nils-herrmann/clean_scirepeval huggingface dataset:
Effects of chocolate on cognitive function and mood: a systematic review.

A systematic review was conducted to evaluate whether chocolate or its constituents were capable of influencing cognitive function and/or mood. Studies investigating potentially psychoactive fractions of chocolate were also included. Eight studies (in six articles) met the inclusion criteria for assessment of chocolate or its components on mood, of which five showed either an improvement in mood state or an attenuation of negative mood. Regarding cognitive function, eight studies (in six articles) met the criteria for inclusion, of which three revealed clear evidence of cognitive enhancement (following cocoa flavanols and methylxanthine). Two studies failed to demonstrate behavioral benefits but did identify significant alterations in brain activation patterns. It is unclear whether the effects of chocolate on mood are due to the orosensory characteristics of chocolate or to the pharmacological actions of chocolate constituents. Two studies have reported acute cognitive effects of supplementation with cocoa polyphenols. Further exploration of the effect of chocolate on cognitive facilitation is recommended, along with substantiation of functional brain changes associated with the components of cocoa.
A Rewritable, Random-Access DNA-Based Storage System

We describe the first DNA-based storage architecture that enables random access to data blocks and rewriting of information stored at arbitrary locations within the blocks. The newly developed architecture overcomes drawbacks of existing read-only methods that require decoding the whole file in order to read one data fragment. Our system is based on new constrained coding techniques and accompanying DNA editing methods that ensure data reliability, specificity and sensitivity of access, and at the same time provide exceptionally high data storage capacity. As a proof of concept, we encoded parts of the Wikipedia pages of six universities in the USA, and selected and edited parts of the text written in DNA corresponding to three of these schools. The results suggest that DNA is a versatile media suitable for both ultrahigh density archival and rewritable storage applications.
V for Vocab: An Intelligent Flashcard Application

Students choose to use flashcard applications available on the Internet to help memorize word-meaning pairs. This is helpful for tests such as GRE, TOEFL or IELTS, which emphasize on verbal skills. However, monotonous nature of flashcard applications can be diminished with the help of Cognitive Science through Testing Effect. Experimental evidences have shown that memory tests are an important tool for long term retention (Roediger and Karpicke, 2006). Based on these evidences, we developed a novel flashcard application called “V for Vocab” that implements short answer based tests for learning new words. Furthermore, we aid this by implementing our short answer grading algorithm which automatically scores the user’s answer. The algorithm makes use of an alternate thesaurus instead of traditional Wordnet and delivers state-of-theart performance on popular word similarity datasets. We also look to lay the foundation for analysis based on implicit data collected from our application.
The influence of reading speed and line length on the effectiveness of reading from screen

With such a large volume of material accessible from the World Wide Web, there is an urgent need to increase our knowledge of factors in#uencing reading from screen. We investigate the e!ects of two reading speeds (normal and fast) and di!erent line lengths on comprehension, reading rate and scrolling patterns. Scrolling patterns are de"ned as the way in which readers proceed through the text, pausing and scrolling. Comprehension and reading rate are also examined in relation to scrolling patterns to attempt to identify some characteristics of e!ective readers. We found a reduction in overall comprehension when reading fast, but the type of information recalled was not dependent on speed. A medium line length (55 characters per line) appears to support e!ective reading at normal and fast speeds. This produced the highest level of comprehension and was also read faster than short lines. Scrolling patterns associated with better comprehension (more time in pauses and more individual scrolling movements) contrast with scrolling patterns used by faster readers (less time in pauses between scrolling). Consequently, e!ective readers can only be de"ned in relation to the aims of the reading task, which may favour either speed or accuracy.
Filicide-suicide: common factors in parents who kill their children and themselves.

The purpose of this phenomenological study was to identify commonly occurring factors in filicide-suicide offenders, to describe this phenomenon better, and ultimately to enhance prevention of child murder. Thirty families' files from a county coroner's office were reviewed for commonly occurring factors in cases of filicide-suicide. Parental motives for filicide-suicide included altruistic and acutely psychotic motives. Twice as many fathers as mothers committed filicide-suicide during the study period, and older children were more often victims than infants. Records indicated that parents frequently showed evidence of depression or psychosis and had prior mental health care. The data support the hypothesis that traditional risk factors for violence appear different from commonly occurring factors in filicide-suicide. This descriptive study represents a step toward understanding filicide-suicide risk.
Reputation and Feedback Systems in Online Platform Markets ∗

