nils-herrmann/clean_scirepeval huggingface dataset:
Psychobiology of plasticity: effects of training and experience on brain and behavior

Supporting Hebb's 1949 hypothesis of use-induced plasticity of the nervous system, our group found in the 1960s that training or differential experience induced neurochemical changes in cerebral cortex of the rat and regional changes in weight of cortex. Further studies revealed changes in cortical thickness, size of synaptic contacts, number of dendritic spines, and dendritic branching. Similar effects were found whether rats were assigned to differential experience at weaning (25 days of age), as young adults (105 days) or as adults (285 days). Enriched early experience improved performance on several tests of learning. Cerebral results of experience in an enriched environment are similar to results of formal training. Enriched experience and training appear to evoke the same cascade of neurochemical events in causing plastic changes in brain. Sufficiently rich experience may be necessary for full growth of species-specific brain characteristics and behavioral potential. Clayton and Krebs found in 1994 that birds that normally store food have larger hippocampi than related species that do not store. This difference develops only in birds given the opportunity to store and recover food. Research on use-induced plasticity is being applied to promote child development, successful aging, and recovery from brain damage; it is also being applied to benefit animals in laboratories, zoos and farms.
Attention and associative learning in humans: An integrative review.

This article presents a comprehensive survey of research concerning interactions between associative learning and attention in humans. Four main findings are described. First, attention is biased toward stimuli that predict their consequences reliably (learned predictiveness). This finding is consistent with the approach taken by Mackintosh (1975) in his attentional model of associative learning in nonhuman animals. Second, the strength of this attentional bias is modulated by the value of the outcome (learned value). That is, predictors of high-value outcomes receive especially high levels of attention. Third, the related but opposing idea that uncertainty may result in increased attention to stimuli (Pearce & Hall, 1980), receives less support. This suggests that hybrid models of associative learning, incorporating the mechanisms of both the Mackintosh and Pearce-Hall theories, may not be required to explain data from human participants. Rather, a simpler model, in which attention to stimuli is determined by how strongly they are associated with significant outcomes, goes a long way to account for the data on human attentional learning. The last main finding, and an exciting area for future research and theorizing, is that learned predictiveness and learned value modulate both deliberate attentional focus, and more automatic attentional capture. The automatic influence of learning on attention does not appear to fit the traditional view of attention as being either goal-directed or stimulus-driven. Rather, it suggests a new kind of “derived” attention.
Time series distance measures: segmentation, classification, and clustering of temporal data

Time series can be found in domains as diverse as medicine, astronomy, geophysics, engineering, and quantitative finance. In general, a time series is a sequence of data points, measured at successive points in time and spaced at uniform time intervals. This thesis is concerned with time series mining, including segmentation, classification, and clustering of temporal data. Many algorithms for these tasks depend upon pairwise (dis)similarity comparisons of (sub)sequences, which accounts for the continued research on time series distance measures as an important subroutine. In the course of this work we introduce several novel distance measures, which describe time series characteristics that may distinguish the individual classes contained in the data. Our proposed time series distance measures address frequently encountered issues, such as the processing of multivariate data, the computational complexity of pairwise (dis)similarity comparisons, the invariance required for temporal data with distortions, the separation of mixed signals, and the analysis of nonlinear systems. Our work contributes to the time series community by introducing novel approaches to pattern recognition in temporal data, presenting miscellaneous sensor fusion techniques for multivariate measurements, offering efficient and robust distance measures for fast time series classification, introducing previously disregarded invariance and proposing corresponding distance measures, comparing various machine learning algorithms for signal separation, and providing nonlinear models for time series mining. In addition to our theoretical contributions, we furthermore demonstrate that our proposed time series distance measures are beneficial in real-world applications, including the optimization of vehicle engines with regard to exhaust emission and the optimization of heating control in terms of energy efficiency. Furthermore, we present several specifically developed time series mining tools, which implement our introduced distance measures and provide graphical user interfaces for straightforward parameter setting as well as exploratory data analysis.
Behavioral Customer Loyalty in Online Shopping: The Role of E-Service Quality and E-Recovery

