nils-herrmann/clean_scirepeval huggingface dataset:
Measuring Enjoyment of Computer Game Play

This paper reports on the development of an instrument designed to measure the enjoyment of computer game play. Despite the enormous technological progress in the field of computer games, enjoyment of computer game play is still not a welldefined construct. Based on Nabi and Krcmar’s (2004) tripartite model of media enjoyment, a survey questionnaire was developed to measure computer game players’ affective, behavioral, and cognitive reactions. Expert consultation, exploratory and confirmatory card sorting sessions were used to refine the instrument. A survey of computer game players was subsequently conducted to test the instrument. Reliabilities and construct validities were analyzed. Findings and their implications were discussed.
Differential remoteness and emotional tone modulate the neural correlates of autobiographical memory.

Autobiographical memory relies on complex interactions between episodic memory contents, associated emotions and a sense of self-continuity along the time axis of one's life history. The neural correlates underlying autobiographical memory are known to primarily comprise areas of prefrontal cortex, medial and lateral temporal cortex, as well as posterior cingulate and retrosplenial cortex. By contrast, the effect of encoding and/or storage parameters such as the emotional tone of the memories retrieved or the length of the time-interval between the initial encoding of information and retrieval remains to be clarified. Using blocked design functional MRI and statistical parametric mapping, we investigated the impact of remoteness (factor 1: recent, remote) and emotional valence (factor 2: positive, negative) on the neural correlates of autobiographical memory retrieval. Changes in neural activity (P < 0.05, corrected) related to autobiographical memory retrieval (irrespective of remoteness and emotional tone) relative to baseline were observed bilaterally in medial and lateral temporal, temporal-occipital, posterior cingulate and frontal cortices. Recent (relative to remote) memories were associated with differentially increased neural activity bilaterally in the retrosplenial cortex and the hippocampal region, whereas remote (relative to recent) memories did not show any statistically significant differential neural activations. Positive (relative to negative) memories bilaterally activated the orbitofrontal cortex, the temporal pole, as well as medial temporal areas, with the activation peak being in the entorhinal region. By contrast, negative (relative to positive) memories differentially increased neural activity in the right middle temporal gyrus only. The data suggest differential functional roles for temporal, prefrontal and retrosplenial regions during autobiographical memory retrieval depending on the remoteness and the emotional valence of the memories retrieved. In particular, our findings support the 'classic' model of long-term memory processing, which suggests a time-limited differential involvement of the hippocampus in memory consolidation. Interestingly, the observation of such a time-dependent involvement of the hippocampal region in memory consolidation corresponds to the course of retrograde amnesia observed in demented patients, with the loss of recent memories appearing during early stages of the disease when conspicuous neurofibrillary changes are restricted mainly to the hippocampal and parahippocampal regions. Only during later stages, as the neurofibrillary changes spread out to neocortical association areas, do remote memories also become impaired. We conclude that the brain regions involved in autobiographical memory retrieval are influenced by the triggered memories' emotional significance and their relationship to the individual time axis.
The calvaria of Sangiran 38, Sendangbusik, Sangiran Dome, Java.

We describe in detail Sangiran 38 (S38), an adult partial calvaria recovered in 1980 from the Bapang (Kabuh) Formation of the Sangiran Dome near the hamlet of Sendangbusik, Java. Several other hominins (Bukuran, Hanoman 1, and Bs 9706) recovered in the vicinity come from either the upper-most Sangiran (Pucangan) or lower-most Bapang formations. S38 is from the lower Bapang Formation, which (40)Ar/(39)Ar age estimates suggest spans between 1.47 and 1.58Ma. Anatomical and metric comparisons with a worldwide set of 'early non-erectus'Homo, and Homo erectus (sensu lato) fossils indicate S38 is best considered a member of H. erectus. Although smaller in size, S38 is similar in overall morphology to the Bukuran specimen of similar age and provenance. The S38 calvaria exhibits several depressed lesions of the vault consistent with a scalp or systemic infection or soft tissue cyst.
Differences in perceptions of communication quality between a Twitterbot and human agent for information seeking and learning

Twitter’s design allows the implementation of automated programs that can submit tweets, interact with others, and generate content based on algorithms. Scholars and end-users alike refer to these programs to as “Twitterbots.” This two-part study explores the differences in perceptions of communication quality between a human agent and a Twitterbot in the areas of cognitive elaboration, information seeking, and learning outcomes. In accordance with the Computers Are Social Actors (CASA) framework (Reeves & Nass, 1996), results suggest that participants learned the same from either a Twitterbot or a human agent. Results are discussed in light of CASA, as well as implications and directions for future studies.
Fast and Provably Good Seedings for k-Means

