people buy virtual items in virtual worlds with real money

Virtual worlds, such as Second Life and Everquest, have grown into virtual game communities that have economic potential. In such communities, virtual items are bought and sold between individuals for real money. The study detailed in this paper aims to identify, model and test the individual determinants for the decision to purchase virtual items within virtual game communities. A comprehensive understanding of these key determinants will enable researchers to further the understanding of player behavior towards virtual item transactions, which are an important aspect of the economic system within virtual games and often raise one of the biggest challenges for game community operators. A model will be developed via a mixture of new constructs and established theories, including the theory of planned behavior (TPB), the technology acceptance model (TAM), trust theory and unified theory of acceptance and use of technology (UTAUT). For this purpose the research uses a sequential, multi-method approach in two phases: combining the use of inductive, qualitative data from focus groups and expert interviews in phase one; and deductive, quantitative survey data in phase two. The final model will hopefully provide an impetus to further research in the area of virtual game community transaction behavior. The paper rounds off with a discussion of further research challenges in this area over the next seven years.
Cross-Sectional Study of Malnutrition and Associated Factors among School Aged Children in Rural and Urban Settings of Fogera and Libo Kemkem Districts, Ethiopia

Little information is available on malnutrition-related factors among school-aged children ≥5 years in Ethiopia. This study describes the prevalence of stunting and thinness and their related factors in Libo Kemkem and Fogera, Amhara Regional State and assesses differences between urban and rural areas.



In this cross-sectional study, anthropometrics and individual and household characteristics data were collected from 886 children. Height-for-age z-score for stunting and body-mass-index-for-age z-score for thinness were computed. Dietary data were collected through a 24-hour recall. Bivariate and backward stepwise multivariable statistical methods were employed to assess malnutrition-associated factors in rural and urban communities.



The prevalence of stunting among school-aged children was 42.7% in rural areas and 29.2% in urban areas, while the corresponding figures for thinness were 21.6% and 20.8%. Age differences were significant in both strata. In the rural setting, fever in the previous 2 weeks (OR: 1.62; 95% CI: 1.23-2.32), consumption of food from animal sources (OR: 0.51; 95% CI: 0.29-0.91) and consumption of the family's own cattle products (OR: 0.50; 95% CI: 0.27-0.93), among others factors were significantly associated with stunting, while in the urban setting, only age (OR: 4.62; 95% CI: 2.09-10.21) and years of schooling of the person in charge of food preparation were significant (OR: 0.88; 95% CI: 0.79-0.97). Thinness was statistically associated with number of children living in the house (OR: 1.28; 95% CI: 1.03-1.60) and family rice cultivation (OR: 0.64; 95% CI: 0.41-0.99) in the rural setting, and with consumption of food from animal sources (OR: 0.26; 95% CI: 0.10-0.67) and literacy of head of household (OR: 0.24; 95% CI: 0.09-0.65) in the urban setting.



The prevalence of stunting was significantly higher in rural areas, whereas no significant differences were observed for thinness. Various factors were associated with one or both types of malnutrition, and varied by type of setting. To effectively tackle malnutrition, nutritional programs should be oriented to local needs.
Big data analytics for transportation: Problems and prospects for its application in China

Transportation, as a means for moving goods and people between different locations, is a vital element of modern society. In this paper, we discuss how big data technology infrastructure fits into the current development of China, and provide suggestions for improvement. We discuss the current situation of China's transportation system, and outline relevant big data technologies that are being used in the transportation domain. Finally we point out opportunities for improvement of China's transportation system, through standardisation, integration of big data analytics in a national framework, and point to the future of transportation in China and beyond.
CPE virtualization by unifying NFV, SDN and Cloud technologies

This paper will describe CPE virtualization solution for home and enterprise environments by unifying Network Function Virtualization (NFV), Software Defined Networking (SDN) and Cloud technologies in Internet providers' networks and data centers. The goal of this solution approach is to reduce operational and capital costs, increase network and service flexibility for providers and to broaden service offering and experience for the end-users. This is achieved by leveraging state of the art technologies that provide automation, flexibility and simplify operations. Possible applications, implementation and potentials will be studied throughout this paper.
Co-design of deep neural nets and neural net accelerators for embedded vision applications

Deep Learning is arguably the most rapidly evolving research area in recent years. As a result it is not surprising that the design of state-of-the-art deep neural net models proceeds without much consideration of the latest hardware targets, and the design of neural net accelerators proceeds without much consideration of the characteristics of the latest deep neural net models. Nevertheless, in this paper we show that there are significant improvements available if deep neural net models and neural net accelerators are co-designed. This paper is trimmed to 6 pages to meet the conference requirement. A longer version with more detailed discussion will be released afterwards.
Assessment of arterial stiffness, a translational medicine biomarker system for evaluation of vascular risk.