Nowadays, the internet has become a suitable platform for the trade of products and services. Online shopping transactions are carried out without face-to-face contact; thus, various factors can affect customers’ purchase intentions. In addition to aesthetics and ease of use, trust is a critical factor which allows customers to choose an online store. Online merchants need to find ways to keep trusting customers satisfied and loyal. This paper investigates how customer loyalty can be created and/or enhanced in online shopping. The statistical population of the study includes the customers of several online stores in Iran. Data collection is carried out using a questionnaire. The results indicate that information security and website performance influence eservice quality both positively and directly. Also, responsiveness, compensation, and contact positively influence e-recovery. E-recovery has positive impact on e-loyalty, which in turn significantly affects online repurchase.
Plasmons in Graphene: Fundamental Properties and Potential Applications

Plasmons in graphene have intriguing fundamental properties and hold great potential for applications. They enable strong confinement of electromagnetic energy at subwavelength scales, which can be tuned and controlled via gate voltage, providing an advantage for graphene's plasmons over surface plasmons (SPs) on a metal-dielectric interface. They have been described for a large span of frequencies from terahertz up to infrared and even in the visible. We provide a critical review of the current knowledge of graphene plasmon properties (dispersion and linewidth) with particular emphasis on plasmonic losses and the competition between different decay channels, which are not yet fully understood. Plasmons in graphene provide an insight into interesting many-body effects such as those arising from the electron-phonon interaction and electron-electron interactions, including hybrid plasmon-phonon collective excitations (either with intrinsic or substrate phonons) and plasmarons. We provide a comparison of SPs on a metal-dielectric interface with plasmons in graphene and 2-D metallic monolayers. We finally outline the potential for graphene's plasmons for applications.
DualNet: Learn Complementary Features for Image Recognition

In this work we propose a novel framework named Dual-Net aiming at learning more accurate representation for image recognition. Here two parallel neural networks are coordinated to learn complementary features and thus a wider network is constructed. Specifically, we logically divide an end-to-end deep convolutional neural network into two functional parts, i.e., feature extractor and image classifier. The extractors of two subnetworks are placed side by side, which exactly form the feature extractor of DualNet. Then the two-stream features are aggregated to the final classifier for overall classification, while two auxiliary classifiers are appended behind the feature extractor of each subnetwork to make the separately learned features discriminative alone. The complementary constraint is imposed by weighting the three classifiers, which is indeed the key of DualNet. The corresponding training strategy is also proposed, consisting of iterative training and joint finetuning, to make the two subnetworks cooperate well with each other. Finally, DualNet based on the well-known CaffeNet, VGGNet, NIN and ResNet are thoroughly investigated and experimentally evaluated on multiple datasets including CIFAR-100, Stanford Dogs and UEC FOOD-100. The results demonstrate that DualNet can really help learn more accurate image representation, and thus result in higher accuracy for recognition. In particular, the performance on CIFAR-100 is state-of-the-art compared to the recent works.
Motivation in second and foreign language learning

Zolt ́n Dörnyei Language Teaching / Volume 31 / Issue 03 / July 1998, pp 117 ­ 135 DOI: 10.1017/S026144480001315X,  2009 Link to this article: http://journals.cambridge.org/abstract_S026144480001315X How to cite this article: Zolt ́n Dörnyei (1998). Motivation in second and foreign language learning. Language Teaching, 31, pp 117­135 doi:10.1017/S026144480001315X Request Permissions : Click here
Musical training, bilingualism, and executive function: working memory and inhibitory control

