nils-herrmann/clean_scirepeval huggingface dataset:
Caffeine and exercise: metabolism, endurance and performance.

Caffeine is a common substance in the diets of most athletes and it is now appearing in many new products, including energy drinks, sport gels, alcoholic beverages and diet aids. It can be a powerful ergogenic aid at levels that are considerably lower than the acceptable limit of the International Olympic Committee and could be beneficial in training and in competition. Caffeine does not improve maximal oxygen capacity directly, but could permit the athlete to train at a greater power output and/or to train longer. It has also been shown to increase speed and/or power output in simulated race conditions. These effects have been found in activities that last as little as 60 seconds or as long as 2 hours. There is less information about the effects of caffeine on strength; however, recent work suggests no effect on maximal ability, but enhanced endurance or resistance to fatigue. There is no evidence that caffeine ingestion before exercise leads to dehydration, ion imbalance, or any other adverse effects. The ingestion of caffeine as coffee appears to be ineffective compared to doping with pure caffeine. Related compounds such as theophylline are also potent ergogenic aids. Caffeine may act synergistically with other drugs including ephedrine and anti-inflammatory agents. It appears that male and female athletes have similar caffeine pharmacokinetics, i.e., for a given dose of caffeine, the time course and absolute plasma concentrations of caffeine and its metabolites are the same. In addition, exercise or dehydration does not affect caffeine pharmacokinetics. The limited information available suggests that caffeine non-users and users respond similarly and that withdrawal from caffeine may not be important. The mechanism(s) by which caffeine elicits its ergogenic effects are unknown, but the popular theory that it enhances fat oxidation and spares muscle glycogen has very little support and is an incomplete explanation at best. Caffeine may work, in part, by creating a more favourable intracellular ionic environment in active muscle. This could facilitate force production by each motor unit.
Korean pop takes off! Social media strategy of Korean entertainment industry

The Korean Wave (K-wave), or Hallyu, referred as “the phenomenon of Korean pop culture, such as TV dramas, films, pop music, fashion, and online games being widely embraced and shared among the people of Japan, China, Hong Kong, Taiwan, and other Asian countries”. After the drama-oriented craze of the first K-wave, Korean pop music (shortly K-pop) took the integral part of the second K-wave. In addition, with the rapid spread of social media like YouTube and Twitter, K-pop has expanded its fandom outside of Asia to the West. The world-wide success of K-pop contributes to improve the `Korea' image and make a positive impact on Korean economy. It is reported that around 1,000 entertainment agencies are active in Korea, while there exist the “big three” record labels and entertainment agencies: SM Entertainment, YG Entertainment and JYP Entertainment. In this case, we address the world-wide phenomenon of K-pop popularity and the role of social media on recent boom of K-pop. Focusing the major agencies above, we present lessons on how to manage social media strategically: align strategic business model with social media; maximize various social media channels; engage customers with on-and offline promotions; and stimulate audience with exclusive contents.
Using CMMI together with agile software development: A systematic review

http://dx.doi.org/10.1016/j.infsof.2014.09.012 0950-5849/ 2014 Elsevier B.V. All rights reserved. ⇑ Corresponding author at: Center of Informatics, Federal University of Pernambuco (CIn/UFPE), Av. Jornalista Anibal Fernandes, S/N, Cidade Universitária, 50. Recife, PE, Brazil. Tel.. E-mail addresses: fss4@cin.ufpe.br (F. Selleri Silva), fsfs@cin.ufpe.br (F.S.F. Soares), alp5@cin.ufpe.br (A.L. Peres), ima2@cin.ufpe.br (I.M.d. Azevedo), aplfv@ci (A.P.L.F. Vasconcelos), fkk@cin.ufpe.br (F.K. Kamei), srlm@cin.ufpe.br (S.R.d.L. Meira). Fernando Selleri Silva a,b,⇑, Felipe Santana Furtado Soares , Angela Lima Peres , Ivanildo Monteiro de Azevedo , Ana Paula L.F. Vasconcelos , Fernando Kenji Kamei , Silvio Romero de Lemos Meira a,c
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function

In this paper, we do a careful study of a bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches. In prior work, it has been reported that in order to get good classification accuracy using LSTMmodels for text classification task, pretraining the LSTM model parameters using unsupervised learning methods such as language modeling or sequence auto-encoder is necessary [2, 20]. However, we find that our simple model, when trained with cross-entropy loss is able to achieve competitive results compared with the more complex models. Furthermore, in addition to cross-entropy loss, by using a combination of entropy minimization, adversarial, and virtual adversarial losses for both labeled and unlabeled data, we report new state-of-the-art results for text classification task on four benchmark datasets. In particular, on ACL-IMDB sentiment analysis and AG-News topic classification datasets, our method outperforms current approaches by a substantial margin.
Understanding Self-Controlled Motor Learning Protocols through the Self-Determination Theory

