Reviewerly/exHarmony huggingface dataset:
Identifying Experts in Community Question Answering Website Based on Graph Convolutional Neural Network

High-quality answers, usually given by experts, play an important role in community question answering (CQA) websites. Therefore, experts in these websites are defined as those who provide high-quality answers. We propose two semi-supervised learning models based on the graph convolution neural network (GCN) to identify them. Both models comprehensively extract features from the social behavior network, user profiles, and question and answer text. Specifically, we construct a social behavior network according to the co-answering relationship among answerers, which means every two answerers are connected if they answer the same questions. The difference between these two models is the methods of extracting text features. One model named GCN-Doc uses Doc2vec to get text vectors before training. The other model named GCN-Lstm with a long short term memory (LSTM) network extracts text features while training. Experiments using real-world data from Zhihu.com, one of the largest Chinese CQA websites, show that both GCN-Doc and GCN-Lstm can identify experts effectively comparing with baselines of PageRank and other GCN based neural network models. Besides, GCN-Lstm performs better than GCN-Doc.
The Political Debate on Immigration in the Election Campaigns in Europe

Migration has become an increasingly pressing topic on the national and European political agendas and in general public debate. The migratory phenomenon, as well as its humanitarian and health implications, are presented nowadays as a challenge for national and supranational governments which requires coordinated responses to ensure citizen security. During the election campaigns in the last three years, right-wing parties have largely depicted the right of freedom of movement as a risk factor, taking advantage of this issue for political propaganda. A significant part of the political debate takes place on social media, which has become the preferred platform for openly expressing political sentiment, including that considered politically incorrect. This study explores the political debate on immigration during the election campaigns of France and Italy over the last three years. More specifically, we perform Emotional Text Mining with the aim of identifying the sentiment surrounding immigration, and how immigrants are portrayed, in the online Twitter debate during the French presidential election (2017), the Italian general election (2018), and the Italian European elections (2019). Results were compared to identify the similarities and differences, and the effect on the election results that characterized two of the European Union's founding countries.
Fusion with distance-aware selection strategy for dandelion algorithm

The Dandelion Algorithm (DA) is a recently proposed swarm intelligent optimization algorithm which is inspired by the process of dandelion sowing. In DA, the selection strategy (SS) is of key importance which is responsible for choosing the dandelions to enter the next generation. However, current SS in DA only considers the function fitness value, which cannot maintain the diversity of the population and can easily fall into local optimal solutions prematurely. To address this problem, a distance-aware selection strategy (DSS) is proposed by jointly considering the function fitness value and the individual position distance. In addition, inspired by ensemble learning, a fusion based adaptive selection strategy (FSS) is proposed to further improve the performance of DA. Specifically, a fusion pool including several famous SSs and our proposed DSS is established. And in each iteration, the best SS in terms of overall reward is selected. Experimental results show that the proposed algorithms are powerful in terms of accuracy in the 28 standard functions from the CEC2013. For DSS, it outperforms several famous SSs in DA. For FSS, it is superior or competitive to 11 participating algorithms benchmarked on CEC2013 and five recently proposed algorithms improved by new selection strategy. Since FSS is a universal framework, it can be applied to other swarm intelligence algorithms with selection strategy.
Speech Recognition of Command Words Based on Convolutional Neural Network

In the current development of artificial intelligence, speech still lags behind the development of natural language processing and image, but there is still a considerable demand for speech recognition in the industry. Therefore, speech recognition based on convolutional neural network is designed in this paper, which can be applied in industry. Experimental results show that compared with the traditional deep neural network training method, the convolutional neural network training method can significantly improve the accuracy of command word speech recognition.
Compensation of Measurement Errors for a Magnetoresistive Angular Sensor Array Using Artificial Neuronal Networks

