nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating the relationship between white matter integrity, cognition, and varieties of video game learning.

Many studies are currently researching the effects of video games, particularly in the domain of cognitive training. Great variability exists among video games however, and few studies have attempted to compare different types of video games. Little is known, for instance, about the cognitive processes or brain structures that underlie learning of different genres of video games.



To examine the cognitive and neural underpinnings of two different types of game learning in order to evaluate their common and separate correlates, with the hopes of informing future intervention research.



Participants (31 younger adults and 31 older adults) completed an extensive cognitive battery and played two different genres of video games, one action game and one strategy game, for 1.5 hours each. DTI scans were acquired for each participant, and regional fractional anisotropy (FA) values were extracted using the JHU atlas.



Behavioral results indicated that better performance on tasks of working memory and perceptual discrimination was related to enhanced learning in both games, even after controlling for age, whereas better performance on a perceptual speed task was uniquely related with enhanced learning of the strategy game. DTI results indicated that white matter FA in the right fornix/stria terminalis was correlated with action game learning, whereas white matter FA in the left cingulum/hippocampus was correlated with strategy game learning, even after controlling for age.



Although cognition, to a large extent, was a common predictor of both types of game learning, regional white matter FA could separately predict action and strategy game learning. Given the neural and cognitive correlates of strategy game learning, strategy games may provide a more beneficial training tool for adults suffering from memory-related disorders or declines in processing speed, particularly older adults.
The Complementary Roles of Traditional and Social Media in Driving Marketing Performance

The media landscape has dramatically changed over t he past decade, with traditional media (e.g., newspapers, television) now supplement ed by social media (e.g., blogs, discussion forums). This new media landscape is not well understood with respect to (i) the joint impacts of traditional and social media on ma rketing performance (e.g., sales), (ii) how these media types influence each other, and (iii) t he mechanisms through which they affect marketing outcomes. These issues are examined with 14 months of daily performance data and media activity for a microfinance website. The authors find that both traditional and social media have strong effects on marketing perfo rmance, though a single unit of social media has a much smaller effect than a single unit of traditional media. However, because social media is created in larger volumes than trad itional media, it has a sizeable effect on performance (i.e., social media is high-volume, low -margin, whereas traditional media is low-volume, high-margin). Further, social media act s as a broker of information flow in an informal network comprising traditional and social media outlets.
E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language

In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data. An added challenge is to generate utterances that contain an accurate representation of the input, while reflecting the fluency and variety of human-generated text. In this paper, we report experiments with NLG models that can be used in task oriented dialogue systems. We explore the use of additional input to the model to encourage diversity and control of outputs. While our submission does not rank highly using automated metrics, qualitative investigation of generated utterances suggests the use of additional information in neural network NLG systems to be a promising research direction.
A Constraint-Based Approach to Constructing Continuous Cartograms

We present a new constraint-based continuous area cartogram construction method that is unique in its ability to preserve essential cues for recognition of region shapes. It automatically achieves desired region areas while maintaining correct map topology. The algorithm is compared with a number of existing methods, and results are shown to be superior in both accuracy and preservation of shape recognition cues. Through hierarchical resolution, we first perform gross adjustments upon a coarsely resampled map and later refine the map at progressively higher levels of detail.
Exploring the relationship between technology acceptance model and usability test

In the past, most studies used the technology acceptance model (TAM) to survey the subjective perception of users in using information technology. The usability test was also used to assess the ease of use of user interfaces. This study introduces a conceptual framework to explore the relationship between user’s beliefs of TAM and usability testing attributes. Usability testing was conducted on an eCampus learning system with a mobile device. TAM data was collected from the participants for analyzing a possible relationship. The findings of this study reveal that TAM results contradict the usability test results in certain areas. The focus of our proposed research model is supported from the causality between perceived ease of use and usability; however, the correlation between perceived usefulness and usability remains unclear.
Bootstrapping for HElib