Seeding – the task of finding initial cluster centers – is critical in obtaining highquality clusterings for k-Means. However, k-means++ seeding, the state of the art algorithm, does not scale well to massive datasets as it is inherently sequential and requires k full passes through the data. It was recently shown that Markov chain Monte Carlo sampling can be used to efficiently approximate the seeding step of k-means++. However, this result requires assumptions on the data generating distribution. We propose a simple yet fast seeding algorithm that produces provably good clusterings even without assumptions on the data. Our analysis shows that the algorithm allows for a favourable trade-off between solution quality and computational cost, speeding up k-means++ seeding by up to several orders of magnitude. We validate our theoretical results in extensive experiments on a variety of real-world data sets.
Overview of the NLPCC-ICCPOL 2016 Shared Task: Open Domain Chinese Question Answering

In this paper, we give the overview of the open domain Question Answering (or open domain QA) shared task in the NLPCC-ICCPOL 2016. We first review the background of QA, and then describe two open domain Chinese QA tasks in this year’s NLPCC-ICCPOL, including the construction of the benchmark datasets and the evaluation metrics. The evaluation results of submissions from participating teams are presented in the experimental part.
A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges

Visual analytics employs interactive visualizations to integrate users’ knowledge and inference capability into numerical/algorithmic data analysis processes. It is an active research field that has applications in many sectors, such as security, finance, and business. The growing popularity of visual analytics in recent years creates the need for a broad survey that reviews and assesses the recent developments in the field. This report reviews and classifies recent work into a set of application categories including space and time, multivariate, text, graph and network, and other applications. More importantly, this report presents analytics space, inspired by design space, which relates each application category to the key steps in visual analytics, including visual mapping, model-based analysis, and user interactions. We explore and discuss the analytics space to add the current understanding and better understand research trends in the field.
Modeling and simulation of a PV pumping system under real climatic conditions

This article presents the modeling and simulation of a PV pumping system operating over the sun which has been developed under PSIM environment. The main objective is to optimize the PV pumping system through the introduction of two stages. The first one concerns the insertion of a DC-DC converter working with an MPPT in order to get the maximum power from the PV array. The second concerns the optimization of the DC-AC inverter which is controlled through a PWM control in order to get good voltage and current outputs spectrum with reduced losses and harmonics rate. The improvements brought by the MPPT and the PWM on the PV pumping system are shown through the example treated under real climatic conditions.
Product aspect ranking using sentiment analysis and TOPSIS

The explosive growth of customer reviews on e-commerce websites has inspired many researchers to explore the problem of identifying the product aspects that have been mentioned in online reviews. Most of the conducted research studies extract the product aspects based on three main criteria: 1) extracting the aspects that have been commented repeatedly in online reviews, 2) determining of important aspects as those that have been described positively and negatively by many customers in their reviews, and 3) the association between the domain product aspect (like `camera') and the other aspects contained in a specific product review. However, a lacuna remains as how to efficiently investigate online reviews to identify the most important product aspects by considering all the three criteria jointly. In response, this paper proposes a novel product aspect ranking framework using sentiment analysis and TOPSIS (Technique for Order Performance by Similarity to Ideal Solution). The proposed work is decomposed into two stages: aspect extraction and aspect ranking. In aspect extraction stage, sentiment analysis is used to identify the product aspects from customer reviews in an unsupervised manner based on the three criteria of extraction. In the second stage, the extracted product aspects from the previous criteria have been involved simultaneously in TOPSIS to produce a ranked list of the most representative product aspects. The empirical evaluation of the proposed work using online reviews of four products shows its effectiveness in finding representative aspects.
Effects of growth mode and pyruvate carboxylase on succinic acid production by metabolically engineered strains of Escherichia coli.

Escherichia coli NZN111, which lacks activities for pyruvate-formate lyase and lactate dehydrogenase, and AFP111, a derivative which contains an additional mutation in ptsG (a gene encoding an enzyme of the glucose phophotransferase system), accumulate significant levels of succinic acid (succinate) under anaerobic conditions. Plasmid pTrc99A-pyc, which expresses the Rhizobium etli pyruvate carboxylase enzyme, was introduced into both strains. We compared growth, substrate consumption, product formation, and activities of seven key enzymes (acetate kinase, fumarate reductase, glucokinase, isocitrate dehydrogenase, isocitrate lyase, phosphoenolpyruvate carboxylase, and pyruvate carboxylase) from glucose for NZN111, NZN111/pTrc99A-pyc, AFP111, and AFP111/pTrc99A-pyc under both exclusively anaerobic and dual-phase conditions (an aerobic growth phase followed by an anaerobic production phase). The highest succinate mass yield was attained with AFP111/pTrc99A-pyc under dual-phase conditions with low pyruvate carboxylase activity. Dual-phase conditions led to significant isocitrate lyase activity in both NZN111 and AFP111, while under exclusively anaerobic conditions, an absence of isocitrate lyase activity resulted in significant pyruvate accumulation. Enzyme assays indicated that under dual-phase conditions, carbon flows not only through the reductive arm of the tricarboxylic acid cycle for succinate generation but also through the glyoxylate shunt and thus provides the cells with metabolic flexibility in the formation of succinate. Significant glucokinase activity in AFP111 compared to NZN111 similarly permits increased metabolic flexibility of AFP111. The differences between the strains and the benefit of pyruvate carboxylase under both exclusively anaerobic and dual-phase conditions are discussed in light of the cellular constraint for a redox balance.
Epigenetics and Signaling Pathways in Glaucoma

