nils-herrmann/clean_scirepeval huggingface dataset:
Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things

Currently, a large number of industrial data, usually referred to big data, are collected from Internet of Things (IoT). Big data are typically heterogeneous, i.e., each object in big datasets is multimodal, posing a challenging issue on the convolutional neural network (CNN) that is one of the most representative deep learning models. In this paper, a deep convolutional computation model (DCCM) is proposed to learn hierarchical features of big data by using the tensor representation model to extend the CNN from the vector space to the tensor space. To make full use of the local features and topologies contained in the big data, a tensor convolution operation is defined to prevent overfitting and improve the training efficiency. Furthermore, a high-order backpropagation algorithm is proposed to train the parameters of the deep convolutional computational model in the high-order space. Finally, experiments on three datasets, i.e., CUAVE, SNAE2, and STL-10 are carried out to verify the performance of the DCCM. Experimental results show that the deep convolutional computation model can give higher classification accuracy than the deep computation model or the multimodal model for big data in IoT.
Problem solving and collaboration using mobile serious games

This paper presents the results obtained with the implementation of a series of learning activities based on Mobile Serious Games (MSGs) for the development of problem solving and collaborative skills in Chilean 8th grade students. Three MSGs were developed and played by teams of four students in order to solve problems collaboratively. A quasi-experimental design was used. The data shows that the experimental group achieved a higher perception of their own collaboration skills and a higher score in the plan execution dimension of the problem solving cycle than did the non-equivalent control group, revealing that MSG-based learning activities may contribute to such learning improvements. This challenges future research to identify under which conditions learning activities based on mobile serious games can promote the development of higher order skills.
Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use

Problematic smartphone use is an important public health challenge and is linked with poor mental health outcomes. However, little is known about the mechanisms that maintain this behavior. We recruited a sample of 308 participants from Amazon’s Mechanical Turk labor market. Participants responded to standardized measures of problematic smartphone use, and frequency of smartphone use, depression and anxiety and possible mechanisms including behavioral activation, need for touch, fear of missing out (FoMO), and emotion regulation. Problematic smartphone use was most correlated with anxiety, need for touch and FoMO. The frequency of use was most correlated (inversely) with depression. In regression models, problematic smartphone use was associated with FoMO, depression (inversely), anxiety, and need for touch. Frequency of use was associated with need for touch, and (inversely) with depressive symptoms. Behavioral activation mediated associations between smartphone use (both problematic and usage frequency) and depression and anxiety symptoms. Emotional suppression also mediated the association between problematic smartphone use and anxiety. Results demonstrate the importance of social and tactile need fulfillment variables such as FoMO and need for touch as critical mechanisms that can explain problematic smartphone use and its association with depression and
A taxonomy of reasoning flaws in the anti-vaccine movement.

In a scholarly analysis of widely held misconceptions, Gilovich provides a classification scheme of common flaws in reasoning seen in contemporary society. He broadly categorizes these flaws as having cognitive determinants or in having motivational and social determinants. In this survey, the authors examine the various claims against routine childhood and adult vaccines as made by the more public and more organized entities of the anti-vaccine movement as well as those made apparent by surveys of parents and other groups of individuals. The claims illustrate the breadth of reasoning flaws while providing a basis for anticipating and correcting them.
Factors Influencing Emoji Usage in Smartphone Mediated Communications

Emojis have become more and more popular in text-based online communication to express emotions. This indicates a potential to utilize emojis in sentiment analysis and emotion measurements. However, many factors could affect people’s emoji usage and need to be examined. Among them, age, gender, and relationship types may result in different interpretations of the same emoji due to the ambiguity of the iconic expression. In this paper, we aim to explore how these factors may affect the frequency, type, and sentiment of people’s emoji usage in communications. After analyzing 6,821 Wechat chatting messages from 158 participants, we found people between 26–35 had lowest frequency of emoji usage; younger and elder groups showed different sentiment levels for the same emojis; people chose emoji types based on relationships. These findings shed light on how people use emojis as a communication tool.
A Blockchain Based Privacy-Preserving Incentive Mechanism in Crowdsensing Applications