Gentry’s bootstrapping technique is still the only known method of obtaining fully homomorphic encryption where the system’s parameters do not depend on the complexity of the evaluated functions. Bootstrapping involves a recryption procedure where the scheme’s decryption algorithm is evaluated homomorphically. So far, there have been precious few implementations of recryption, and fewer still that can handle “packed ciphertexts” that encrypt vectors of elements. In the current work, we report on an implementation of recryption of fully-packed ciphertexts using the HElib library for somewhathomomorphic encryption. This implementation required extending the recryption algorithms from the literature, as well as many aspects of the HElib library. Our implementation supports bootstrapping of packed ciphertexts over many extension fields/rings. One example that we tested involves ciphertexts that encrypt vectors of 1024 elements from GF(2). In that setting, the recryption procedure takes under 5.5 minutes (at security-level ≈ 76) on a single core, and allows a depth-9 computation before the next recryption is needed.
Revenue : 229 B Revenue : ? Birth Year : ? Height : ? Weight : 70

Knowledge bases (KB) are often represented as a collection of facts in the form (head, predicate, tail), where head and tail are entities while predicate is a binary relationship that links the two. It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction. However, though frequently ignored, these repositories also contain numerical facts. Numerical facts link entities to numerical values via numerical predicates; e.g., (Paris, latitude, 48.8). Likewise, numerical facts also suffer from the incompleteness problem. To address this issue, we introduce the numerical attribute prediction problem. This problem involves a new type of query where the relationship is a numerical predicate. Consequently, and contrary to link prediction, the answer to this query is a numerical value. We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base. Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes. An extensive set of experiments on benchmark versions of Freebase and Yago show that our approaches largely outperform sensible baselines. We make the datasets available under a permissive BSD-3 license.
Cooperative map building using qualitative reasoning for several AIBO robots

The problem that a robot navigates autonomously through its environment, builds its own map and localizes itself in the map, is still an open problem. It is known as the SLAM (Simultaneous Localization and Map Building) problem. This problem is made even more difficult when we have several robots cooperating to build a common map of an unknown environment, due to the problem of map integration of several submaps built independently by each robot, and with a high degree of error, making the map matching specially difficult. Most of the approaches to solve map building problems are quantitative, resulting in a great computational cost and a low level of abstraction. In order to fulfil these drawbacks qualitative models have been recently used. However, qualitative models are non deterministic. Therefore, the solution recently adopted has been to mix both qualitative and quantitative models to represent the environment and build maps. However, no reasoning process has been used to deal with the information stored in maps up to now, therefore maps are only static storage of landmarks. In this paper we propose a novel method for cooperative map building based on hybrid (qualitative+quantitative) representation which includes also a reasoning process. Distinctive landmarks acquisition for map representation is provided by the cognitive vision and infrared modules which compute differences from the expected data according to the current map and the actual information perceived. We will store in the map the relative orientation information of the landmarks which appear in the environment, after a qualitative reasoning process, therefore the map will be independent of the point of view of the robot. Map integration will then be achieved by localizing each robot in the maps made by the other robots, through a process of pattern matching of the hybrid maps elaborated by each robot, resulting in an integrated map which all robots share, and which is the main objective of this work. This map building method is currently being tested on a team of Sony AIBO four
Techniques and Systems for Image and Video Retrieval

Storage and retrieval of multimedia has become a requirement for many contemporary information systems These systems need to provide browsing querying navigation and sometimes composition capabilities involving various forms of media In this survey we review techniques and systems for image and video retrieval We rst look at visual and non visual features for image retrieval and techniques for using them Temporal aspects of video retrieval are discussed next We review several research and commercial systems including WWW based systems and conclude with future directions
The Bluetooth radio system

A few years ago it was recognized that the vision of a truly low-cost, low-power radio-based cable replacement was feasible. Such a ubiquitous link would provide the basis for portable devices to communicate together in an ad hoc fashion by creating personal area networks which have similar advantages to their office environment counterpart, the local area network. Bluetooth/sup TM/ is an effort by a consortium of companies to design a royalty-free technology specification enabling this vision. This article describes the radio system behind the Bluetooth concept. Designing an ad hoc radio system for worldwide usage poses several challenges. The article describes the critical system characteristics and motivates the design choices that have been made.
A 0.002-mm$^{2}$  6.4-mW 10-Gb/s Full-Rate Direct DFE Receiver With 59.6% Horizontal Eye Opening Under 23.3-dB Channel Loss at Nyquist Frequency