Glaucoma is the most common cause of irreversible blindness worldwide. This neurodegenerative disease becomes more prevalent with aging, but predisposing genetic and environmental factors also contribute to increased risk. Emerging evidence now suggests that epigenetics may also be involved, which provides potential new therapeutic targets. These three factors work through several pathways, including TGF-β, MAP kinase, Rho kinase, BDNF, JNK, PI-3/Akt, PTEN, Bcl-2, Caspase, and Calcium-Calpain signaling. Together, these pathways result in the upregulation of proapoptotic gene expression, the downregulation of neuroprotective and prosurvival factors, and the generation of fibrosis at the trabecular meshwork, which may block aqueous humor drainage. Novel therapeutic agents targeting these pathway members have shown preliminary success in animal models and even human trials, demonstrating that they may eventually be used to preserve retinal neurons and vision.
SEISA: set expansion by iterative similarity aggregation

In this paper, we study the problem of expanding a set of given seed entities into a more complete set by discovering other entities that also belong to the same concept set. A typical example is to use "Canon" and "Nikon" as seed entities, and derive other entities (e.g., "Olympus") in the same concept set of camera brands. In order to discover such relevant entities, we exploit several web data sources, including lists extracted from web pages and user queries from a web search engine. While these web data are highly diverse with rich information that usually cover a wide range of the domains of interest, they tend to be very noisy. We observe that previously proposed random walk based approaches do not perform very well on these noisy data sources. Accordingly, we propose a new general framework based on iterative similarity aggregation, and present detailed experimental results to show that, when using general-purpose web data for set expansion, our approach outperforms previous techniques in terms of both precision and recall.
Measurement and analysis of online social networks

Online social networking sites like Orkut, YouTube, and Flickr are among the most popular sites on the Internet. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks.
 This paper presents a large-scale measurement study and analysis of the structure of multiple online social networks. We examine data gathered from four popular online social networks: Flickr, YouTube, LiveJournal, and Orkut. We crawled the publicly accessible user links on each site, obtaining a large portion of each social network's graph. Our data set contains over 11.3 million users and 328 million links. We believe that this is the first study to examine multiple online social networks at scale.
 Our results confirm the power-law, small-world, and scale-free properties of online social networks. We observe that the indegree of user nodes tends to match the outdegree; that the networks contain a densely connected core of high-degree nodes; and that this core links small groups of strongly clustered, low-degree nodes at the fringes of the network. Finally, we discuss the implications of these structural properties for the design of social network based systems.
Quantum Amplitude Amplification and Estimation

Consider a Boolean function χ : X → {0, 1} that partitions set X between its good and bad elements, where x is good if χ(x) = 1 and bad otherwise. Consider also a quantum algorithm A such that A|0〉 =∑x∈X αx|x〉 is a quantum superposition of the elements of X, and let a denote the probability that a good element is produced if A|0〉 is measured. If we repeat the process of running A, measuring the output, and using χ to check the validity of the result, we shall expect to repeat 1/a times on the average before a solution is found. Amplitude amplification is a process that allows to find a good x after an expected number of applications of A and its inverse which is proportional to 1/ √ a, assuming algorithm A makes no measurements. ∗ Département IRO, Université de Montréal, C.P. 6128, succursale centre-ville, Montréal (Québec), Canada H3C 3J7. email: brassard@iro.umontreal.ca. Supported in part by Canada’s Nserc and Québec’s Fcar. † BRICS, Department of Computer Science, University of Aarhus, Ny Munkegade, Bldg. 540, DK-8000 Aarhus C, Denmark.  CACR, Department of C&O, Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1. email: mmosca@cacr.math.uwaterloo.ca. Most of this work was done while at Centre for Quantum Computation, Clarendon Laboratory, University of Oxford. Supported in part by Canada’s Nserc and UK’s Cesg. § CACR, email: atapp@cacr.math.uwaterloo.ca. Most of this work was done while at Département IRO, Université de Montréal. Supported in part by a postdoctoral fellowship from Canada’s nserc. This is a generalization of Grover’s searching algorithm in which A was restricted to producing an equal superposition of all members of X and we had a promise that a single x existed such that χ(x) = 1. Our algorithm works whether or not the value of a is known ahead of time. In case the value of a is known, we can find a good x after a number of applications of A and its inverse which is proportional to 1/ √ a even in the worst case. We show that this quadratic speedup can also be obtained for a large family of search problems for which good classical heuristics exist. Finally, as our main result, we combine ideas from Grover’s and Shor’s quantum algorithms to perform amplitude estimation, a process that allows to estimate the value of a. We apply amplitude estimation to the problem of approximate counting, in which we wish to estimate the number of x ∈ X such that χ(x) = 1. We obtain optimal quantum algorithms in a variety of settings.
Multi-modality web video categorization