Online marketplaces have become ubiquitous as sites like eBay, Taobao, Uber and AirBnB are frequented by billions of users regularly. The success of these marketplaces is attributed not only to the ease in which buyers can find sellers, but also to the fact that they provide reputation and feedback systems that help facilitate trust. I begin by briefly describing the basic ideas of how reputation helps facilitate trust and trade, and offer an overview of how feedback and reputation systems work in online marketplaces. I then describe the literature that explores the effects of reputation and feedback systems on online marketplaces, and highlight some of the problems of bias in feedback and reputation systems as they appear today. I discuss ways to address these problems in order to improve the practical design of online marketplaces and suggest some directions for future research. ∗I am grateful to Oren Reshef for very helpful research assistance and to Tim Bresnahan, the Editor, for helpful feedback on an earlier draft.
Fast and Compact Hash Tables for Integer Keys

A hash table is a fundamental data structure in computer science that can offer rapid storage and retrieval of data. A leading implementation for string keys is the cacheconscious array hash table. Although fast with strings, there is currently no information in the research literatur e on its performance with integer keys. More importantly, we do not know how efficient an integer-based array hash table is compared to other hash tables that are designed for integers, such as bucketized cuckoo hashing. In this paper, we explain how to efficiently implement an array hash table for integers. We then demonstrate, through careful experimental evaluations, which hash table, whether it be a bucketized cuckoo hash table, an array hash table, or alternative hash table schemes such as linear probing, offers the best performance—with respect to time and space— for maintaining a large dictionary of integers in-memory, on a current cache-oriented processor.
Variable Speed Wind Turbines for Power System Stability Enhancement

This paper investigates possible improvements in grid voltage stability and transient stability with wind energy converter units using modified P/Q control. The voltage source converter (VSC) in modern variable speed wind turbines is utilized to achieve this enhancement. The findings show that using only available hardware for variable-speed turbines improvements could be obtained in all cases. Moreover, it was found that power system stability improvement is often larger when the control is modified for a given variable speed wind turbine rather than when standard variable speed turbines are used instead of fixed speed turbines. To demonstrate that the suggested modifications can be incorporated in real installations, a real situation is presented where short-term voltage stability is improved as an additional feature of an existing VSC high voltage direct current (HVDC) installation
An Early Performance Analysis of Cloud Computing Services for Scientific Computing

Cloud Computing is emerging today as a commercial infrastructure that eliminates the need for maintaining expensive computing hardware. Through the use of virtualization, clouds promise to address with the same shared set of physical resources a large user base with different needs. Thus, clouds promise to be for scientists an alternative to clusters, grids, and supercomputers. However, virtualization may induce significant performance penalties for the demanding scientific computing workloads. In this work we present an evaluation of the usefulness of the current cloud computing services for scientific computing. We analyze the performance of the Amazon EC2 platform using micro-benchmarks, kernels, and e-Science workloads. We also compare using long-term traces the performance characteristics and cost models of clouds with those of other platforms accessible to scientists. While clouds are still changing, our results indicate that the current cloud services need an order of magnitude in performance improvement to be useful to the scientific community. Wp 1 http://www.pds.ewi.tudelft.nl/∼iosup/ S. Ostermann et al. Wp Early Cloud Computing EvaluationWp PDS
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation

Latent Dirichlet allocation (LDA) is a Bayesian network tha has recently gained much popularity in applications ranging from document mode ling to computer vision. Due to the large scale nature of these applications, current inference procedures like variational Bayes and Gibbs sampling have been found lacking. In this paper we propose the collapsed variational Bayesian in fere ce algorithm for LDA, and show that it is computationally efficient, easy to im plement and significantly more accurate than standard variational Bayesian in fere ce for LDA.
Bayesian Word Sense Induction

Sense induction seeks to automatically identify word senses directly from a corpus. A key assumption underlying previous work is that the context surrounding an ambiguous word is indicative of its meaning. Sense induction is thus typically viewed as an unsupervised clustering problem where the aim is to partition a word’s contexts into different classes, each representing a word sense. Our work places sense induction in a Bayesian context by modeling the contexts of the ambiguous word as samples from a multinomial distribution over senses which are in turn characterized as distributions over words. The Bayesian framework provides a principled way to incorporate a wide range of features beyond lexical cooccurrences and to systematically assess their utility on the sense induction task. The proposed approach yields improvements over state-of-the-art systems on a benchmark dataset.
Noncausal Autoregressive Model in Application to Bitcoin/USD Exchange Rates

This paper introduces a noncausal autoregressive process with Cauchy errors in application to the exchange rates of the Bitcoin electronic currency against the US Dollar. The dynamics of the daily Bitcoin/USD exchange rate series displays episodes of local trends, which can be modelled and interpreted as speculative bubbles. The bubbles may result from the speculative component in the on-line trading. The Bitcoin/USD exchange rates are modelled and predicted.
Optimizing rating scale category effectiveness.

