nils-herrmann/clean_scirepeval huggingface dataset:
Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.
The Influence of Feature Representation of Text on the Performance of Document Classification

In this paper we perform a comparative analysis of three models for feature representation of text documents in the context of document classification. In particular, we consider the most often used family of models bag-of-words, recently proposed continuous space models word2vec and doc2vec, and the model based on the representation of text documents as language networks. While the bag-of-word models have been extensively used for the document classification task, the performance of the other two models for the same task have not been well understood. This is especially true for the network-based model that have been rarely considered for representation of text documents for classification. In this study, we measure the performance of the document classifiers trained using the method of random forests for features generated the three models and their variants. The results of the empirical comparison show that the commonly used bag-of-words model has performance comparable to the one obtained by the emerging continuous-space model of doc2vec. In particular, the low-dimensional variants of doc2vec generating up to 75 features are among the top-performing document representation models. The results finally point out that doc2vec shows a superior performance in the tasks of classifying large Corresponding Author: Department of Informatics, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia, +385 51 584 714 Email addresses: smarti@uniri.hr (Sanda Martinčić-Ipšić), tanja.milicic@student.uniri.hr (Tanja Miličić), Todorovski@fu.uni-lj.si (Ljupčo Todorovski)  2017 documents.
Predicting Moves in Chess using Convolutional Neural Networks

We used a three layer Convolutional Neural Network (CNN) to make move predictions in chess. The task was defined as a two-part classification problem: a piece-selector CNN is trained to score which white pieces should be made to move, and move-selector CNNs for each piece produce scores for where it should be moved. This approach reduced the intractable class space in chess by a square root. The networks were trained using 20,000 games consisting of 245,000 moves made by players with an ELO rating higher than 2000 from the Free Internet Chess Server. The piece-selector network was trained on all of these moves, and the move-selector networks trained on all moves made by the respective piece. Black moves were trained on by using a data augmentation to frame it as a move made by the
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.
Understanding Information Systems Continuance: An Expectation-Confirmation Model

This paper examines cognitive beliefs and affect influencing ones intention to continue using (continuance) information systems (IS). Expectationconfirmation theory is adapted from the consumer behavior literature and integrated with theoretical and empirical findings from prior IS usage research to theorize a model of IS continuance. Five research hypotheses derived from this model are empirically validated using a field survey of online banking users. The results suggest that users continuance intention is determined by their satisfaction with IS use and perceived usefulness of continued IS use. User satisfaction, in turn, is influenced by their confirmation of expectation from prior IS use and perceived usefulness. Postacceptance perceived usefulness is influenced by Ron Weber was the accepting senior editor for this paper. users confirmation level. This study draws attention to the substantive differences between acceptance and continuance behaviors, theorizes and validates one of the earliest theoretical models of IS continuance, integrates confirmation and user satisfaction constructs within our current understanding of IS use, conceptualizes and creates an initial scale for measuring IS continuance, and offers an initial explanation for the acceptancediscontinuance anomaly.
Comparative Analysis of the Composition and Active Property Evaluation of Certain Essential Oils to Assess their Potential Applications in Active Food Packaging

The antifungal, antibacterial, and antioxidant activity of four commercial essential oils (EOs) (thyme, clove, rosemary, and tea tree) from Romanian production were studied in order to assess them as bioactive compounds for active food packaging applications. The chemical composition of the oils was determined with the Folin-Ciocâlteu method and gas chromatography coupled with mass spectrometry and flame ionization detectors, and it was found that they respect the AFNOR/ISO standard limits. The EOs were tested against three food spoilage fungi-Fusarium graminearum, Penicillium corylophilum, and Aspergillus brasiliensis-and three potential pathogenic food bacteria-Staphylococcus aureus, Escherichia coli, and Listeria monocytogenes-using the disc diffusion method. It was found that the EOs of thyme, clove, and tea tree can be used as antimicrobial agents against the tested fungi and bacteria, thyme having the highest inhibitory effect. Concerning antioxidant activity determined by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis 3-ethylbenzthiazoline-6-sulfonic acid (ABTS) methods, it has been established that the clove oil exhibits the highest activity because of its high phenolic content. Promising results were obtained by their incorporation into chitosan emulsions and films, which show potential for food packaging. Therefore, these essential oils could be suitable alternatives to chemical additives, satisfying the consumer demand for naturally preserved food products ensuring its safety.
Semantic search engine : A survey