The current study investigated whether long-term experience in music or a second language is associated with enhanced cognitive functioning. Early studies suggested the possibility of a cognitive advantage from musical training and bilingualism but have failed to be replicated by recent findings. Further, each form of expertise has been independently investigated leaving it unclear whether any benefits are specifically caused by each skill or are a result of skill learning in general. To assess whether cognitive benefits from training exist, and how unique they are to each training domain, the current study compared musicians and bilinguals to each other, plus to individuals who had expertise in both skills, or neither. Young adults (n = 153) were categorized into one of four groups: monolingual musician; bilingual musician; bilingual non-musician; and monolingual non-musician. Multiple tasks per cognitive ability were used to examine the coherency of any training effects. Results revealed that musically trained individuals, but not bilinguals, had enhanced working memory. Neither skill had enhanced inhibitory control. The findings confirm previous associations between musicians and improved cognition and extend existing evidence to show that benefits are narrower than expected but can be uniquely attributed to music compared to another specialized auditory skill domain. The null bilingual effect despite a music effect in the same group of individuals challenges the proposition that young adults are at a performance ceiling and adds to increasing evidence on the lack of a bilingual advantage on cognition.
WhatsApp, Doc? A First Look at WhatsApp Public Group Data

In this dataset paper we describe our work on the collection and analysis of public WhatsApp group data. Our primary goal is to explore the feasibility of collecting and using WhatsApp data for social science research. We therefore present a generalisable data collection methodology, and a publicly available dataset for use by other researchers. To provide context, we perform statistical exploration to allow researchers to understand what public WhatsApp group data can be collected and how this data can be used. Given the widespread use of WhatsApp, our techniques to obtain public data and potential applications are important for the community.
LIVING LAB : user-driven innovation for sustainability

This paper summarizes and discusses the results from the LIVING LAB design study, a project within the 7 Framework Programme of the European Union. The aim of this project was to develop the conceptual design of the LIVING LAB Research Infrastructure that will be used to research human interaction with, and stimulate the adoption of, sustainable, smart and healthy innovations around the home.  A LIVING LAB is a combined lab-/household system, analysing existing product-service-systems as well as technical and socioeconomic influences focused on the social needs of people, aiming at the development of integrated technical and social innovations and simultaneously promoting the conditions of sustainable development (highest resource efficiency, highest user orientation, etc.). This approach allows the development and testing of sustainable domestic technologies, while putting the user on centre stage.  As this paper discusses the design study, no actual findings can be presented here but the focus is on presenting the research approach.  Research and development of sustainable products and services through userdriven innovation in experimental-oriented environments 2  The two elements (real homes and living laboratories) of this approach are what make the LIVING LAB research infrastructure unique. The research conducted in LIVING LAB will be innovative in several respects. First, it will contribute to market innovation by producing breakthroughs in sustainable domestic technologies that will be easy to install, user friendly and that meet environmental performance standards in real life. Second, research from LIVING LAB will contribute to innovation in practice by pioneering new forms of in-context, user-centred research, including long-term and cross-cultural research.
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation

Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning methods were employed with success for background initialization, foreground detection and deep learned features. Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies. Furthermore, a huge amount of papers was published since 2016 when Braham and Van Droogenbroeck published their first work on CNN applied to background subtraction providing a regular gain of performance. In this context, we provide the first review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions. For this, we first surveyed the methods used background initialization, background subtraction and deep learned features. Then, we discuss the adequacy of deep neural networks for background subtraction. Finally, experimental results are presented on the CDnet 2014 dataset. Thierry Bouwmans Lab. MIA, Univ. La Rochelle, France E-mail: tbouwman@univ-lr.fr Sajid Javed Dept. of Computer Science, University of Warwick, UK E-mail: s.javed.1@warwick.ac.uk Maryam Sultana Dept. of Computer Science and Engineering, Kyungpook National University, Republic of Korea E-mail: maryam@vr.knu.ac.kr Soon Ki Jung Dept. of Computer Science and Engineering, Kyungpook National University, Republic of Korea E-mail: skjung@knu.ac.kr 2 Thierry Bouwmans, Sajid Javed, Maryam Sultana, Soon Ki Jung
Object tracking: A survey