Rating scales are employed as a means of extracting more information out of an item than would be obtained from a mere "yes/no", "right/wrong" or other dichotomy. But does this additional information increase measurement accuracy and precision? Eight guidelines are suggested to aid the analyst in optimizing the manner in which rating scales categories cooperate in order to improve the utility of the resultant measures. Though these guidelines are presented within the context of Rasch analysis, they reflect aspects of rating scale functioning which impact all methods of analysis. The guidelines feature rating-scale-based data such as category frequency, ordering, rating-to-measure inferential coherence, and the quality of the scale from measurement and statistical perspectives. The manner in which the guidelines prompt recategorization or reconceptualization of the rating scale is indicated. Utilization of the guidelines is illustrated through their application to two published data sets.
Pixel bar charts: a visualization technique for very large multi-attribute data sets?

Portions reprinted, with permission from Keim et al.  Simple presentation graphics are intuitive and easy-to-use, but show only highly aggregated data presenting only a very small number of data values (as in the case of bar charts) and may have a high degree of overlap occluding a significant portion of the data values (as in the case of the x-y plots). In this article, the authors therefore propose a generalization of traditional bar charts and x-y plots, which allows the visualization of large amounts of data. The basic idea is to use the pixels within the bars to present detailed information of the data records. The so-called pixel bar charts retain the intuitiveness of traditional bar charts while allowing very large data sets to be visualized in an effective way. It is shown that, for an effective pixel placement, a complex optimization problem has to be solved. The authors then present an algorithm which efficiently solves the problem. The application to a number of real-world ecommerce data sets shows the wide applicability and usefulness of this new idea, and a comparison to other well-known visualization techniques (parallel coordinates and spiral techniques) shows a number of clear advantages. Information Visualization (2002) 1, 20 – 34. DOI: 10.1057/palgrave/ivs/9500003
How Far are We from Solving Pedestrian Detection?

Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the "perfect single frame detector". We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech dataset), and by manually clustering the recurrent errors of a top detector. Our results characterise both localisation and background-versusforeground errors. To address localisation errors we study the impact of training annotation noise on the detector performance, and show that we can improve even with a small portion of sanitised training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech dataset, and provide a new sanitised set of training and test annotations.
Topic evolution and social interactions: how authors effect research

We propose a method for discovering the dependency relationships between the topics of documents shared in social networks using the latent social interactions, attempting to answer the question: given a seemingly new topic, from where does this topic evolve? In particular, we seek to discover the pair-wise probabilistic dependency in topics of documents which associate social actors from a latent social network, where these documents are being shared. By viewing the evolution of topics as a Markov chain, we estimate a Markov transition matrix of topics by leveraging social interactions and topic semantics. Metastable states in a Markov chain are applied to the clustering of topics. Applied to the CiteSeer dataset, a collection of documents in academia, we show the trends of research topics, how research topics are related and which are stable. We also show how certain social actors, authors, impact these topics and propose new ways for evaluating author impact.
Design of Single-Feed Dual-Frequency Patch Antenna for GPS and WLAN Applications

A design of single-feed dual-frequency patch antennas with different polarizations and radiation patterns is proposed. The antenna structure is composed of two stacked patches, in which the top is a square patch and the bottom is a corner-truncated square-ring patch, and the two patches are connected together with four conducting strips. Two operating frequencies can be found in the antenna structure. The radiations at the lower and higher frequencies are a broadside pattern with circular polarization and a conical pattern with linear polarization, respectively. A prototype operating at 1575 and 2400 MHz bands is constructed. Both experimental and simulated results show that the prototype has good performances and is suitable for GPS and WLAN applications.
Face recognition with learning-based descriptor