The semantic search engines have some advantages on the web search engines from the users view. In this fast life everybody need the answer for their queries very fast .In this scenario semantic searching engines will be helpful. It deals with the actual meaning of the queries. The tremendous growth in the volume of data or the information lead the traditional search engines to get the answers syntactically correct but large in amount. That might be the reason to get into the semantic search engines which gives the selected results which the user searching for. So here in this paper, a survey is done about the semantic search engines to revel the promising features of the semantic search engines(SSE).It deals about the description of some of the best semantic search engines .
Distributed Information Retrieval

A multi database model of distributed information retrieval is presented in which people are assumed to have access to many searchable text databases In such an environment full text information retrieval consists of discovering database contents ranking databases by their expected ability to satisfy the query searching a small number of databases and merging results returned by di erent databases This paper presents algorithms for each task It also discusses how to reorganize conventional test collections into multi database testbeds and evaluation methodologies for multi database experiments A broad and diverse group of experimental results is presented to demonstrate that the algorithms are e ective e cient robust and scalable
Human Detection Using Oriented Histograms of Flow and Appearance

Detecting humans in films and videos is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. We develop a detector for standing and moving people in videos with possibly moving cameras and backgrounds, testing several different motion coding schemes and showing empirically that orientated histograms of differential optical flow give the best overall performance. These motion-based descriptors are combined with our Histogram of Oriented Gradient appearance descriptors. The resulting detector is tested on several databases including a challenging test set taken from feature films and containing wide ranges of pose, motion and background variations, including moving cameras and backgrounds. We validate our results on two challenging test sets containing more than 4400 human examples. The combined detector reduces the false alarm rate by a factor of 10 relative to the best appearance-based detector, for example giving false alarm rates of 1 per 20,000 windows tested at 8% miss rate on our Test Set 1.
FOTS: Fast Oriented Text Spotting with a Unified Network

Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community. Most existing methods treat text detection and recognition as separate tasks. In this work, we propose a unified end-to-end trainable Fast Oriented Text Spotting (FOTS) network for simultaneous detection and recognition, sharing computation and visual information among the two complementary tasks. Specifically, RoIRotate is introduced to share convolutional features between detection and recognition. Benefiting from convolution sharing strategy, our FOTS has little computation overhead compared to baseline text detection network, and the joint training method makes our method perform better than these two-stage methods. Experiments on ICDAR 2015, ICDAR 2017 MLT, and ICDAR 2013 datasets demonstrate that the proposed method outperforms state-of-the-art methods significantly, which further allows us to develop the first real-time oriented text spotting system which surpasses all previous state-of-the-art results by more than 5% on ICDAR 2015 text spotting task while keeping 22.6 fps.
MoodLens: an emoticon-based sentiment analysis system for chinese tweets

Recent years have witnessed the explosive growth of online social media. Weibo, a Twitter-like online social network in China, has attracted more than 300 million users in less than three years, with more than 1000 tweets generated in every second. These tweets not only convey the factual information, but also reflect the emotional states of the authors, which are very important for understanding user behaviors. However, a tweet in Weibo is extremely short and the words it contains evolve extraordinarily fast. Moreover, the Chinese corpus of sentiments is still very small, which prevents the conventional keyword-based methods from being used. In light of this, we build a system called MoodLens, which to our best knowledge is the first system for sentiment analysis of Chinese tweets in Weibo. In MoodLens, 95 emoticons are mapped into four categories of sentiments, i.e. angry, disgusting, joyful, and sad, which serve as the class labels of tweets. We then collect over 3.5 million labeled tweets as the corpus and train a fast Naive Bayes classifier, with an empirical precision of 64.3%. MoodLens also implements an incremental learning method to tackle the problem of the sentiment shift and the generation of new words. Using MoodLens for real-time tweets obtained from Weibo, several interesting temporal and spatial patterns are observed. Also, sentiment variations are well captured by MoodLens to effectively detect abnormal events in China. Finally, by using the highly efficient Naive Bayes classifier, MoodLens is capable of online real-time sentiment monitoring. The demo of MoodLens can be found at http://goo.gl/8DQ65.
Knowledge Graph Completion via Complex Tensor Factorization

In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs—labeled directed graphs— and predicting missing relationships—labeled edges. State-of-the-art embedding models propose different trade-offs between modeling expressiveness, and time and space complexity. We reconcile both expressiveness and complexity through the use of complex-valued embeddings and explore the link between such complex-valued embeddings and unitary diagonalization. We corroborate our approach theoretically and show that all real square matrices—thus all possible relation/adjacency matrices—are the real part of some unitarily diagonalizable matrix. This results opens the door to a lot of other applications of square matrices factorization. Our approach based on complex embeddings is arguably simple, as it only involves a Hermitian dot product, the complex counterpart of the standard dot product between real vectors, whereas other methods resort to more and more complicated composition functions to increase their expressiveness. The proposed complex embeddings are scalable to large data sets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.
Body odor attractiveness as a cue of impending ovulation in women: Evidence from a study using hormone-confirmed ovulation