This paper reports a first comprehensive study and large-scale test on web video (so-called user generated video or micro video) categorization. Observing that web videos are characterized by a much higher diversity of quality, subject, style, and genres compared with traditional video programs, we focus on studying the effectiveness of different modalities in dealing with such high variation. Specifically, we propose two novel modalities including a semantic modality and a surrounding text modality, as effective complements to most commonly used low-level features. The semantic modality includes three feature representations, i.e., concept histogram, visual word vector model and visual word Latent Semantic Analysis (LSA), while text modality includes the titles, descriptions and tags of web videos. We conduct a set of comprehensive experiments for evaluating the effectiveness of the proposed feature representations over different classifiers such as Support Vector Machine (SVM), Gaussian Mixture Model (GMM) and Manifold Ranking (MR). Our experiments on a large-scale dataset with 11k web videos (nearly 450 hours in total) demonstrate that (1) the proposed multimodal feature representation is effective for web video categorization, and (2) SVM outperforms GMM and MR on nearly all the modalities.
Cascading Bandits for Large-Scale Recommendation Problems

Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend K most attractive items from a large set of L candidate items. We propose two algorithms for solving this problem, which are based on the idea of linear generalization. The key idea in our solutions is that we learn a predictor of the attraction probabilities of items from their features, as opposing to learning the attraction probability of each item independently as in the existing work. This results in practical learning algorithms whose regret does not depend on the number of items L. We bound the regret of one algorithm and comprehensively evaluate the other on a range of recommendation problems. The algorithm performs well and outperforms all baselines.
On the Direction of Arrival (DoA) Estimation for a Switched-Beam Antenna System Using Neural Networks

A generic direction of arrival (DoA) estimation methodology is presented that is based on neural networks (NNs) and designed for a switched-beam system (SBS). The method incorporates the benefits of NNs and SBSs to achieve DoA estimation in a less complex and expensive way compared to the corresponding widely known super resolution algorithms. The proposed technique is step-by-step developed and thoroughly studied and explained, especially in terms of the beam pattern structure and the neuro-computational procedures. Emphasis is given on the direct sequence code division multiple access (DS-CDMA) applications, and particularly the Universal Mobile Telecommunication System (UMTS). Extensive simulations are realized for each step of the method, demonstrating its performance. It is shown that a properly trained NN can accurately find the signal of interest (SoI) angle of arrival at the presence of a varying number of mobile users and a varying SoI to interference ratio. The proposed NN-SBS DoA estimation method can be applied to current cellular communications base stations, promoting the wider use of smart antenna beamforming.
Measuring discrimination in algorithmic decision making

Society is increasingly relying on data-driven predictive models for automated decision making. This is not by design, but due to the nature and noisiness of observational data, such models may systematically disadvantage people belonging to certain categories or groups, instead of relying solely on individual merits. This may happen even if the computing process is fair and well-intentioned. Discrimination-aware data mining studies of how to make predictive models free from discrimination, when the historical data, on which they are built, may be biased, incomplete, or even contain past discriminatory decisions. Discrimination-aware data mining is an emerging research discipline, and there is no firm consensus yet of how to measure the performance of algorithms. The goal of this survey is to review various discrimination measures that have been used, analytically and computationally analyze their performance, and highlight implications of using one or another measure. We also describe measures from other disciplines, which have not been used for measuring discrimination, but potentially could be suitable for this purpose. This survey is primarily intended for researchers in data mining and machine learning as a step towards producing a unifying view of performance criteria when developing new algorithms for non-discriminatory predictive modeling. In addition, practitioners and policy makers could use this study when diagnosing potential discrimination by predictive models.