nils-herrmann/clean_scirepeval huggingface dataset:
Future Trends in Marine Robotics [TC Spotlight]

The IEEE Robotics and Automation Society (RAS) Marine Robotics Technical Committee (MRTC) was first established in 2008 following the dismissal of the Underwater Robotics Technical Committee in spring 2008. The goal of the MRTC is to foster research on robots and intelligent systems that extend the human capabilities in marine environments and to promote maritime robotic applications important to science, industry, and defense. The TC organizes conferences, workshops, and special issues that bring marine robotics research to the forefront of the broader robotics community. The TC also introduces its members to the latest development of marine robotics through Web sites and online social media.
Pathological video-game use among youth ages 8 to 18: a national study.

Researchers have studied whether some youth are "addicted" to video games, but previous studies have been based on regional convenience samples. Using a national sample, this study gathered information about video-gaming habits and parental involvement in gaming, to determine the percentage of youth who meet clinical-style criteria for pathological gaming. A Harris poll surveyed a randomly selected sample of 1,178 American youth ages 8 to 18. About 8% of video-game players in this sample exhibited pathological patterns of play. Several indicators documented convergent and divergent validity of the results: Pathological gamers spent twice as much time playing as nonpathological gamers and received poorer grades in school; pathological gaming also showed comorbidity with attention problems. Pathological status significantly predicted poorer school performance even after controlling for sex, age, and weekly amount of video-game play. These results confirm that pathological gaming can be measured reliably, that the construct demonstrates validity, and that it is not simply isomorphic with a high amount of play.
Effect size, confidence interval and statistical significance: a practical guide for biologists.

Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
Mean Birds: Detecting Aggression and Bullying on Twitter

In recent years, bullying and aggression against social media users have grown significantly, causing serious consequences to victims of all demographics. Nowadays, cyberbullying affects more than half of young social media users worldwide, suffering from prolonged and/or coordinated digital harassment. Also, tools and technologies geared to understand and mitigate it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users. We find that bullies post less, participate in fewer online communities, and are less popular than normal users. Aggressors are relatively popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, with over 90% AUC.
EXTENDING THE UTAUT MODEL IN M-BANKING ADOPTION

The banks are motivated to leverage their m-banking activities with the advancement of mobile technologies and the high mobile penetration rate. Despite the availability of the mbanking services, many consumers still posses the attitude of ‘wait-and-see’ which resulted to unsatisfactory adoption rate. Hence, this paper aims to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) model to better understand what drives Malaysian consumers to adopt m-banking services. The conceptual model also provide better strategic insights for commercial banks, service developers, mobile manufacturers, and others to yield higher acceptance of m-banking.
Reading words in discourse: the modulation of lexical priming effects by message-level context.

Repetition and semantic-associative priming effects have been demonstrated for words in nonstructured contexts (i.e., word pairs or lists of words) in numerous behavioral and electrophysiological studies. The processing of a word has thus been shown to benefit from the prior presentation of an identical or associated word in the absence of a constraining context. An examination of such priming effects for words that are embedded within a meaningful discourse context provides information about the interaction of different levels of linguistic analysis. This article reviews behavioral and electrophysiological research that has examined the processing of repeated and associated words in sentence and discourse contexts. It provides examples of the ways in which eye tracking and event-related potentials might be used to further explore priming effects in discourse. The modulation of lexical priming effects by discourse factors suggests the interaction of information at different levels in online language comprehension.
ADVISE: Symbolism and External Knowledge for Decoding Advertisements

In order to convey the most content in their limited space, advertisements embed references to outside knowledge via symbolism. For example, a motorcycle stands for adventure (a positive property the ad wants associated with the product being sold), and a gun stands for danger (a negative property to dissuade viewers from undesirable behaviors). We show how to use symbolic references to better understand the meaning of an ad. We further show how anchoring ad understanding in general-purpose object recognition and image captioning improves results. We formulate the ad understanding task as matching the ad image to human-generated statements that describe the action that the ad prompts, and the rationale it provides for taking this action. Our proposed method outperforms the state of the art on this task, and on an alternative formulation of question-answering on ads. We show additional applications of our learned representations for matching ads to slogans, and clustering ads according to their topic, without extra training.
Towards Scalable and Private Industrial Blockchains

The blockchain emerges as an innovative tool that has the potential to positively impact the way we design a number of online applications today. In many ways, the blockchain technology is, however, still not mature enough to cater for industrial standards. Namely, existing Byzantine tolerant permission-based blockchain deployments can only scale to a limited number of nodes. These systems typically require that all transactions (and their order of execution) are publicly available to all nodes in the system, which comes at odds with common data sharing practices in the industry, and prevents a centralized regulator from overseeing the full blockchain system. In this paper, we propose a novel blockchain architecture devised specifically to meet industrial standards. Our proposal leverages the notion of satellite chains that can privately run different consensus protocols in parallel - thereby considerably boosting the scalability premises of the system. Our solution also accounts for a hands-off regulator that oversees the entire network, enforces specific policies by means of smart contracts, etc. We implemented our solution and integrated it with Hyperledger Fabric v0.6.
Revision of Achievement Goal Theory : Necessary and Illuminating