Scent communication plays a central role in the mating behavior of many nonhuman mammals but has often been overlooked in the study of human mating. However, a growing body of evidence suggests that men may perceive women's high-fertility body scents (collected near ovulation) as more attractive than their low-fertility body scents. The present study provides a methodologically rigorous replication of this finding, while also examining several novel questions. Women collected samples of their natural body scent twice--once on a low-fertility day and once on a high-fertility day of the ovulatory cycle. Tests of luteinizing hormone confirmed that women experienced ovulation within two days of their high-fertility session. Men smelled each woman's high- and low-fertility scent samples and completed discrimination and preference tasks. At above-chance levels, men accurately discriminated between women's high- and low-fertility scent samples (61%) and chose women's high-fertility scent samples as more attractive than their low-fertility scent samples (56%). Men also rated each scent sample on sexiness, pleasantness, and intensity, and estimated the physical attractiveness of the woman who had provided the sample. Multilevel modeling revealed that, when high- and low-fertility scent samples were easier to discriminate from each other, high-fertility scent samples received even more favorable ratings compared with low-fertility scent samples. This study builds on a growing body of evidence indicating that men are attracted to cues of impending ovulation in women and raises the intriguing question of whether women's cycling hormones influence men's attraction and sexual approach behavior.
Caveats for using statistical significance tests in research assessments

This article raises concerns about the advantages of using statistical significance tests in research assessments as has recently been suggested in the debate about proper normalization procedures for citation indicators by Opthof and Leydesdorff (2010). Statistical significance tests are highly controversial and numerous criticisms have been leveled against their use. Based on examples from articles by proponents of the use statistical significance tests in research assessments, we address some of the numerous problems with such tests. The issues specifically discussed are the ritual practice of such tests, their dichotomous application in decision making, the difference between statistical and substantive significance, the implausibility of most null hypotheses, the crucial assumption of randomness, as well as the utility of standard errors and confidence intervals for inferential purposes. We argue that applying statistical significance tests and mechanically adhering to their results are highly problematic and detrimental to critical thinking. We claim that the use of such tests do not provide any advantages in relation to deciding whether differences between citation indicators are important or not. On the contrary their use may be harmful. Like many other critics, we generally believe that statistical significance tests are overand misused in the empirical sciences including scientometrics and we encourage a reform on these matters.  We warn against the use of statistical significance tests (NHST) in research assessments. We introduce the controversial debate of NHST to the informetric community. We demonstrate some of the numerous flaws, misconceptions and misuses of NHST. We discuss potential alternatives and conclude that no "easy fixes" exist. We advocate informed judgment, free of the NHST-ritual, in decision processes. Acknowledgement. The author would like to thank the three anonymous reviewers for their very useful comments on previous drafts of this article.
Factors Affecting Students ’ Acceptance of e-Learning Environments in Developing Countries : A Structural Equation Modeling Approach

—A number of studies have shown that e-learning implementation is not simply a technological solution, but a process of many different factors such as social and behavioural contexts. Yet little is known about the important rule of such factors in technology adoption and use in the context of developing countries such as Lebanon. Therefore, the main objective of our study is to empirically validate an extended Technology Acceptance Model (TAM) (to include Social Norms and Quality of Work Life constructs) in the Lebanese context. A quantitative methodology approach was adopted in this study. To test the hypothesized research model, data were collected from 569 undergraduate and postgraduate students studying in Lebanon via questionnaire. The collected data were analysed using structural equation modeling (SEM) technique based on AMOS methods and in conjunction with multi-group analysis. As hypothesized, the results of the study revealed perceived usefulness (PU), perceived ease of use (PEU), social norms (SN) and Quality of Work life (QWL) to be significant determinants of students' behavioral intention (BI). This provides support for the applicability of the extended TAM in the Lebanese context. Implications to both theory and practice of this study are discussed at the end of the paper.
Notice of Violation of IEEE Publication PrinciplesHybrid recommender systems: Survey and experiments

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, system that combines content-based recommendation and collaborative filtering to recommend restaurants.
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading

This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading. Traditional methods that exploit knowledge from KBs encode knowledge as discrete indicator features. Not only do these features generalize poorly, but they require task-specific feature engineering to achieve good performance. We propose KBLSTM, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading. To effectively integrate background knowledge with information from the currently processed text, our model employs an attention mechanism with a sentinel to adaptively decide whether to attend to background knowledge and which information from KBs is useful. Experimental results show that our model achieves accuracies that surpass the previous state-of-the-art results for both entity extraction and event extraction on the widely used ACE2005 dataset.