Reviewerly/exHarmony huggingface dataset:
An Optimized Algorithm for Dangerous Driving Behavior Identification Based on Unbalanced Data

It is of great significance to identify dangerous driving behavior by extracting vehicle trajectory through video monitoring to ensure highway traffic safety. At present, there is no suitable method to identify dangerous driving vehicles accurately based on trajectory data. This paper aims to develop a detection algorithm for identifying dangerous driving behavior based on the road scene, which is mainly composed of imbalanced dangerous driver detection and labeling, extraction of driving behavior characteristics and the establishment of a recognition model about dangerous driving behavior. Firstly, this paper defines the risk index of the vehicle related to five types of dangerous driving behavior: dangerous following, lateral deviation, frequent acceleration and deceleration, frequent lane change, and forced insertion. Then, a variety of methods, including K-means clustering, local factor anomaly algorithm, isolation forest and OneClassSVM, are used to carry out anomaly detection on the risk indicators of drivers, and the optimal method is proposed to identify dangerous drivers. Then, the speed and acceleration of each vehicle are Fourier transformed to obtain the characteristics of the driver’s driving behavior. Finally, considering the imbalanced characteristic of the analyzed dataset with a very small proportion of dangerous drivers, this paper compares a variety of imbalanced classification algorithms to optimize the recognition performance of dangerous driving behavior. The results show that the OneClassSVM detection algorithm can be effectively applied to the identification of dangerous driving behavior. The improved Xgboost algorithm performs best for the extremely imbalanced data of dangerous drivers.
Reality-Virtuality Technologies in the Field of Materials Science and Engineering

The increasing use of reality-virtuality technologies (RVTs, which encompass virtual, augmented, and mixed reality) in different fields over the last decade is a phenomenon for which materials science and engineering (MSE) is no exception. To obtain an overview of the implementation of RVTs in MSE, this team conducted a systematic search of the scientific literature published since 2010 addressing the use of RVTs in MSE. Forty-one relevant papers were selected and analyzed in depth to reach several conclusions, including: (i) most of the works (67.3%) are focused on the MSE area of materials structure, processing, and properties, which implies that there are great possibilities for research in other MSE areas; (ii) most of the works (86.8%) are aimed exclusively at education or research, which means that there are many fields outside of the university in which the use of RVT tools has not been developed and evaluated; (iii) the most used technology is virtual reality (85.1%), which means that there are many research possibilities focused on augmented and mixed reality. Researchers can find in the present work examples of the use of RVTs in MSE as well as other relevant information useful to open new lines of research and ideas that can contribute to their current and future work.
Optimizing Graph Neural Network With Multiaspect Hilbert–Schmidt Independence Criterion

The graph neural network (GNN) has demonstrated its superior power in various data mining tasks and has been widely applied in diversified fields. The core of GNN is the aggregation and combination functions, and mainstream GNN studies focus on the enhancement of these functions. However, GNNs face a common challenge, i.e., useless features contained in neighbor nodes may be integrated into the target node during the aggregation process. This leads to poor node embedding and undermines downstream tasks. To tackle this problem, this article proposes a novel GNN optimization framework GNN-MHSIC by introducing the nonparametric dependence method Hilbert-Schmidt independence criterion (HSIC) under the guidance of information bottleneck. HSIC is utilized to guide the information propagation among layers of a GNN from multiaspect views. GNN-MHSIC aims to achieve three main objectives: 1) minimizing the HSIC between the input features and the propagation layers; 2) maximizing the HSIC between the propagation layers and the ground truth; and 3) minimizing the HSIC between the propagation layers. With a multiaspect design, GNN-MHSIC can minimize the propagation of redundant information while preserving relevant information about the target node. We prove GNN-MHSIC's finite upper and lower bounds theoretically and evaluate it experimentally with four classic GNN models, including the graph convolutional network, the graph attention network (GAT), the heterogeneous GAT, and the heterogeneous graph (HG) propagation network on three widely used HGs. The results illustrate the usefulness and performance of GNN-MHSIC.
Deep learning-based prediction of heart failure rehospitalization during 6, 12, 24-month follow-ups in patients with acute myocardial infarction

Heart failure is a clinical syndrome that occurs when the heart is too weak or stiff and cannot pump enough blood that our body needs. It is one of the most expensive diseases due to frequent hospitalizations and emergency room visits. Reducing unnecessary rehospitalizations is also an important and challenging task that has the potential of saving healthcare costs, enabling discharge planning, and identifying patients at high risk. Therefore, this paper proposes a deep learning-based prediction model of heart failure rehospitalization during 6, 12, 24-month follow-ups after hospital discharge in patients with acute myocardial infarction (AMI). We used the Korea Acute Myocardial Infarction-National Institutes of Health (KAMIR-NIH) registry which included 13,104 patient records and 551 features. The proposed deep learning-based rehospitalization prediction model outperformed traditional machine learning algorithms such as logistic regression, support vector machine, AdaBoost, gradient boosting machine, and random forest. The performance of the proposed model was accuracy, the area under the curve, precision, recall, specificity, and F1 score of 99.37%, 99.90%, 96.86%, 98.61%, 99.49%, and 97.73%, respectively. This study showed the potential of a deep learning-based model for cardiology, which can be used for decision-making and medical diagnosis tool of heart failure rehospitalization in patients with AMI.
CupMar: A deep learning model for personalized news recommendation based on contextual user-profile and multi-aspect article representation