Crowdsensing applications utilize the pervasive smartphone users to collect large-scale sensing data efficiently. The quality of sensing data depends on the participation of highly skilled users. To motivate these skilled users to participate, they should receive enough rewards for compensating their resource consumption. Available incentive mechanisms mainly consider the truthfulness of the mechanism, but mostly ignore the issues of security and privacy caused by a “trustful” center. In this paper, we propose a privacy-preserving blockchain incentive mechanism in crowdsensing applications, in which a cryptocurrency built on blockchains is used as a secure incentive way. High quality contributors will get their payments that are recorded in transaction blocks. The miners will verify the transaction according to the sensing data assessment criteria published by the server. As the transaction information can disclose users’ privacy, a node cooperation verification approach is proposed to achieve  $k$ -anonymity privacy protection. Through theoretical analysis and simulation experiments, we show the feasibility and security of our incentive mechanism.
Discrete Differential-Geometry Operators for Triangulated 2-Manifolds

This paper proposes a unified and consistent set of flexible tools to approximate important geometric attributes, including normal vectors and curvatures on arbitrary triangle meshes. We present a consistent derivation of these first and second order differential properties using averaging Voronoi cells and the mixed Finite-Element/Finite-Volume method, and compare them to existing formulations. Building upon previous work in discrete geometry, these operators are closely related to the continuous case, guaranteeing an appropriate extension from the continuous to the discrete setting: they respect most intrinsic properties of the continuous differential operators. We show that these estimates are optimal in accuracy under mild smoothness conditions, and demonstrate their numerical quality. We also present applications of these operators, such as mesh smoothing, enhancement, and quality checking, and show results of denoising in higher dimensions, such as for tensor images.
Obstacle Detection and Collision Avoidance for a UAV With Complementary Low-Cost Sensors

This paper demonstrates an innovative and simple solution for obstacle detection and collision avoidance of unmanned aerial vehicles (UAVs) optimized for and evaluated with quadrotors. The sensors exploited in this paper are low-cost ultrasonic and infrared range finders, which are much cheaper though noisier than more expensive sensors such as laser scanners. This needs to be taken into consideration for the design, implementation, and parametrization of the signal processing and control algorithm for such a system, which is the topic of this paper. For improved data fusion, inertial and optical flow sensors are used as a distance derivative for reference. As a result, a UAV is capable of distance controlled collision avoidance, which is more complex and powerful than comparable simple solutions. At the same time, the solution remains simple with a low computational burden. Thus, memory and time-consuming simultaneous localization and mapping is not required for collision avoidance.
Single Document Keyphrase Extraction Using Neighborhood Knowledge

Existing methods for single document keyphrase extraction usually make use of only the information contained in the specified document. This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. A specified document is expanded to a small document set by adding a few neighbor documents close to the document, and the graph-based ranking algorithm is then applied on the expanded document set to make use of both the local information in the specified document and the global information in the neighbor documents. Experimental results demonstrate the good effectiveness and robustness of our proposed approach.
Does the benefit of testing depend on lag, and if so, why? Evaluating the elaborative retrieval hypothesis.

Despite the voluminous literatures on testing effects and lag effects, surprisingly few studies have examined whether testing and lag effects interact, and no prior research has directly investigated why this might be the case. To this end, in the present research we evaluated the elaborative retrieval hypothesis (ERH) as a possible explanation for why testing effects depend on lag. Elaborative retrieval involves the activation of cue-related information during the long-term memory search for the target. If the target is successfully retrieved, this additional information is encoded with the cue-target pair to yield a more elaborated memory trace that enhances target access on a later memory test. The ERH states that the degree of elaborative retrieval during practice is greater when testing takes place after a long rather than a short lag (whereas elaborative retrieval during restudy is minimal at either lag). Across two experiments, final-test performance was greater following practice testing than following restudy only, and this memorial advantage was greater with long-lag than with short-lag practice. The final test also included novel cue conditions used to diagnose the degree of elaborative retrieval during practice. The overall pattern of performance in these conditions provided consistent evidence for the ERH, with more extensive elaborative retrieval during long- than during short-lag practice testing.
Efficient Feature Selection Technique for Network Intrusion Detection System Using Discrete Differential Evolution and Decision

Network intrusion is a critical challenge in information and communication systems amongst other forms of fraud perpetrated over the Internet. Despite the various traditional techniques proposed to prevent this intrusion, the threat persists. These days, intrusion detection systems (IDS) are faced with detecting attacks in large streams of connections due to the sporadic increase in network traffics. Although machine learning (ML) has been introduced in IDS to deal with finding patterns in big data, the irrelevant features in the data tend to degrade both the speed and accuracy of detection of attacks. Also, it increases the computational resource needed during training and testing of IDS models. Therefore, in this paper, we seek to find the optimal feature set using discretized differential evolution (DDE) and C4.5 ML algorithm from NSL-KDD standard intrusion dataset. The result obtained shows a significant improvement in detection accuracy, a reduction in training and testing time using the reduced feature set. The method also buttresses the fact that differential evolution (DE) is not limited to optimization of continuous problems but work well for discrete optimization.
Chat More: Deepening and Widening the Chatting Topic via A Deep Model