C. Midgley et al. (2001) raised important questions about the effects of performance-approach goals. The present authors disagree with their characterization of the research findings and implications for theory. They discuss 3 reasons to revise goal theory: (a) the importance of separating approach from avoidance strivings, (b) the positive potential of performance-approach goals, and (c) identification of the ways performance-approach goals can combine with mastery goals to promote optimal motivation. The authors review theory and research to substantiate their claim that goal theory is in need of revision, and they endorse a multiple goal perspective. The revision of goal theory is underway and offers a more complex, but necessary, perspective on important issues of motivation, learning, and achievement.
Deep learning with word embeddings improves biomedical named entity recognition

Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pre-defined features which try to capture the specific surface properties of entity types, properties of the typical local context, background knowledge, and linguistic information. State-of-the-art tools are entity-specific, as dictionaries and empirically optimal feature sets differ between entity types, which makes their development costly. Furthermore, features are often optimized for a specific gold standard corpus, which makes extrapolation of quality measures difficult.



We show that a completely generic method based on deep learning and statistical word embeddings [called long short-term memory network-conditional random field (LSTM-CRF)] outperforms state-of-the-art entity-specific NER tools, and often by a large margin. To this end, we compared the performance of LSTM-CRF on 33 data sets covering five different entity classes with that of best-of-class NER tools and an entity-agnostic CRF implementation. On average, F1-score of LSTM-CRF is 5% above that of the baselines, mostly due to a sharp increase in recall.


Availability and implementation
The source code for LSTM-CRF is available at https://github.com/glample/tagger and the links to the corpora are available at https://corposaurus.github.io/corpora/ .
FEVER: a large-scale dataset for Fact Extraction and VERification

In this paper we introduce a new publicly available dataset for verification against textual sources,  Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as SUPPORTED, REFUTED or NOTENOUGHINFO by annotators achieving 0.6841 in Fleiss κ. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87%, while if we ignore the evidence we achieve 50.91%. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.
Computational Intelligence: Principles, Techniques and Applications

Will reading habit influence your life? Many say yes. Reading computational intelligence principles techniques and applications is a good habit; you can develop this habit to be such interesting way. Yeah, reading habit will not only make you have any favourite activity. It will be one of guidance of your life. When reading has become a habit, you will not make it as disturbing activities or as boring activity. You can gain many benefits and importances of reading.
Space Robotics-DLR's Telerobotic Concepts, Lightweight Arms and Articulated Hands

The paper briefly outlines DLR's experience with real space robot missions (ROTEX and ETS VII). It then discusses forthcoming projects, e.g., free-flying systems in low or geostationary orbit and robot systems around the space station ISS, where the telerobotic system MARCO might represent a common baseline. Finally it describes our efforts in developing a new generation of "mechatronic" ultra-light weight arms with multifingered hands. The third arm generation is operable now (approaching present-day technical limits). In a similar way DLR's four-fingered hand II was a big step towards higher reliability and yet better performance. Artificial robonauts for space are a central goal now for the Europeans as well as for NASA, and the first verification tests of DLR's joint components are supposed to fly already end of 93 on the space station.
Handbook of Blind Source Separation: Independent Component Analysis and Applications

Give us 5 minutes and we will show you the best book to read today. This is it, the handbook of blind source separation independent component analysis and applications that will be your best choice for better reading book. Your five times will not spend wasted by reading this website. You can take the book as a source to make better concept. Referring the books that can be situated with your needs is sometime difficult. But here, this is so easy. You can find the best thing of book that you can read.
The phenomena of inner experience

This study provides a survey of phenomena that present themselves during moments of naturally occurring inner experience. In our previous studies using Descriptive Experience Sampling (DES) we have discovered five frequently occurring phenomena--inner speech, inner seeing, unsymbolized thinking, feelings, and sensory awareness. Here we quantify the relative frequency of these phenomena. We used DES to describe 10 randomly identified moments of inner experience from each of 30 participants selected from a stratified sample of college students. We found that each of the five phenomena occurred in approximately one quarter of sampled moments, that the frequency of these phenomena varied widely across individuals, that there were no significant gender differences in the relative frequencies of these phenomena, and that higher frequencies of inner speech were associated with lower levels of psychological distress.
Dehumanization and infrahumanization.