Sensing setups based on the magnetic field possess many benefits that make such sensors an important class in many application fields. Especially in industrial or automotive applications, such sensing concepts are very important parts in many essential system components. Magnetic field sensors are typically used to measure angular positions and rotational velocity as well as linear positions. Due to their high robustness and accuracy, MR-based sensors have a dominant role in the angular sensing domain. Nevertheless, novel vehicle concepts such as electrical driven or hybrid cars as well as increasing quality and reliability requirements demand improvements. However, those sensors face challenges due to new technologies, which are accompanied by new regulations. From this follows that manufactures can either adapt existing technologies or develop new sensing concepts. The goal of the following work is the evaluation of the capability of neuronal networks to enhance the measurement results of magnetic field sensors if these are disturbed by magnetic stray fields. Therefore, the system setup and possible error sources are analyzed before different neural networks are designed, tried and tested, whereby special attention is given to the network configuration in order to enable a deployment close to the individual sensors. Finally, the results are verified with an adapted measurement setup.
FaceLab: Scalable Facial Performance Capture for Visual Effects

In visual effects it is usually required to process multiple film plates in order to reproduce the performance of real actors using a digital 3D character. One of the most challenging aspects of this process is the complexity of facial animation.
Bilinear Convolutional Auto-encoder based Pilot-free End-to-end Communication Systems

Recently, deep learning based end-to-end communication systems have been developed, where both the transmitter and the receiver are represented as deep neural networks (DNN) and an end-to-end loss is optimized directly. In this paper, we address the effects of the more general wireless channels to the end-to-end framework. We formulate this problem as training a deep auto-encoder system with an adversarial convolutional layer and propose a training procedure with mini-batches of input samples and channels. Instead of using pilots to explicitly estimate the unknown channel, the auto-encoder learns to address the channel effects without any pilot information. In particular, the receiver contains two modules, designed for channel information extraction and data recovery, respectively. The features obtained from the channel information extraction module are combined with received signals by a bilinear production and then processed by the data recovery module to reconstruct the original input data. The experimental results show a performance improvement compared with the traditional methods in commonly seen wireless channels, including frequency-selective channels and multi-input multi-output (MIMO) channels.
Blockchain Based Decentralized Trust Management framework

The blockchain is a storage technology and transmission of information, transparent, secure, and operating without central control. In this paper, we propose a new decentralized trust management and cooperation model where data is shared via blockchain and we explore the revenue distribution under different consensus schemes. To reduce the power calculation with respect to the control mechanism, our proposal adopts the possibility of Proof on Trust (PoT) and Proof of proof-of-stake based trust to replace the proof of work (PoW) scheme, to carry out the mining and storage of new data blocks. To detect nodes with malicious behavior to provide false system information, the trust updating algorithm is proposed..
Application of Cognitive Computing for Smart Crowd Management

A crowd can be defined as a large gathering of people at a particular place showing different types of attitudes and behaviors. Monitoring and tracking these wide varieties of people is tedious in a real environment. The crowded scenarios have a high tendency to change into an abnormal condition due to sudden external pressures such as gunshots/fire or internal stress such as overcrowding, where things get often uncontrollable and the consequences are disastrous. Moreover, the use of a large number of monitoring cameras and the limited capability of human operators to analyze the video contents result in an urge to developing smart crowd monitoring systems with humanlike capabilities. This article discusses the human cognition capability and its application for smart crowd management. Cognitive computing facilitates complex task automation, real-time decision-making, predictive analytics, and processing of voluminous structured and unstructured data.
An empirical study of the effect of acoustic-prosodic entrainment on the perceived trustworthiness of conversational avatars

Entrainment is the tendency of interlocutors to become more similar to each other in their way of speaking. This phenomenon has been repeatedly documented and is associated with multiple social aspects of human-human conversations. However, there is a dearth of research on the effects of spoken dialogue systems (SDSs) with implemented acoustic-prosodic (dis)entrainment policies. The goal of the present work is to provide further empirical evidence on how acoustic-prosodic (dis)entraining policies affect users. In particular, this article focuses on its effects on users’ trust toward the SDSs. In the experiments reported here we analyze if and how different acoustic-prosodic (dis)entrainment policies affect users’ perception of a system’s ability. We collected data from 98 unique participants, all native speakers of Argentine Spanish. Our results suggest that acoustic-prosodic (dis)entrainment in spoken dialogue systems is effectively associated with the way users perceive the capabilities of such systems. Characterizing these effects remains a challenging task. Overall, we observe a positive effect on trust of entrainment on intensity and a negative effect of entrainment on pitch. Estimated effect sizes are far from negligible.
Category similarity affects study choices in self-regulated learning

