nils-herrmann/clean_scirepeval huggingface dataset:
Big data analytics: The stakes for students, scientists & managers - a management perspective

For a developing nation, deploying big data (BD) technology and introducing data science in higher education is a challenge. A pessimistic scenario is: Mis-use of data in many possible ways, waste of trained manpower, poor BD certifications from institutes, under-utilization of resources, disgruntled management staff, unhealthy competition in the market, poor integration with existing technical infrastructures. Also, the questions in the minds of students, scientists, engineers, teachers and managers deserve wider attention. Besides the stated perceptions and analyses perhaps ignoring socio-political and scientific temperaments in developing nations, the following questions arise: How did the BD phenomenon naturally occur, post technological developments in Computer and Communications Technology and how did different experts react to it? Are academicians elsewhere agreeing on the fact that BD is a new science? Granted that big data science is a new science what are its foundations as compared to conventional topics in Physics, Chemistry or Biology? Or, is it similar in an esoteric sense to astronomy or nuclear science? What are the technological and engineering implications locally and globally and how these can be advantageously used to augment business intelligence, for example? In other words, will the industry adopt the changes due to tactical advantages? How can BD success stories be faithfully carried over elsewhere? How will BD affect the Computer Science and other curricula? How will BD benefit different segments of our society on a large scale? To answer these, an appreciation of the BD as a science and as a technology is necessary. This paper presents a quick BD overview, relying on the contemporary literature; it addresses: characterizations of BD and the BD people, the background required for the students and teachers to join the BD bandwagon, the management challenges in embracing BD so that the bottomline is clear.
Uncertain Data Mining: An Example in Clustering Location Data

Data uncertainty is an inherent property in various applications due to reasons such as outdated sources or imprecise measurement. When data mining techniques are applied to these data, their uncertainty has to be considered to obtain high quality results. We present UK-means clustering, an algorithm that enhances the K-means algorithm to handle data uncertainty. We apply UKmeans to the particular pattern of moving-object uncertainty. Experimental results show that by considering uncertainty, a clustering algorithm can produce more accurate results.
Collaborative filtering recommender systems

Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decisionmaking processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce .They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.
Dragonfly: strengthening programming skills by building a game engine from scratch

Computer game development has been shown to be an effective hook for motivating students to learn both introductory and advanced computer science topics. While games can be made from scratch, to simplify the programming required game development often uses game engines that handle complicated or frequently used components of the game. These game engines present the opportunity to strengthen programming skills and expose students to a range of fundamental computer science topics. While educational efforts have been effective in using game engines to improve computer science education, there have been no published papers describing and evaluating students building a game engine from scratch as part of their course work. This paper presents the Dragonfly-approach in which students build a fully functional game engine from scratch and make a game using their engine as part of a junior-level course. Details on the programming projects are presented, as well as an evaluation of the results from two offerings that used Dragonfly. Student performance on the projects as well as student assessments demonstrate the efficacy of having students build a game engine from scratch in strengthening their programming skills.
Mid-Curve Recommendation System: a Stacking Approach Through Neural Networks

Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily-basis; a concrete case is the so-called Mid-Curve Calendar Spread (MCCS). The actual procedure in place is full of pitfalls and a more systematic approach where more information at hand is crossed and aggregated to find good trading picks can be highly useful and undoubtedly increase the trader’s productivity. Therefore, in this work we propose an MCCS Recommendation System based on a stacking approach through Neural Networks. In order to suggest that such approach is methodologically and computationally feasible, we used a list of 15 different types of US Dollar MCCSs regarding expiration, forward and swap tenure. For each MCCS, we used 10 years of historical data ranging weekly from Sep/06 to Sep/16. Then, we started the modelling stage by: (i) fitting the base learners using as the input sensitivity metrics linked with the MCCS at time t, and its subsequent annualized returns as the output; (ii) feeding the prediction from each base model to a particular stacker; and (iii) making predictions and comparing different modelling methodologies by a set of performance metrics and benchmarks. After establishing a backtesting engine and setting performance metrics, our results suggest that our proposed Neural Network stacker compared favourably to other combination procedures.
Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks

Providing a plausible explanation for the relationship between two related entities is an important task in some applications of knowledge graphs, such as in search engines. However, most existing methods require a large number of manually labeled training data, which cannot be applied in large-scale knowledge graphs due to the expensive data annotation. In addition, these methods typically rely on costly handcrafted features. In this paper, we propose an effective pairwise ranking model by leveraging clickthrough data of a Web search engine to address these two problems. We first construct large-scale training data by leveraging the query-title pairs derived from clickthrough data of a Web search engine. Then, we build a pairwise ranking model which employs a convolutional neural network to automatically learn relevant features. The proposed model can be easily trained with backpropagation to perform the ranking task. The experiments show that our method significantly outperforms several strong baselines.
Encoding Syntactic Knowledge in Neural Networks for Sentiment Classification

Phrase/Sentence representation is one of the most important problems in natural language processing. Many neural network models such as Convolutional Neural Network (CNN), Recursive Neural Network (RNN), and Long Short-Term Memory (LSTM) have been proposed to learn representations of phrase/sentence, however, rich syntactic knowledge has not been fully explored when composing a longer text from its shorter constituent words. In most traditional models, only word embeddings are utilized to compose phrase/sentence representations, while the syntactic information of words is yet to be explored. In this article, we discover that encoding syntactic knowledge (part-of-speech tag) in neural networks can enhance sentence/phrase representation. Specifically, we propose to learn tag-specific composition functions and tag embeddings in recursive neural networks, and propose to utilize POS tags to control the gates of tree-structured LSTM networks. We evaluate these models on two benchmark datasets for sentiment classification, and demonstrate that improvements can be obtained with such syntactic knowledge encoded.
Fanning the Flames of Hate : Social Media and Hate Crime *

This paper investigates the link between social media and hate crime using handcollected data from Facebook. We study the case of Germany, where the recently emerged right-wing party Alternative für Deutschland (AfD) has developed a major social media presence. Using a Bartik-type empirical strategy, we show that right-wing anti-refugee sentiment on Facebook predicts violent crimes against refugees in otherwise similar municipalities with higher social media usage. To establish causality, we further exploit exogenous variation in major internet and Facebook outages, which fully undo the correlation between social media and hate crime. We further find that the effect decreases with distracting news events; increases with user network interactions; and does not hold for posts unrelated to refugees. Our results suggest that social media can act as a propagation mechanism between online hate speech and real-life incidents.
Deep Residual Learning for Weakly-Supervised Relation Extraction

Deep residual learning (ResNet) (He et al., 2016) is a new method for training very deep neural networks using identity mapping for shortcut connections. ResNet has won the ImageNet ILSVRC 2015 classification task, and achieved state-of-theart performances in many computer vision tasks. However, the effect of residual learning on noisy natural language processing tasks is still not well understood. In this paper, we design a novel convolutional neural network (CNN) with residual learning, and investigate its impacts on the task of distantly supervised noisy relation extraction. In contradictory to popular beliefs that ResNet only works well for very deep networks, we found that even with 9 layers of CNNs, using identity mapping could significantly improve the performance for distantly-supervised relation extraction.
CapsGAN: Using Dynamic Routing for Generative Adversarial Networks

In this paper, we propose a novel technique for generating images in the 3D domain from images with high degree of geometrical transformations. By coalescing two popular concurrent methods that have seen rapid ascension to the machine learning zeitgeist in recent years: GANs (Goodfellow et. al.) and Capsule networks (Sabour, Hinton et. al.)  CapsGAN. We show that CapsGAN performs better than or equal to traditional CNN based GANs in generating images with high geometric transformations using rotated MNIST. In the process, we also show the efficacy of using capsules architecture in the GANs domain. Furthermore, we tackle the Gordian Knot in training GANs the performance control and training stability by experimenting with using Wasserstein distance (gradient clipping, penalty) and Spectral Normalization. The experimental findings of this paper should propel the application of capsules and GANs in the still exciting and nascent domain of 3D image generation, and plausibly video (frame) generation.
Axial coding and the grounded theory controversy.