Stiffening of the large arteries is a common feature of aging and is exacerbated by a number of disorders such as hypertension, diabetes, and renal disease. Arterial stiffening is recognized as an important and independent risk factor for cardiovascular events. This article will provide a comprehensive review of the recent advance on assessment of arterial stiffness as a translational medicine biomarker for cardiovascular risk.



The key topics related to the mechanisms of arterial stiffness, the methodologies commonly used to measure arterial stiffness, and the potential therapeutic strategies are discussed. A number of factors are associated with arterial stiffness and may even contribute to it, including endothelial dysfunction, altered vascular smooth muscle cell (SMC) function, vascular inflammation, and genetic determinants, which overlap in a large degree with atherosclerosis. Arterial stiffness is represented by biomarkers that can be measured noninvasively in large populations. The most commonly used methodologies include pulse wave velocity (PWV), relating change in vessel diameter (or area) to distending pressure, arterial pulse waveform analysis, and ambulatory arterial stiffness index (AASI). The advantages and limitations of these key methodologies for monitoring arterial stiffness are reviewed in this article. In addition, the potential utility of arterial stiffness as a translational medicine surrogate biomarker for evaluation of new potentially vascular protective drugs is evaluated.



Assessment of arterial stiffness is a sensitive and useful biomarker of cardiovascular risk because of its underlying pathophysiological mechanisms. PWV is an emerging biomarker useful for reflecting risk stratification of patients and for assessing pharmacodynamic effects and efficacy in clinical studies.
Supporting Homework Compliance in Cognitive Behavioural Therapy: Essential Features of Mobile Apps

Cognitive behavioral therapy (CBT) is one of the most effective psychotherapy modalities used to treat depression and anxiety disorders. Homework is an integral component of CBT, but homework compliance in CBT remains problematic in real-life practice. The popularization of the mobile phone with app capabilities (smartphone) presents a unique opportunity to enhance CBT homework compliance; however, there are no guidelines for designing mobile phone apps created for this purpose. Existing literature suggests 6 essential features of an optimal mobile app for maximizing CBT homework compliance: (1) therapy congruency, (2) fostering learning, (3) guiding therapy, (4) connection building, (5) emphasis on completion, and (6) population specificity. We expect that a well-designed mobile app incorporating these features should result in improved homework compliance and better outcomes for its users.
EARNING S YNTAX AND L EXICON

We propose a neural language model capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and better language modeling. Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic information. On the other hand, tree-structured recursive networks usually require additional structural supervision at the cost of human expert annotation. In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model. In our model, the gradient can be directly back-propagated from the language model loss into the neural parsing network. Experiments show that the proposed model can discover the underlying syntactic structure and achieve state-of-the-art performance on word/character-level language model tasks.
Image processing based Feature extraction of Bangladeshi banknotes

Counterfeit currency is a burning question throughout the world. The counterfeiters are becoming harder to track down because of their rapid adoption of and adaptation with highly advanced technology. One of the most effective methods to stop counterfeiting can be the widespread use of counterfeit detection tools/software that are easily available and are efficient in terms of cost, reliability and accuracy. This paper presents a core software system to build a robust automated counterfeit currency detection tool for Bangladeshi bank notes. The software detects fake currency by extracting existing features of banknotes such as micro-printing, optically variable ink (OVI), water-mark, iridescent ink, security thread and ultraviolet lines using OCR (Optical Character recognition), Contour Analysis, Face Recognition, Speeded UP Robust Features (SURF) and Canny Edge & Hough transformation algorithm of OpenCV. The success rate of this software can be measured in terms of accuracy and speed. This paper also focuses on the pros and cons of implementation details that may degrade the performance of image processing based paper currency authentication systems.
Creative Learning and Innovative Teaching Final Report on the Study on Creativity and Innovation in Education in the EU Member States