During learning, interleaving exemplars from different categories (e.g., ABCBCACAB) rather than blocking by category (e.g., AAABBBCCC) often enhances inductive learning, especially when the categories are highly similar. However, when allowed to select their own study schedules, learners overwhelmingly tend to block rather than interleave. Category similarity has been shown to moderate the relative benefit of interleaved versus blocked study. We investigated whether learners were sensitive to category similarity when choosing exemplars for study, and whether these choices predicted their learning outcomes. In Experiment 1, learners interleaved more often when the categories were highly similar (difficult to discriminate from each other), compared with when similarity was low. In Experiment 2, learners were presented with two sets of categories to learn; categories within each set were similar to each other, but categories were dissimilar across sets. When learners chose to interleave, they tended to switch to a similar rather than dissimilar category. Importantly, learners’ study choices predicted their subsequent categorization performance. Our findings suggest that learners are strategic in their search for commonalities within versus differences among categories and can regulate their study behaviors based on category similarity.
An Online Recommendation System Using Deep Learning for Textile Products

Recommendation systems are frequently preferred in recent years ensuring customer satisfaction and accelerating sales. Thanks to these systems, it is aimed to accelerate the decision-making process of customers. Recommendation systems have become a necessary part, especially in online shopping. Most of the recommendation systems used in many different areas have been attracting attention, focusing on fashion, and clothing recently. In this paper, a deep learning-based online recommendation system has been proposed with a Convolutional Neural Network (CNN). Classes of different patterns in the CNN architecture have been determined according to users' and designers' pattern preferences. The deep learning model recommends patterns considering color compatibility for textile products. The proposed model has been trained and tested using our own pattern dataset including 12000 images. Experiments on pattern datasets show the effectiveness of our proposed approach.
Exploring the BipedalWalker benchmark with MAP-Elites and curiosity-driven A3C

The MAP-Elites algorithm can efficiently explore reinforcement learning problems and optimize collections of solutions over user-defined ranges of possible behaviors. However, MAP-Elites can be difficult to scale to highly dimensional problems, such as the optimization of large deep neural networks. Traditional deep reinforcement can train agents with a complex network model by relying on human-designed extrinsic rewards. In complex problems, this translates into reward landscapes that are extremely sparse and hard to explore. This has led to the development of curiosity-driven reinforcement learning algorithms that use intrinsic rewards to continuously optimize for novel policies. While this approach encourages exploration, it remains to be seen if it can be used similarly to MAP-Elites to search for a collection of highly diverse solutions.
Effects of Glow Data Augmentation on Face Recognition System based on Deep Learning

Biometric artificial intelligence application depends on amount of material on which they are trained. In this paper, we integrated Glow data augmentation technique to diversify the facial images dataset to analyze its effects on face classification and identification system based on Convolutional Neural Network (CNN). In first phase, we trained our CNN with publicly available Labeled Faces in the Wild (LFW) database and evaluated the proposed system, which achieved accuracy of 92.2%. In second phase, we diversified LFW dataset with Glow method and then trained our CNN network. The experiment results shows that Glow data augmentation improved the accuracy of proposed network to 93.6%.
On the prediction of continuous integration build failures using search-based software engineering

Continuous Integration (CI) aims at supporting developers in integrating code changes quickly through automated building. However, in such context, the build process is typically time and resource-consuming. As a response, the use of machine learning (ML) techniques has been proposed to cut the expenses of CI build time by predicting its outcome. Nevertheless, the existing ML-based solutions are challenged by problems related mainly to the imbalanced distribution of successful and failed builds. To deal with this issue, we introduce a novel approach based on Multi-Objective Genetic Programming (MOGP) to build a prediction model. Our approach aims at finding the best prediction rules based on two conflicting objective functions to deal with both minority and majority classes. We evaluated our approach on a benchmark of 15,383 builds. The results reveal that our technique outperforms state-of-the-art approaches by providing a better balance between both failed and passed builds.
An Optimized Multilayer Outlier Detection for Internet of Things (IoT) Network as Industry 4.0 Automation and Data Exchange