The purpose of this article is to describe the similarities and differences between two approaches to grounded theory research: grounded theory as espoused by Glaser and grounded theory as espoused by Strauss and Corbin. The focus of the article is the controversy surrounding the use of axial coding. The author proposes a resolution to the controversy by suggesting that one does not need to view either approach as right or wrong; rather, the qualitative and grounded theory researcher can choose an approach, and that choice is based on the goal of the researcher's study. Examples of both approaches, from the author's research study on the experiences of living in a family with a child with attention deficit hyperactivity disorder (ADHD), are provided.
Truth and deception at the rhetorical structure level

This paper furthers the development of methods to distinguish truth from deception in textual data. We use rhetorical structure theory (RST) as the analytic framework to identify systematic differences between deceptive and truthful stories in terms of their coherence and structure. A sample of 36 elicited personal stories, self-ranked as truthful or deceptive, is manually analyzed by assigning RST discourse relations among each story’s constituent parts. A vector space model (VSM) assesses each story’s position in multidimensional RST space with respect to its distance from truthful and deceptive centers as measures of the story’s level of deception and truthfulness. Ten human judges evaluate independently whether each story is deceptive and assign their confidence levels (360 evaluations total), producing measures of the expected human ability to recognize deception. As a robustness check, a test sample of 18 truthful stories (with 180 additional evaluations) is used to determine the reliability of our RST-VSM method in determining deception. The contribution is in demonstration of the discourse structure analysis as a significant method for automated deception detection and an effective complement to lexicosemantic analysis. The potential is in developing novel discourse-based tools to alert information users to potential deception in computermediated texts.
Introduction to artificial neural networks.

The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of 'intelligent' agents, such as artificial neural networks (ANNs), able to adapt themselves dynamically to problems of high complexity. ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on individual basis and not as average trends. These tools can offer specific advantages with respect to classical statistical techniques. This article is designed to acquaint gastroenterologists with concepts and paradigms related to ANNs. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data and from complex, dynamic, and multidimensional phenomena, which are often poorly predictable in the traditional 'cause and effect' philosophy.
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Training Convolutional Networks with Noisy Labels

The availability of large labeled datasets has allowed Convolutional Network models to achieve impressive recognition results. However, in many settings manual annotation of the data is impractical; instead our data has noisy labels, i.e. there is some freely available label for each image which may or may not be accurate. In this paper, we explore the performance of discriminatively-trained Convnets when trained on such noisy data. We introduce an extra noise layer into the network which adapts the network outputs to match the noisy label distribution. The parameters of this noise layer can be estimated as part of the training process and involve simple modifications to current training infrastructures for deep networks. We demonstrate the approaches on several datasets, including large scale experiments on the ImageNet classification benchmark.
Optimization of Precast Prestressed Concrete Bridge Girder Systems

60 A practical approach to the optimal design of precast, prestressed concrete highway bridge girder systems is presented. The approach aims at standardizing the optimal design of bridge systems, as opposed to standardizing girder sections. Structural system optimization is shown to be more relevant than conventional girder optimization for an arbitrarily chosen structural system. Bridge system optimization is defined as the optimization of both longitudinal and transverse bridge configurations (number of spans, number of girders, girder type, reinforcements and tendon layout). As a result, the preliminary design process is much simplified by using some developed design charts from which selection of the optimum bridge system, number and type of girders, and amounts of prestressed and non-prestressed reinforcements are easily obtained for a given bridge length, width and loading type.
A Taxonomy of Cyber Attacks on SCADA Systems

systems are deeply ingrained in the fabric of critical infrastructure sectors. These computerized real-time process control systems, over geographically dispersed continuous distribution operations, are increasingly subject to serious damage and disruption by cyber means due to their standardization and connectivity to other networks. However, SCADA systems generally have little protection from the escalating cyber threats. In order to understand the potential danger and to protect SCADA systems, in this paper, we highlight their difference from standard IT systems and present a set of security property goals. Furthermore, we focus on systematically identifying and classifying likely cyber attacks including cyber-induced cyber-physical attack son SCADA systems. Determined by the impact on control performance of SCADA systems, the attack categorization criteria highlights commonalities and important features of such attacks that define unique challenges posed to securing SCADA systems versus traditional Information Technology(IT) systems.
Traffic Accidents Classification and Injury Severity Prediction