EU policies call for the strengthening of Europe's innovative capacity and the development of a creative and knowledge-intensive economy and society through reinforcing the role of education and training in the knowledge triangle and focusing school curricula on creativity, innovation and entrepreneurship. This report brings evidence to the debate on the status, barriers and enablers for creativity and innovation in compulsory schooling in Europe. It is the final report of the project: ‘Creativity and Innovation in Education and Training in the EU27 (ICEAC)’ carried out by IPTS in collaboration with DG Education and Culture, highlighting the main messages gathered from each phase of the study: a literature review, a survey with teachers, an analysis of curricula and of good practices, stakeholder and expert interviews, and experts workshops. Based on this empirical material, five major areas for improvement are proposed to enable more creative learning and innovative teaching in Europe: curricula, pedagogies and assessment, teacher training, ICT and digital media, and educational culture and leadership. The study highlights the need for action at both national and European level to bring about the changes required for an open and innovative European educational culture based on the creative and innovative potential of its future generation. How to obtain EU publications Our priced publications are available from EU Bookshop (http://bookshop.europa.eu), where you can place an order with the sales agent of your choice. The Publications Office has a worldwide network of sales agents. You can obtain their contact details by sending a fax to (352) 29 29-42758. The mission of the Joint Research Centre is to provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of European Union policies. As a service of the European Commission, the Joint Research Centre functions as a reference centre of science and technology for the Union. Close to the policy-making process, it serves the common interest of the Member States, while being independent of special interests, whether private or national. LF-N A -275-EN -C
Design of Winding Changeable BLDC Motor Considering Demagnetization in Winding Change Section

In this paper, a changeable winding brushless DC (BLDC) motor for the expansion of the speed region is described. The changeable winding BLDC motor is driven by a large number of phase turns at low speeds and by a reduced number of turns at high speeds. For this reason, the section where the winding changes is very important. Ideally, the time at which the windings are to be converted should be same as the time at which the voltage changes. However, if this timing is not exactly synchronized, a large current is generated in the motor, and the demagnetization of the permanent magnet occurs. In addition, a large torque ripple is produced. In this paper, we describe the demagnetization of the permanent magnet in a fault situation when the windings change, and we suggest a design process to solve this problem.
Cyclic codes over F2+uF2+vF2+uvF2

In this work, we focus on cyclic codes over the ring F2+uF2+vF2+uvF2, which is not a finite chain ring. We use ideas from group rings and works of AbuAlrub et al. in ( 2007) to characterize the ring (F2 + uF2 + vF2 + uvF2)/(x − 1) and cyclic codes of odd length. Some good binary codes are obtained as the images of cyclic codes over F2+uF2+vF2+uvF2 under two Gray maps that are defined. We also characterize the binary images of cyclic codes over F2 + uF2 + vF2 + uvF2 in general.
Learning Deployment Trade-offs for Self-Optimization of Internet of Things Applications

The Internet of Things (IoT) is the next big wave in computing characterized by large scale open ended heterogeneous network of things, with varying sensing, actuating, computing and communication capabilities. Compared to the traditional field of autonomic computing, the IoT is characterized by an open ended and highly dynamic ecosystem with variable workload and resource availability. These characteristics make it difficult to implement self-awareness capabilities for IoT to manage and optimize itself. In this work, we introduce a methodology to explore and learn the trade-offs of different deployment configurations to autonomously optimize the QoS and other quality attributes of IoT applications. Our experiments demonstrate that our proposed methodology can automate the efficient deployment of IoT applications in the presence of multiple optimization objectives and variable operational circumstances.
Intelligent Control Framework for Robotic Rehabilitation after Stroke

This paper presents a new approach to robot assisted rehabilitation for stroke patients. The control architecture is represented in terms of hybrid system model combining a high-level and a low-level controller. The main focus of this paper is to present an intelligent controller, which is the high-level controller in the control architecture. The high-level controller is designed to monitor the progress and safety of the rehabilitation task. It also makes decisions on the modification of the task that might be needed for the therapy. Experimental results on unimpaired subjects are presented to demonstrate the efficacy of the high-level controller.
Clustering algorithms for bank customer segmentation