We review early and recent psychological theories of dehumanization and survey the burgeoning empirical literature, focusing on six fundamental questions. First, we examine how people are dehumanized, exploring the range of ways in which perceptions of lesser humanness have been conceptualized and demonstrated. Second, we review who is dehumanized, examining the social targets that have been shown to be denied humanness and commonalities among them. Third, we investigate who dehumanizes, notably the personality, ideological, and other individual differences that increase the propensity to see others as less than human. Fourth, we explore when people dehumanize, focusing on transient situational and motivational factors that promote dehumanizing perceptions. Fifth, we examine the consequences of dehumanization, emphasizing its implications for prosocial and antisocial behavior and for moral judgment. Finally, we ask what can be done to reduce dehumanization. We conclude with a discussion of limitations of current scholarship and directions for future research.
Deep Learning Object Detection Methods for Ecological Camera Trap Data

Deep learning methods for computer vision tasks show promise for automating the data analysis of camera trap images. Ecological camera traps are a common approach for monitoring an ecosystem's animal population, as they provide continual insight into an environment without being intrusive. However, the analysis of camera trap images is expensive, labour intensive, and time consuming. Recent advances in the field of deep learning for object detection show promise towards automating the analysis of camera trap images. Here, we demonstrate their capabilities by training and comparing two deep learning object detection classifiers, Faster R-CNN and YOLO v2.0, to identify, quantify, and localize animal species within camera trap images using the Reconyx Camera Trap and the self-labeled Gold Standard Snapshot Serengeti data sets. When trained on large labeled datasets, object recognition methods have shown success. We demonstrate their use, in the context of realistically sized ecological data sets, by testing if object detection methods are applicable for ecological research scenarios when utilizing transfer learning. Faster R-CNN outperformed YOLO v2.0 with average accuracies of 93.0% and 76.7% on the two data sets, respectively. Our findings show promising steps towards the automation of the labourious task of labeling camera trap images, which can be used to improve our understanding of the population dynamics of ecosystems across the planet.
Discovering physical concepts with neural networks

While neural networks have been remarkably successful for a variety of practical problems, they are often applied as a black box, which limits their utility for scientific discoveries. Here, we present a neural network architecture that can be used to discover physical concepts from experimental data without being provided with additional prior knowledge. For a variety of simple systems in classical and quantum mechanics, our network learns to compress experimental data to a simple representation and uses the representation to answer questions about the physical system. Physical concepts can be extracted from the learned representation, namely: (1) The representation stores the physically relevant parameters, like the frequency of a pendulum. (2) The network finds and exploits conservation laws: it stores the total angular momentum to predict the motion of two colliding particles. (3) Given measurement data of a simple quantum mechanical system, the network correctly recognizes the number of degrees of freedom describing the underlying quantum state. (4) Given a time series of the positions of the Sun and Mars as observed from Earth, the network discovers the heliocentric model of the solar system — that is, it encodes the data into the angles of the two planets as seen from the Sun. Our work provides a first step towards answering the question whether the traditional ways by which physicists model nature naturally arise from the experimental data without any mathematical and physical pre-knowledge, or if there are alternative elegant formalisms, which may solve some of the fundamental conceptual problems in modern physics, such as the measurement problem in quantum mechanics.
Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images

Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying crop health. This paper reports some experiences related to the analysis of cultivations (vineyards and tomatoes) with Tetracam multispectral data. The Tetracam camera was mounted on a multi-rotor hexacopter. The multispectral data were processed with a photogrammetric pipeline to create triband orthoimages of the surveyed sites. Those orthoimages were employed to extract some Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Soil Adjusted Vegetation Index (SAVI), examining the vegetation vigor for each crop. The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and OPEN ACCESS Remote Sens. 2015, 7 4027 evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications.
Agricultural environment monitoring system using wireless sensor networks and IoT

Monitoring and control the environmental parameters in agricultural constructions are essential to improve energy efficiency and productivity. Real-time monitoring allows the detection and early correction of unfavourable situations, optimizing consumption and protecting crops against diseases. This work describes an automatic system for monitoring farm environments with the aim of increasing efficiency and quality of the agricultural environment. Based on the Internet of Things, the system uses a low-cost wireless sensor network, called Sun Spot, programmed in Java, with the Java VM running on the device itself and the Arduino platform for Internet connection. The data collected is shared through the social network of Facebook. The temperature and brightness parameters are monitored in real time. Other sensors can be added to monitor the issue for specific purposes. The results show that conditions within greenhouses may in some cases be very different from those expected. Therefore, the proposed system can provide an effective tool to improve the quality of agricultural production and energy efficiency.
Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

Alim Samat 1,2,*, Paolo Gamba 3, Jilili Abuduwaili 1,2, Sicong Liu 4 and Zelang Miao 5 1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; jilil@ms.xjb.ac.cn 2 Chinese Academy of Sciences Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China 3 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy; paolo.gamba@unipv.it 4 College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China; sicongliu.rs@gmail.com 5 Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China; cumtzlmiao@gmail.com * Correspondence: ;