The purpose of the present review was to provide a theoretical understanding of the learning advantages underlying a self-controlled practice context through the tenets of the self-determination theory (SDT). Three micro-theories within the macro-theory of SDT (Basic psychological needs theory, Cognitive  Theory, and Organismic Integration Theory) are used as a framework for examining the current self-controlled motor learning literature. A review of 26 peer-reviewed, empirical studies from the motor learning and medical training literature revealed an important limitation of the self-controlled research in motor learning: that the effects of motivation have been assumed rather than quantified. The SDT offers a basis from which to include measurements of motivation into explanations of changes in behavior. This review suggests that a self-controlled practice context can facilitate such factors as feelings of autonomy and competence of the learner, thereby supporting the psychological needs of the learner, leading to long term changes to behavior. Possible tools for the measurement of motivation and regulation in future studies are discussed. The SDT not only allows for a theoretical reinterpretation of the extant motor learning research supporting self-control as a learning variable, but also can help to better understand and measure the changes occurring between the practice environment and the observed behavioral outcomes.
Boosting in the presence of label noise

Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost’s robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of robust classifiers, although converges faster than non label-noise aware AdaBoost, is still susceptible to label noise. However, pairing it with the new robust Boosting algorithm we propose here results in a more resilient algorithm under mislabelling.
Towards Internet of Things (IOTS): Integration of Wireless Sensor Network to Cloud Services for Data Collection and Sharing

Cloud computing provides great benefits for applications hosted on the Web that also have special computational and storage requirements. This paper proposes an extensible and flexible architecture for integrating Wireless Sensor Networks with the Cloud. We have used REST based Web services as an interoperable application layer that can be directly integrated into other application domains for remote monitoring such as e-health care services, smart homes, or even vehicular area networks (VAN). For proof of concept, we have implemented a REST based Web services on an IP based low power WSN test bed, which enables data access from anywhere. The alert feature has also been implemented to notify users via email or tweets for monitoring data when they exceed values and events of interest.
Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories

Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a part-based probabilistic representation , we propose a new 3D object class model that is capable of recognizing unseen views by pose estimation and synthesis. We achieve this by using a dense, multiview representation of the viewing sphere parameterized by a triangular mesh of viewpoints. Each triangle of viewpoints can be morphed to synthesize new viewpoints. By incorporating 3D geometrical constraints, our model establishes explicit correspondences among object parts across viewpoints. We propose an incremental learning algorithm to train the generative model. A cellphone video clip of an object is first used to initialize model learning. Then the model is updated by a set of unsorted training images without viewpoint labels. We demonstrate the robustness of our model on object detection, viewpoint classification and synthesis tasks. Our model performs superiorly to and on par with state-of-the-art algorithms on the Savarese et al. 2007 and PASCAL datasets in object detection. It outperforms all previous work in viewpoint classification and offers promising results in viewpoint synthesis.
A Prolog Framework for Integrating Business Rules into Java Applications

Business specifications – that formerly only supported IT development – increasingly become business configurations in the form of rules that can be loaded directly into IT solutions. PROLOG is well–known for its qualities in the development of sophisticated rule systems. It is desirable to combine the advantages of PROLOG with JAVA, since JAVA has become one of the most used programming languages in industry. However, experts of both programming languages are rare. To overcome the resulting interoperability problems, we have developed a framework which generates a JAVA archive that provides methods to query a given set of PROLOG rules; it ensures that valid knowledge bases are transmitted between JAVA and PROLOG. We use XML Schema for describing the format for exchanging a knowledge base between PROLOG and JAVA. From the XML Schema desciption, we scaffold JAVA classes; the JAVA programmer can use them and fill in the open slots by statements accessing other JAVA data structures. The data structure on the JAVA side reflects the complex structured knowledge base, with which the PROLOG rules work, in an object–oriented way. We can to some extend verify the correctness of the data set / knowledge base sent from JAVA to PROLOG using standard methods for XML Schema. Moreover, we can add constraints that go beyond XML. For instance, we can specify standard integrity constraints known from relational databases, such as primary key, foreign key, and not–null constraints. Since we are dealing with complex structured XML data, however, there can be far more general integrity constraints. These can be expressed by standard PROLOG rules, which can be evaluated on the PROLOG side; they could also be compiled to JAVA by available PROLOG to JAVA converters such as Prolog Cafe – since they will usually be written in a supported subset of PROLOG. We have used our framework for integrating PROLOG business rules into a commercial E–Commerce system written in JAVA.
Future paths for integer programming and links to artificial intelligence