The past decade has witnessed the boom of human-machine interactions, particularly via dialog systems. In this paper, we study the task of response generation in open-domain multi-turn dialog systems. Many research efforts have been dedicated to building intelligent dialog systems, yet few shed light on deepening or widening the chatting topics in a conversational session, which would attract users to talk more. To this end, this paper presents a novel deep scheme consisting of three channels, namely global, wide, and deep ones. The global channel encodes the complete historical information within the given context, the wide one employs an attention-based recurrent neural network model to predict the keywords that may not appear in the historical context, and the deep one trains a Multi-layer Perceptron model to select some keywords for an in-depth discussion. Thereafter, our scheme integrates the outputs of these three channels to generate desired responses. To justify our model, we conducted extensive experiments to compare our model with several state-of-the-art baselines on two datasets: one is constructed by ourselves and the other is a public benchmark dataset. Experimental results demonstrate that our model yields promising performance by widening or deepening the topics of interest.
Low quality fingerprint image enhancement based on Gabor filter

Fingerprint enhancement proceeds by fully utilizing the intrinsic properties of original images to improve the ridges and eliminate the noises. Although Gabor filter is a classical enhancement method, its function may suffer for the low-quality images due to the unreliable orientation and frequency map estimated by conventional approach. In this paper, a new approach has been proposed to estimate these parameters in an effective way. The local image is modeled as a non-stationary signal and the technique of Short Time Fourier Transform(STFT) is applied to it. Besides, to avoid the multi-step processing and inter-dependency, we extend the theory of probability in mathematics to get the orientation and frequency simultaneously. The performance of our approach is evaluated by the verification system of NIST and FVC2004 DB1_A database. Experimental results show that our method can improve both the image quality and the accuracy of fingerprint recognition.
Toward Meaningful Engagement: A Framework for Design and Research of Gamified Information Systems

Gamification, an emerging idea for using game design elements and principles to make everyday tasks more engaging, is permeating many different types of information systems. Excitement surrounding gamification results from its many potential organizational benefits. However, few research and design guidelines exist regarding gamified information systems. We therefore write this commentary to call upon information systems scholars to investigate the design and use of gamified information systems from a variety of disciplinary perspectives and theories, including behavioral economics, psychology, social psychology, information systems, etc. We first explicate the idea of gamified information systems, provide real-world examples of successful and unsuccessful systems, and, based on a synthesis of the available literature, present a taxonomy of gamification design elements. We then develop a framework for research and design: its main theme is to create meaningful engagement for users; that is, gamified information systems should be designed to address the dual goals of instrumental and experiential outcomes. Using this framework, we develop a set of design principles and research questions, using a running case to illustrate some of our ideas. We conclude with a summary of opportunities for IS researchers to extend our knowledge of gamified information systems, and, at the same time, advance existing theories.
DroidMiner: Automated Mining and Characterization of Fine-grained Malicious Behaviors in Android Applications

DroidMiner is a new malicious Android app detection system that uses static analysis to automatically mine malicious program logic from known Android malware. DroidMiner uses a behavioral graph to abstract malware program logic into a sequence of threat modalities, and then applies machine-learning techniques to identify and label elements of the graph that match harvested threat modalities. Once trained on a mobile malware corpus, DroidMiner can automatically scan a new Android app to (i) determine whether it contains malicious modalities, (ii) diagnose the malware family to which it is most closely associated, and (iii) precisely characterize behaviors found within the analyzed app. While DroidMiner is not the first to attempt automated classification of Android applications based on Framework API calls, it is distinguished by its development of modalities that are resistant to noise insertions and its use of associative rule mining that enables automated association of malicious behaviors with modalities. We evaluate DroidMiner using 2,466 malicious apps, identified from a corpus of over 67,000 third-party market Android apps, plus an additional set of over 10,000 official market Android apps. Using this set of real-world apps, DroidMiner achieves a 95.3% detection rate, with a 0.4% false positive rate. We further evaluate DroidMiner’s ability to classify malicious apps under their proper family labels, and measure its label accuracy at 92%.
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression

Deep neural networks (DNNs) have achieved great success in solving a variety of machine learning (ML) problems, especially in the domain of image recognition. However, recent research showed that DNNs can be highly vulnerable to adversarially generated instances, which look seemingly normal to human observers, but completely confuse DNNs. These adversarial samples are crafted by adding small perturbations to normal, benign images. Such perturbations, while imperceptible to the human eye, are picked up by DNNs and cause them to misclassify the manipulated instances with high confidence. In this work, we explore and demonstrate how systematic JPEG compression can work as an effective pre-processing step in the classification pipeline to counter adversarial attacks and dramatically reduce their effects (e.g., Fast Gradient Sign Method, DeepFool). An important component of JPEG compression is its ability to remove high frequency signal components, inside square blocks of an image. Such an operation is equivalent to selective blurring of the image, helping remove additive perturbations. Further, we propose an ensemble-based technique that can be constructed quickly from a given well-performing DNN, and empirically show how such an ensemble that leverages JPEG compression can protect a model from multiple types of adversarial attacks, without requiring knowledge about the model.
Junior: The Stanford Entry in the Urban Challenge

This article presents the architecture of Junior, a robotic vehicle capable of navigating urban environments autonomously. In doing so, the ve icle is able to select its own routes, perceive and interact with other traffic, and execute various urban driving skills including lane changes, U-turns, parking, a nd merging into moving traffic. The vehicle successfully finished and won second pla ce in the DARPA Urban Challenge, a robot competition organized by the U.S. Gove rnm nt.
Document Level Sentiment Analysis: A survey

Sentiment analysis becomes a very active research area in the text mining field. It aims to extract people's opinions, sentiments, and subjectivity from the texts. Sentiment analysis can be performed at three levels: at document level, at sentence level and at aspect level. An important part of research effort focuses on document level sentiment classification, including works on opinion classification of reviews. This survey paper tackles a comprehensive overview of the last update of sentiment analysis at document level. The main target of this survey is to give nearly full image of sentiment analysis application, challenges and techniques at this level. In addition, some future research issues are also presented.
Predicting the Future with Transformational States

An intelligent observer looks at the world and sees not only what is, but what is moving and what can be moved. In other words, the observer sees how the present state of the world can transform in the future. We propose a model that predicts future images by learning to represent the present state and its transformation given only a sequence of images. To do so, we introduce an architecture with a latent state composed of two components designed to capture (i) the present image state and (ii) the transformation between present and future states, respectively. We couple this latent state with a recurrent neural network (RNN) core that predicts future frames by transforming past states into future states by applying the accumulated state transformation with a learned operator. We describe how this model can be integrated into an encoder-decoder convolutional neural network (CNN) architecture that uses weighted residual connections to integrate representations of the past with representations of the future. Qualitatively, our approach generates image sequences that are stable and capture realistic motion over multiple predicted frames, without requiring adversarial training. Quantitatively, our method achieves prediction results comparable to state-of-the-art results on standard image prediction benchmarks (Moving MNIST, KTH, and UCF101).
Effect of probiotics and synbiotics on blood glucose: a systematic review and meta-analysis of controlled trials.

High fasting blood glucose (FBG) can lead to chronic diseases such as diabetes mellitus, cardiovascular and kidney diseases. Consuming probiotics or synbiotics may improve FBG. A systematic review and meta-analysis of controlled trials was conducted to clarify the effect of probiotic and synbiotic consumption on FBG levels.



PubMed, Scopus, Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature databases were searched for relevant studies based on eligibility criteria. Randomized or non-randomized controlled trials which investigated the efficacy of probiotics or synbiotics on the FBG of adults were included. Studies were excluded if they were review articles and study protocols, or if the supplement dosage was not clearly mentioned.



A total of fourteen studies (eighteen trials) were included in the analysis. Random-effects meta-analyses were conducted for the mean difference in FBG. Overall reduction in FBG observed from consumption of probiotics and synbiotics was borderline statistically significant (-0.18 mmol/L 95 % CI -0.37, 0.00; p = 0.05). Neither probiotic nor synbiotic subgroup analysis revealed a significant reduction in FBG. The result of subgroup analysis for baseline FBG level ≥7 mmol/L showed a reduction in FBG of 0.68 mmol/L (-1.07, -0.29; ρ < 0.01), while trials with multiple species of probiotics showed a more pronounced reduction of 0.31 mmol/L (-0.58, -0.03; ρ = 0.03) compared to single species trials.



This meta-analysis suggests that probiotic and synbiotic supplementation may be beneficial in lowering FBG in adults with high baseline FBG (≥7 mmol/L) and that multispecies probiotics may have more impact on FBG than single species.