The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle’s self-localization, which is generally performed by high-end global navigation satellite system (GNSS)/inertial measurement unit (IMU) integration. However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals. Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs) and manually identifying those points in the data, are both labor-intensive and time-consuming. In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data. The proposed framework has three key steps: (1) extracting road features from the MMS and aerial data; (2) obtaining Gaussian mixture models from the extracted aerial road features; and (3) performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT). The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping.
Early detection of DDoS attacks against SDN controllers

A Software Defined Network (SDN) is a new network architecture that provides central control over the network. Although central control is the major advantage of SDN, it is also a single point of failure if it is made unreachable by a Distributed Denial of Service (DDoS) Attack. To mitigate this threat, this paper proposes to use the central control of SDN for attack detection and introduces a solution that is effective and lightweight in terms of the resources that it uses. More precisely, this paper shows how DDoS attacks can exhaust controller resources and provides a solution to detect such attacks based on the entropy variation of the destination IP address. This method is able to detect DDoS within the first five hundred packets of the attack traffic.
Artificial Intelligence and Data Science in the Automotive Industry

Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future. This article defines the terms “data science” (also referred to as “data analytics”) and “machine learning” and how they are related. In addition, it defines the term “optimizing analytics“ and illustrates the role of automatic optimization as a key technology in combination with data analytics. It also uses examples to explain the way that these technologies are currently being used in the automotive industry on the basis of the major subprocesses in the automotive value chain (development, procurement; logistics, production, marketing, sales and after-sales, connected customer). Since the industry is just starting to explore the broad range of potential uses for these technologies, visionary application examples are used to illustrate the revolutionary possibilities that they offer. Finally, the article demonstrates how these technologies can make the automotive industry more efficient and enhance its customer focus throughout all its operations and activities, extending from the product and its development process to the customers and their connection to the product.
DOLORES: Deep Contextualized Knowledge Graph Embeddings

We introduce a new method DOLORES for learning knowledge graph embeddings that effectively captures contextual cues and dependencies among entities and relations. First, we note that short paths on knowledge graphs comprising of chains of entities and relations can encode valuable information regarding their contextual usage. We operationalize this notion by representing knowledge graphs not as a collection of triples but as a collection of entity-relation chains, and learn embeddings for entities and relations using deep neural models that capture such contextual usage. In particular, our model is based on Bi-Directional LSTMs and learn deep representations of entities and relations from constructed entity-relation chains. We show that these representations can very easily be incorporated into existing models to significantly advance the state of the art on several knowledge graph prediction tasks like link prediction, triple classification, and missing relation type prediction (in some cases by at least 9.5%).
ELECTRONIC WORD-OF-MOUTH VIA CONSUMER-OPINION PLATFORMS: WHAT MOTIVATES CONSUMERS TO ARTICULATE THEMSELVES ON THE INTERNET?

Through Web-based consumer opinion platforms (e.g., epinions.com), the Internet enables customers to share their opinions on, and experiences with, goods and services with a multitude of other consumers; that is, to engage in electronic wordof-mouth (eWOM) communication. Drawing on findings from research on virtual communities and traditional word-of-mouth literature, a typology for motives of consumer online articulation is
Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation

Hand pose estimation from single depth images is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural networks, accurate hand pose estimation is still a challenging problem. In this paper we propose a novel approach named as Pose guided structured Region Ensemble Network (Pose-REN) to boost the performance of hand pose estimation. Under the guidance of an initially estimated pose, the proposed method extracts regions from the feature maps of convolutional neural network and generates more optimal and representative features for hand pose estimation. The extracted feature regions are then integrated hierarchically according to the topology of hand joints by tree-structured fully connections to regress the refined hand pose. The final hand pose is obtained by an iterative cascaded method. Comprehensive experiments on public hand pose datasets demonstrate that our proposed method outperforms state-of-the-art algorithms.