We present a novel approach to address the representation issue and the matching issue in face recognition (verification). Firstly, our approach encodes the micro-structures of the face by a new learning-based encoding method. Unlike many previous manually designed encoding methods (e.g., LBP or SIFT), we use unsupervised learning techniques to learn an encoder from the training examples, which can automatically achieve very good tradeoff between discriminative power and invariance. Then we apply PCA to get a compact face descriptor. We find that a simple normalization mechanism after PCA can further improve the discriminative ability of the descriptor. The resulting face representation, learning-based (LE) descriptor, is compact, highly discriminative, and easy-to-extract. To handle the large pose variation in real-life scenarios, we propose a pose-adaptive matching method that uses pose-specific classifiers to deal with different pose combinations (e.g., frontal v.s. frontal, frontal v.s. left) of the matching face pair. Our approach is comparable with the state-of-the-art methods on the Labeled Face in Wild (LFW) benchmark (we achieved 84.45% recognition rate), while maintaining excellent compactness, simplicity, and generalization ability across different datasets.
Text mining applied to plagiarism detection: The use of words for detecting deviations in the writing style

0957-4174/$ see front matter 2013  A http://dx.doi.org/10.1016/j.eswa.2012.12.082 ⇑ Corresponding author.  E-mail addresses: goberreu@ing.uchile.cl (G. Ober (J.D. Velásquez). URL: http://wi.dii.uchile.cl/ (J.D. Velásquez). Plagiarism detection is of special interest to educational institutions, and with the proliferation of digital documents on the Web the use of computational systems for such a task has become important. While traditional methods for automatic detection of plagiarism compute the similarity measures on a document-to-document basis, this is not always possible since the potential source documents are not always available. We do text mining, exploring the use of words as a linguistic feature for analyzing a document by modeling the writing style present in it. The main goal is to discover deviations in the style, looking for segments of the document that could have been written by another person. This can be considered as a classification problem using self-based information where paragraphs with significant deviations in style are treated as outliers. This so-called intrinsic plagiarism detection approach does not need comparison against possible sources at all, and our model relies only on the use of words, so it is not language specific. We demonstrate that this feature shows promise in this area, achieving reasonable results compared to benchmark models.
A Constituent-Centric Neural Architecture for Reading Comprehension

Reading comprehension (RC), aiming to understand natural texts and answer questions therein, is a challenging task. In this paper, we study the RC problem on the Stanford Question Answering Dataset (SQuAD). Observing from the training set that most correct answers are centered around constituents in the parse tree, we design a constituent-centric neural architecture where the generation of candidate answers and their representation learning are both based on constituents and guided by the parse tree. Under this architecture, the search space of candidate answers can be greatly reduced without sacrificing the coverage of correct answers and the syntactic, hierarchical and compositional structure among constituents can be well captured, which contributes to better representation learning of the candidate answers. On SQuAD, our method achieves the state of the art performance and the ablation study corroborates the effectiveness of individual modules.
Smart parking solutions for urban areas

Finding a parking place in a busy city centre is often a frustrating task for many drivers; time and fuel are wasted in the quest for a vacant spot and traffic in the area increases due to the slow moving vehicles circling around. In this paper, we present the results of a survey on the needs of drivers from parking infrastructures from a smart services perspective. As smart parking systems are becoming a necessity in today's urban areas, we discuss the latest trends in parking availability monitoring, parking reservation and dynamic pricing schemes. We also examine how these schemes can be integrated forming technologically advanced parking infrastructures whose aim is to benefit both the drivers and the parking operators alike.
ECC Parity: A Technique for Efficient Memory Error Resilience for Multi-Channel Memory Systems

Servers and HPC systems often use a strong memory error correction code, or ECC, to meet their reliability and availability requirements. However, these ECCs often require significant capacity and/or power overheads. We observe that since memory channels are independent from one another, error correction typically needs to be performed for one channel at a time. Based on this observation, we show that instead of always storing in memory the actual ECC correction bits as do existing systems, it is sufficient to store the bitwise parity of the ECC correction bits of different channels for fault-free memory regions, and store the actual ECC correction bits only for faulty memory regions. By trading off the resultant ECC capacity overhead reduction for improved memory energy efficiency, the proposed technique reduces memory energy per instruction by 54.4% and 20.6%, respectively, compared to a commercial chipkill correct ECC and a DIMM-kill correct ECC, while incurring similar or lower capacity overheads.
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms

In this paper we present some theoretical and empirical results on the interacting roles of population size and crossover in genetic algorithms. We summarize recent theoretical results on the disruptive effect of two forms of multi-point crossover: npoint crossover and uniform crossover. We then show empirically that disruption analysis alone is not sufficient for selecting appropriate forms of crossover. However, by taking into account the interacting effects of population size and crossover, a general picture begins to emerge. The implications of these results on implementation issues and performance are discussed, and several directions for further research are suggested.