This paper reports a full-rate direct decision-feedback-equalization (DFE) receiver with circuit techniques to widen the data eye opening with competitive power and area efficiencies. Specifically, a current-reuse active-inductor (AI) linear equalizer is merged into a clocked-one-tap DFE core for joint-elimination of pre-cursor and long-tail post-cursors. Unlike the passive-inductor designs that are bulky and untunable, the AI linear equalizer offers orthogonally tunable low- and high-frequency de-emphasis. The clocked-one-tap DFE resolves the first post-cursor via return-to-zero feedback data patterns for sharper data transition (i.e., horizontal eye opening), and is followed by a D-flip-flop slicer to maximize the data height (i.e., vertical eye opening). A 10-Gb/s DFE receiver was fabricated in 65-nm CMOS. Measured over an 84-cm printed circuit board differential trace with 23.3-dB channel loss at Nyquist frequency (5 GHz), the achieved figure-of-merit is 0.027 pJ/bit/dB (power consumption/date rate/channel loss). At 10-12 bit error rate under 27-1 pseudorandom binary sequence, the horizontal and vertical eye opening are 59.6% and 189.3 mV, respectively. The die size is 0.002 mm2.
Machine Learning With Big Data: Challenges and Approaches

The Big Data revolution promises to transform how we live, work, and think by enabling process optimization, empowering insight discovery and improving decision making. The realization of this grand potential relies on the ability to extract value from such massive data through data analytics; machine learning is at its core because of its ability to learn from data and provide data driven insights, decisions, and predictions. However, traditional machine learning approaches were developed in a different era, and thus are based upon multiple assumptions, such as the data set fitting entirely into memory, what unfortunately no longer holds true in this new context. These broken assumptions, together with the Big Data characteristics, are creating obstacles for the traditional techniques. Consequently, this paper compiles, summarizes, and organizes machine learning challenges with Big Data. In contrast to other research that discusses challenges, this work highlights the cause–effect relationship by organizing challenges according to Big Data Vs or dimensions that instigated the issue: volume, velocity, variety, or veracity. Moreover, emerging machine learning approaches and techniques are discussed in terms of how they are capable of handling the various challenges with the ultimate objective of helping practitioners select appropriate solutions for their use cases. Finally, a matrix relating the challenges and approaches is presented. Through this process, this paper provides a perspective on the domain, identifies research gaps and opportunities, and provides a strong foundation and encouragement for further research in the field of machine learning with Big Data.
Learning from Demonstrations for Real World Reinforcement Learning

Deep reinforcement learning (RL) has achieved several high profile successes in difficult control problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages this data to massively accelerate the learning process even from relatively small amounts of demonstration data. DQfD works by combining temporal difference updates with large-margin classification of the demonstrator’s actions. We show that DQfD has better initial performance than Deep Q-Networks (DQN) on 40 of 42 Atari games and it receives more average rewards than DQN on 27 of 42 Atari games. We also demonstrate that DQfD learns faster than DQN even when given poor demonstration data.
Design of Broadband Constant-Beamwidth Conical Corrugated-Horn Antennas [Antenna Designer's Notebook]

In this paper, a new design procedure is proposed for the design of wideband constant-beamwidth conical corrugated-horn antennas, with minimum design and construction complexity. The inputs to the procedure are the operating frequency band, the required minimum beamwidth in the entire frequency band, and the frequency in which the maximum gain is desired to occur. Based on these values, the procedure gives a relatively good design with a relative bandwidth of up to 2.5:1. Based on the proposed procedure, a corrugated-horn antenna with a constant beamwidth over the frequencies of 8 to 18 GHz was designed and simulated using commercial software. The designed antenna was also constructed, and its electromagnetic performance was measured. The measured results of the constructed prototype antenna confirmed the simulation results and satisfied the design requirements, validating the proposed design procedure.
Identifying user habits through data mining on call data records