Integer programming has benefited from many innovations in models and methods. Some of the promising directions for elaborating these innovations in the future may be viewed from a framework that links the perspectives of artificial intelligence and operations research. To demonstrate this, four key areas are examined: (1) controlled randomization, (2) learning strategies, (3) induced decomposition and (4) tabu search. Each of these is shown to have characteristics that appear usefully relevant to developments on the horizon.
Dissociation of algorithmic and heuristic processes in language comprehension: Evidence from aphasia

Three groups of aphasic patients, Broca’s, Conduction, and Wernicke’s, and a nonaphasic patients control group were tested for comprehension ofobject-relative center-embedded sentences. The sentences were of three types: sentences in which semantic constraints between words allowed the subjects to assign a correct semantic reading of the sentence without decoding the syntax, sentences in which semantic constraints were relaxed and for which a correct reading was only possible with knowledge of syntactic relationships among words, and sentences which described highly improbable events. The subjects’ task was to choose which of two pictures captured the meaning expressed in the sentence. Broca’s and Conduction aphasics performed near perfectly on sentences where they could use semantic information. Their performance dropped to chance when they had to use syntactic information. These results support a neuropsychological dissociation of heuristic and algorithmic processes based primarily, though not exclusively, on semantic and syntactic information, respectively.
Design comparison of NdFeB and ferrite radial flux magnetic gears

Magnetic gears promise the benefits of mechanical gears with added advantages from contactless power transfer. Although most literature focuses on minimizing the size of magnetic gears, their material costs must also be reduced to achieve economic feasibility. This work compares the active material costs of NdFeB and ferrite radial flux magnetic gears with surface permanent magnets through a parametric 2D and 3D finite element analysis (FEA) study. Differences in optimal design trends such as pole count and magnet thicknesses are illustrated for the two materials. The results demonstrate that, for most historical price combination scenarios, NdFeB gear designs are capable of achieving lower active material costs than ferrite gear designs, and they are always capable of achieving much higher torque densities. Based on the selected design constraints, relative to a nominal ferrite cost of $10/kg, NdFeB must cost more than $90/kg before ferrite is cost competitive. Additionally, contour plots are provided to show the impact of material price rate variation on the cost break points.
Modeling Distributive and Integrative Negotiations . Review and Revised Characterization

The development of user-friendly negotiation support systems enabled negotiators to obtain advice directly from the system rather than via an intermediary. The emergence of e-commerce and the development of negotiating software agents further contributed to the automation of negotiation activities. These developments exposed inconsistencies in the descriptions of integrative and distributed negotiations. They also showed limitations of the existing modeling methods. These methods were designed to support negotiation experts who themselves had to make distinctions between distributive and integrative processes. Inconsistent descriptions and the lack of formal models that could be embedded in systems often contribute to a mechanical approach to negotiations compounding the difficulty in the design and development of software that can be used in real-life situations. The contradictions between the characteristics of integrative and distributive negotiations are discussed and assumptions for these two types as well as qualitative differences between them are proposed. Negotiation literature suggests that it is the negotiators’ perception of the problem that leads to the their focus on either distributive or integrative conflict resolution. This may be the case for negotiations that are not supported with software. In case of the latter it is the design principles and information processing that that differentiates these two types of negotiations. Negotiation representation based on the information requirements for different types of conflict is proposed. 1 Dr. Sunil Noronha’s (IBM T. J. Watson Research Center) contribution to this paper is gratefully acknowledged. This work has been supported by grants from the Natural Sciences and Engineering Research Council of Canada and from Concordia University. Modelling Distributive and Integrative Negotiations 2/25
Learning Linear Threshold Functions in the Presence of Classification Noise