In this work, a multilayered, multidimensional outlier detection mechanism is proposed. The multilayered system consists of: ultra-lightweight, lightweight and heavyweight detection whereas multiple dimensions involve machine learning, Markov construction, and content and context-based outlier detection. A threshold-based system is proposed for ultra-lightweight and lightweight outlier detection systems. The heavyweight outlier detection system requires higher computational and communicational costs for outlier detection. In simulation it is observed that the optimal number of clusters required for 50, 100, 500, 1000, 2000, 3000, 4000 and 5000 node network are 5–18, 16–17, 5–25, 33–34, 13–39, 38–39, 22–51 and 45–52, respectively.
Survivability modelling using Bayesian network for patients with first and secondary primary cancers

Multiple primary cancers significantly threat patient survivability. Predicting the survivability of patients with two cancers is challenging because its stochastic pattern relates with numerous variables. In this study, a Bayesian network (BN) model was proposed to describe the occurrence of two primary cancers and predict the five-year survivability of patients using probabilistic evidence. Eleven types of major primary cancers and contingent occurrences of secondary cancers were investigated. A nationwide two-cancer database involving 7,845 patients in Taiwan was investigated. The BN topology is rigorously examined and imbalanced dataset is processed by the synthetic minority oversampling technique. The proposed BN survivability prognosis model was compared with benchmark approaches. The proposed model significantly outperformed the back-propagation neural network, logistic regression, support vector machine, and naïve Bayes in terms of sensitivity, which is a critical performance index for the non-survival group. Using the proposed BN model, one can estimate the posterior probabilities for every query provided appropriate prior evidences. The potential survivability information of patients, treatment effects, and socio-demographics factor effects predicted by the proposed model can help in cancer treatment assessment and cancer development monitoring.
Research on legibility of English text based on improved decision tree and intelligent interactive system

The provision of appropriate reading materials is a prerequisite and key to effective reading activities. It is necessary for the learner to select reading materials that stimulate his reading motivation, cause reading interest and conform to his or her own abilities, which can improve the learner’s reading ability. Therefore, how to provide learners with personalized reading materials that meet their ability level and meet their learning needs has gradually become the focus of educational technology research. From the perspective of English text reading experience, this paper studies the information cognition characteristics and operational behavior of mobile terminals and introduces the concept of legibility into the field of information presentation research. Moreover, this study carried out the classification research and feature analysis of information presentation legibility and explored the interaction between reading experience and the legibility of information presentation. In addition, this study studied the performance of the algorithm by experimental analysis. The results show that the research model has certain effects in the difficulty recognition of English text.
A Novel Approach for Knowledge State Representation and Prediction

Online learning systems with open navigation allow learners to select the next learning activity in order to achieve desired mastery. To help learners make an informed choice regarding the next learning activity, we propose to represent and communicate the learner's knowledge state as the average success rate in the course for each skill, rather than as the probability of correctly answering the next question. We first show that we can accurately estimate the proposed knowledge state. We then show that the proposed attention-based model to estimate the knowledge state requires fewer parameters, provides actionable information to the learners, and achieves equivalent or better accuracy compared to RNN (Recurrent Neural Network) based models.
Professional Activity of a University Teacher: Personal Values and Temporal Perception

The purpose of the article is to actualize the problems of personality values and senses in university teachers’ professional activities. The authors present and substantiate the methodology for identifying the relation between personal significance for teachers of various aspects of their professional activities and time costs for the implementation of these aspects (components). The methodology is based on their pairwise comparison. This comparison is carried out first by the criterion of the significance of these aspects, and after 1-2 weeks – by the criterion of their time cost. Based on their own work as well as other studies, the authors identify three of the most timeconsuming aspects of teachers’ activity: the preparation and conducting of lessons and lectures; scientific products creation; work with documents. It is confirmed that the preparation and conducting of lessons and lectures as well as the preparation of various plans, programs, reports, etc. are the dominant components of university teachers’ activity. The disparity between the personal significance and time costs is the smallest if it concerns preparation of lessons. In this case, the time cost is compensated by understanding of its importance. As for work with documents, such compensation does not exist here. So, it turns into a potential source of internal tensions and conflicts. The component “creation of scientific products” is quite contradictory: time costs for it subjectively seem quite large while this component is perceived as not high significant. This is consistent with other studies that show that this aspect of activity often turns out to be formally motivated, not related to serious scientific research. In the article, the situation, empirically recognized by many authors, gets a fundamentally new interpretation, as the temporal parameter acquires the status of value. The authors propose to consider time costs in relation to various aspects of university teachers’ activities in this particular context.