In this paper we propose a framework for identifying patterns and regularities in the pseudoanonymized Call Data Records (CDR) pertaining a generic subscriber of a mobile operator. We face the challenging task of automatically deriving meaningful information from the available data, by using an unsupervised procedure of cluster analysis and without including in the model any apriori knowledge on the applicative context. Clusters mining results are employed for understanding users’ habits and to draw their characterizing profiles. We propose two implementations of the data mining procedure; the first is based on a novel system for clusters and knowledge discovery called LD-ABCD, capable of retrieving clusters and, at the same time, to automatically discover for each returned cluster the most appropriate dissimilarity measure (local metric). The second approach instead is based on PROCLUS, the well-know subclustering algorithm. The dataset under analysis contains records characterized only by few features and, consequently, we show how to generate additional fields which describe implicit information hidden in data. Finally, we propose an effective graphical representation of the results of the data-mining procedure, which can be easily understood and employed by analysts for practical applications.
A Wideband Differential-Fed Slot Antenna Using Integrated Compact Balun With Matching Capability

This communication proposes a wideband differential-fed slot antenna that works in its dominant mode ( λ/2 mode). The antenna has a near omnidirectional radiation pattern with vertical polarization, which is designed for near-ground wireless sensor node applications. A compact integrated T-slot balun and a loop to T-slot feeding structure are introduced to realize the differential feeding; the bandwidth is expanded to 30.7% (2.2 GHz-3.0 GHz) with a 3.8 dB peak gain. A detailed matching method without using lumped elements is proposed, which can be applied to various types of RFIC chips. A prototype is built and measured. Its differential impedance, antenna patterns, and gain are consistent with the simulation results.
Characterization of antimicrobial activity in Kombucha fermentation

Fermented tea drink, Kombucha, can inhibit the growth of Shigella sonnei, Escherichia coli, Salmonella enteritidis and Salmonella typhimurium. Several metabolites were analyzed every two days during a 14-day Kombucha fermentation. Levels of acetic acid and gluconic acid were found to increase with fermentation time. No lactic acid or ethanol was detected. Systematic investigation of the antimicrobial activity in Kombucha revealed the presence of antimicrobial compounds other than organic acids or proteins (enzymes) produced during fermentation or the tannins originally present in the tea broth.
MDM tool: A data mining framework integrated into Moodle

The interest in developing Learning Analytics tools that can be integrated into the well-known Moodle course management systems is increasing nowadays. These tools generally provide some type of basic analytics and graphs about users’ interaction in the course. However, they do not enable a varied set of DataMining techniques to be applied, such as approaches for classification, clustering, or association. To address this issue, a new and freely available Moodle Data Mining tool, named MDM, has been proposed in this paper. The proposed tool eases the whole knowledge discovery process, including tasks such as selection, data pre-processing, and data mining from Moodle courses. The proposed MDM tool has been developed in PHP programming language, so it can be easily integrated into Moodle as a module for a specific course. Its main features and architecture are described in depth, and a tutorial is also provided as a practical way of using the MDM interface. Finally, some experimental results using a real-life sample dataset of mechanical engineering students are analyzed. 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:90–102, 2017; View this article online at wileyonlinelibrary.com/ journal/cae; DOI 10.1002/cae.21782
Breathing at a rate of 5.5 breaths per minute with equal inhalation-to-exhalation ratio increases heart rate variability.

Prior studies have found that a breathing pattern of 6 or 5.5 breaths per minute (bpm) was associated with greater heart rate variability (HRV) than that of spontaneous breathing rate. However, the effects of combining the breathing rate with the inhalation-to-exhalation ratio (I:E ratio) on HRV indices are inconsistent. This study aimed to examine the differences in HRV indices and subjective feelings of anxiety and relaxation among four different breathing patterns.



Forty-seven healthy college students were recruited for the study, and a Latin square experimental design with a counterbalance in random sequences was applied. Participants were instructed to breathe at two different breathing rates (6 and 5.5 breaths) and two different I:E ratios (5:5 and 4:6). The HRV indices as well as anxiety and relaxation levels were measured at baseline (spontaneous breathing) and for the four different breathing patterns.



The results revealed that a pattern of 5.5 bpm with an I:E ratio of 5:5 produced a higher NN interval standard deviation and higher low frequency power than the other breathing patterns. Moreover, the four different breathing patterns were associated with significantly increased feeling of relaxation compared with baseline.



The study confirmed that a breathing pattern of 5.5 bpm with an I:E ratio of 5:5 achieved greater HRV than the other breathing patterns. This finding can be applied to HRV biofeedback or breathing training in the future.
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks

We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result using a single model. First, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network's hidden state. Second, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. Finally, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to multiple metrics. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well as recently published methods based on deep neural networks.