Traffic accidents constitutes the first cause of death and injury in many developed countries. However, traffic accidents information and data provided by public organisms can be exploited to classify these accidents according to their type and severity, and consequently try to build predictive model. Detecting and identifying injury severity in traffic accidents in real time is primordial for speeding post-accidents protocols as well as developing general road safety policies. This article presents a case study of traffic accidents classification and severity prediction in Spain. Raw data are from Spanish traffic agency covering a period of six years ranging from 2011 to 2015. To this end, are compared three different machine learning classification techniques, such as Gradient Boosting Trees, Deep Learning and Naïve Bayes.
Relative total body fat and skinfold patterning in filipino national combat sport athletes.

The purpose of this study was to assess relative total body fat and skinfold patterning in Filipino national karate and pencak silat athletes. Participants were members of the Philippine men's and women's national teams in karate (12 males, 5 females) and pencak silat (17 males and 5 females). In addition to age, the following anthropometric measurements were taken: height, body mass, triceps, subscapular, supraspinale, umbilical, anterior thigh and medial calf skinfolds. Relative total body fat was expressed as sum of six skinfolds. Sum of skinfolds and each individual skinfold were also expressed relative to Phantom height. A two-way (Sport*Gender) ANOVA was used to determine the differences between men and women in total body fat and skinfold patterning. A Bonferroni-adjusted alpha was employed for all analyses. The women had a higher proportional sum of skinfols (80.19 ± 25.31 mm vs. 51.77 ± 21.13 mm, p = 0. 001, eta(2) = 0.275). The men had a lower proportional triceps skinfolds (-1.72 ± 0.71 versus - 0.35 ± 0.75, p < 0.001). Collapsed over gender, the karate athletes (-2.18 ± 0.66) had a lower proportional anterior thigh skinfold than their pencak silat colleagues (-1.71 ± 0.74, p = 0.001). Differences in competition requirements between sports may account for some of the disparity in anthropometric measurements.  purpose of the present investigation was to assess relative total body fat and skinfold patterning in Filipino national karate and pencak silat athletes.The results seem to suggest that there was no difference between combat sports in fatness.Skinfold patterning was more in line with what was reported in the literature with the males recording lower extremity fat.
Motion Feature Network: Fixed Motion Filter for Action Recognition

Spatio-temporal representations in frame sequences play an important role in the task of action recognition. Previously, a method of using optical flow as a temporal information in combination with a set of RGB images that contain spatial information has shown great performance enhancement in the action recognition tasks. However, it has an expensive computational cost and requires two-stream (RGB and optical flow) framework. In this paper, we propose MFNet (Motion Feature Network) containing motion blocks which make it possible to encode spatiotemporal information between adjacent frames in a unified network that can be trained end-to-end. The motion block can be attached to any existing CNN-based action recognition frameworks with only a small additional cost. We evaluated our network on two of the action recognition datasets (Jester and Something-Something) and achieved competitive performances for both datasets by training the networks from scratch.
A Practical Wireless Attack on the Connected Car and Security Protocol for In-Vehicle CAN

Vehicle-IT convergence technology is a rapidly rising paradigm of modern vehicles, in which an electronic control unit (ECU) is used to control the vehicle electrical systems, and the controller area network (CAN), an in-vehicle network, is commonly used to construct an efficient network of ECUs. Unfortunately, security issues have not been treated properly in CAN, although CAN control messages could be life-critical. With the appearance of the connected car environment, in-vehicle networks (e.g., CAN) are now connected to external networks (e.g., 3G/4G mobile networks), enabling an adversary to perform a long-range wireless attack using CAN vulnerabilities. In this paper we show that a long-range wireless attack is physically possible using a real vehicle and malicious smartphone application in a connected car environment. We also propose a security protocol for CAN as a countermeasure designed in accordance with current CAN specifications. We evaluate the feasibility of the proposed security protocol using CANoe software and a DSP-F28335 microcontroller. Our results show that the proposed security protocol is more efficient than existing security protocols with respect to authentication delay and communication load.