I show that the linear threshold functions are polynomially learnable in the presence of classification noise, i.e., polynomial in <italic>n</italic>, 1/ε, 1/δ, and 1/&sgr;, where <italic>n</italic> is the number of Boolean attributes, ε and δ are the usual accuracy and confidence parameters, and &sgr; indicates the minimum distance of any example from the target hyperplane, which is assumed to be lower than the average distance of the examples from any hyperplane. This result is achieved by modifying the Perceptron algorithm—for each update, a weighted average of a sample of misclassified examples and a correction vector is added to the current weight vector. Similar modifications are shown for the Weighted Majority algorithm. The correction vector is simply   the mean of the normalized examples. In the special case of Boolean threshold functions, the modified Perceptron algorithm performs <italic>O</italic> (<italic>n</italic><supscrpt>2</supscrpt>ε<supscrpt>−2</supscrpt> ) iterations over <italic>O</italic>(<italic>n</italic><supscrpt>4</supscrpt>ε<supscrpt> −2</supscrpt>ln(<italic>n</italic>/(δε))) examples. This improves on the previous classification-noise result of Angluin and Laird to a much larger concept class with a similar number of examples, but with multiple iterations over the examples.
Using Neural Networks to Generate Inferential Roles for Natural Language

Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how the meanings of complex linguistic expressions, such as sentences, are understood. We argue that one thing such models need to be able to do is generate predictions about which further sentences are likely to follow from a given sentence; these define the sentence's "inferential role." We then show that it is possible to train a tree-structured neural network model to generate very simple examples of such inferential roles using the recently released Stanford Natural Language Inference (SNLI) dataset. On an empirical front, we evaluate the performance of this model by reporting entailment prediction accuracies on a set of test sentences not present in the training data. We also report the results of a simple study that compares human plausibility ratings for both human-generated and model-generated entailments for a random selection of sentences in this test set. On a more theoretical front, we argue in favor of a revision to some common assumptions about semantics: understanding a linguistic expression is not only a matter of mapping it onto a representation that somehow constitutes its meaning; rather, understanding a linguistic expression is mainly a matter of being able to draw certain inferences. Inference should accordingly be at the core of any model of semantic cognition.
PTBI: An efficient privacy-preserving biometric identification based on perturbed term in the cloud

Biometric identification has played an important role in achieving user authentication. For efficiency and economic savings, biometric data owners are motivated to outsource the biometric data and identification tasks to a third party, which however introduces potential threats to user’s privacy. In this paper, we propose a new privacy-preserving biometric identification scheme which can release the database owner from heavy computation burden. In the proposed scheme, we design concrete biometric data encryption and matching algorithms, and introduce perturb terms in each biometric data. A thorough analysis indicates that our schemes are secure, and the ultimate scheme offers a high level of privacy protection. In addition, the performance evaluations via extensive simulations demonstrate our schemes’ efficiency.
Reduced keyframes for fast bundle adjustment using point and line features in monoslam

Visual Simultaneous Localization and Mapping (VSLAM) requires feature detection on visual data. In indoor scenes that include architectures such as plain walls and doors, there are no or less corners detected, in such cases tracking will be lost. To overcome this situation we track different types of features that help in feature extraction even in textureless scenes. Line features are used to get the outline of the structure. This paper explains method of line based localization when no point features are detected and process of mapping the matched lines between two frames. One of the existing state of art monocular SLAM that extracts point features only, is used as the main build and LBD (Line Band Descriptor) is incorporated in it for localization and mapping. Our new system extracts more features compared to the existing system and complexity of the bundle adjustment is decreased as it decreases the keyframes selected for local mapping and optimization when tested using our own dataset and compared with the existing system.
On image classification: city images vs. landscapes

Grouping images into semantically meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, eeective indices can be built for an image database. In this paper, we show how a speciic high-level classiication problem (city images vs. landscapes) can be solved from relatively simple low-level features geared for the particular classes. We have developed a procedure to qualitatively measure the saliency of a feature towards a classiication problem based on the plot of the intra-class and inter-class distance distributions. We use this approach to determine the discriminative power of the following features: color histogram, color coherence vector, DCT coeecient, edge direction histogram, and edge direction coherence vector. We determine that the edge direction-based features have the most discriminative power for the classiication problem of interest here. A weighted k-NN classiier is used for the classiication which results in an accuracy of 93:9% when evaluated on an image database of 2; 716 images using the leave-one-out method. This approach has been extended to further classify 528 landscape images into forests, mountains, and sunset/sunrise classes. First, the input images are classiied as sunset/sunrise images vs. forest & mountain images (94:5% accuracy) and then the forest & mountain images are classiied as forest images or mountain images (91:7% accuracy). We are currently identifying further semantic classes to assign to images as well as extracting low level features which are salient for these classes. Our nal goal is to combine multiple 2-class classiiers into a single hierarchical classiier.