Market segmentation is one of the most important area of knowledge-based marketing. In banks, it is really a challenging task as data bases are large and multidimensional. In the paper we consider cluster analysis, which is the methodology, the most often applied in this area. We compare clustering algorithms in cases of high dimensionality with noise. We discuss using three algorithms: density based DBSCAN, k-means and based on it two-phase clustering process. We compare algorithms concerning their effectiveness and scalability. Some experiments with exemplary bank data sets are presented.
Accurate wild animal recognition using PCA, LDA and LBPH

In this paper, the performances of image recognition methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Patterns Histograms (LBPH) are tested and compared for the image recognition of the input animal images. The main idea of this paper is to present an independent, comparative study and some of the benefits and drawbacks of these most popular image recognition methods. Two sets of experiments are conducted for relative performance evaluations. In the first part of our experiments, the recognition accuracy of PCA, LDA and LBPH is demonstrated. The overall time execution for animal recognition process is evaluated in the second set of our experiments. We conduct tests on created animal database. The all algorithms have been tested on 300 different subjects (60 images for each class). The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set.
3D Robot Pose Estimation from 2D Images

This paper considers the task of locating articulated poses of multiple robots in images. Our approach simultaneously infers the number of robots in a scene, identifies joint locations and estimates sparse depth maps around joint locations. The proposed method applies staged convolutional feature detectors to 2D image inputs and computes robot instance masks using a recurrent network architecture. In addition, regression maps of most likely joint locations in pixel coordinates together with depth information are computed. Compositing 3D robot joint kinematics is accomplished by applying masks to joint readout maps. Our end-to-end formulation is in contrast to previous work in which the composition of robot joints into kinematics is performed in a separate postprocessing step. Despite the fact that our models are trained on artificial data, we demonstrate generalizability to real world images.
Metadata-Based Collaborative Filtering Using K-Partite Graph for Movie Recommendation

Collaborative filtering recommends items to a user based on the interests of other users having similar preferences. However, high dimensional, sparse data result in poor performance in collaborative filtering. This paper introduces an approach called multiple metadata-based collaborative filtering (MMCF), which utilizes meta-level information to alleviate this problem, e.g., metadata such as genre, director, and actor in the case of movie recommendation. MMCF builds a k-partite graph of users, movies and multiple metadata, and extracts implicit relationships among the metadata and between users and the metadata. Then the implicit relationships are propagated further by applying random walk process in order to alleviate the problem of sparseness in the original data set. The experimental results show substantial improvement over previous approaches on the real Netflix movie dataset.
The ups and downs of SIRT 1

Reversible acetylation has emerged as a key post-translational modification of proteins. Although the number of acetylated proteins is rapidly growing, the ways in which protein acetyltransferases and deacetylases connect with extracellular stimuli remain unclear. Recently, a regulatory network has emerged that controls the expression and activity of SIRT1, a mammalian class-III protein deacetylase. SIRT1 is an important regulator of metabolism, senescence, cancer and, possibly, longevity and is connected with crucial stress-responsive signaltransduction pathways. These connections provide important clues about how protein acetylation and deacetylation mediate cellular adaptations to extrinsic stress.
Generalizing Matching Knowledge using Active Learning

Research on integrating small numbers of datasets suggests the use of customized matching rules in order to adapt to the patterns in the data and achieve better results. The state-of-the-art work on matching large numbers of datasets exploits attribute co-occurrence as well as the similarity of values between multiple sources. We build upon these research directions in order to develop a method for generalizing matching knowledge using minimal human intervention. The central idea of our research program is that even in large numbers of datasets of a specific domain patterns (matching knowledge) reoccur, and discovering those can facilitate the integration task. Our proposed approach plans to use and extend existing work of our group on schema and instance matching as well as on learning expressive rules with active learning. We plan to evaluate our approach on publicly available e-commerce data collected from the Web.
Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition

We present a computational method for extracting simple descriptions of high dimensional data sets in the form of simplicial complexes. Our method, called Mapper, is based on the idea of partial clustering of the data guided by a set of functions defined on the data. The proposed method is not dependent on any particular clustering algorithm, i.e. any clustering algorithm may be used with Mapper. We implement this method and present a few sample applications in which simple descriptions of the data present important information about its structure.