Abstract In modern days, making recommendation for news articles poses a great challenge due to vast amount of online information. However, providing personalized recommendations from news articles, which are the sources of condense textual information is not a trivial task. A recommendation system needs to understand both the textual information of a news article, and the user contexts in terms of long-term and temporary preferences via the user’s historic records. Unfortunately, many existing methods do not possess the capability to meet such need. In this work, we propose a neural deep news recommendation model called CupMar, that not only is able to learn the user-profile representation in different contexts, but also is able to leverage the multi-aspects properties of a news article to provide accurate, personalized news recommendations to users. The main components of our CupMar approach include the News Encoder and the User-Profile Encoder. Specifically, the News Encoder uses multiple properties such as news category, knowledge entity, title and body content with advanced neural network layers to derive informative news representation, while the User-Profile Encoder looks through a user’s browsed news, infers both of her long-term and recent preference contexts to encode a user representation, and finds the most relevant candidate news for her. We evaluate our CupMar model with extensive experiments on the popular Microsoft News Dataset (MIND), and demonstrate the strong performance of our approach.
Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition

Deep-learning-based, cross-age face recognition has improved significantly in recent years. However, when using the discriminative method, it is still challenging to extract robust age-invariant features that can reduce the interference caused by age. In this paper, we propose a novel, effective, attention-based feature decomposition model, the age-invariant features extraction network, which can learn more discriminative feature representations and reduce the disturbance caused by aging. Our method uses an efficient channel attention block-based feature decomposition module to extract age-independent identity features from facial representations. Our end-to-end framework learns the age-invariant features directly, which is more convenient and can greatly reduce training complexity compared with existing multi-stage training methods. In addition, we propose a direct sum loss function to reduce the interference of age-related features. Our method achieves a comparable and stable performance. Experimental results demonstrate superior performance on four benchmarked datasets over the state-of-the-art. We obtain the relative improvements of 0.06%, 0.2%, and 2.2% on the cross-age datasets CACD-VS, AgeDB, and CALFW, respectively, and a relative 0.03% improvement on a general dataset LFW.
Evaluating a mobile instant messaging tool for efficient large-class speaking instruction

The overarching goal of this design-based research was to explore WhatsApp’s potential for facilitating and supporting speaking and pronunciation instruction in an EFL large-class speaking course. More specifically, this paper explored the perceived learning outcomes of WhatsApp-supported pedagogy in large English-speaking classes. Ninety-nine English students practiced their English by recording themselves, listening to the recordings, and comparing their recordings to native speakers’ pronunciation. When satisfied with the quality, they sent their own recordings to a five-member WhatsApp group made up of their classmates. The recordings involved speed reading, singing a song or reading the lyrics aloud, tongue twisters, and making a video summary. Students listened to the recordings of the other four group members and provided feedback on pronunciation. The data were collected via an open-ended survey and individual and focus-group interviews. Qualitative content analysis of the data indicated that the vast majority of the participants reacted positively and mentioned the following major contributions to large speaking classes: (1) educational affordances for pronunciation, speaking, motivation, vocabulary, and reading; and (2) technological affordances, i.e. ease of use and accessibility. The adopted approach supported speaking and pronunciation instruction, specifically through instructor and peer feedback, speed reading, and ease of access. The paper concludes that it is rewarding to unravel WhatsApp’s potential as a mobile instant messaging (MIM) tool in large-classes.
Byzantine-Robust and Efficient Federated Learning for the Internet of Things

The expansion of Internet-of-Things (IoT) devices with a wealth of generated data opens up new possibilities for intelligent IoT applications (i.e., smart home and smart transportation), but the increasing concern about data privacy makes the enabling force of intelligent IoT, machine learning (ML), harder to deploy. Federated learning (FL), an emerging distributed ML paradigm that allows on-device ML model training without sharing private raw data, is becoming a promising solution to achieve collaborative intelligence in IoT. However, the privacy-preserving design of FL makes it vulnerable to Byzantine workers who behave arbitrarily and send poisonous model updates to the central server to corrupt the joint learning process. Moreover, traditional FL suffers from massive communications overhead due to the large number of training rounds to convergence with non-independent identically distributed (non-i.i.d.) data across clients. To address these problems, we propose a novel robust aggregation rule for FL, federated mutual information (FedMI) which leverages the mutual information between clients to build resilience to Byzantine workers and accelerate the convergence speed. We perform experiments over the non-i.i.d FEMNIST dataset under different adversarial settings. Experimental results demonstrate the effectiveness of the proposed FedMI: much faster convergence speed and higher defense capability when compared to the state-of-the-art robust aggregation rules for FL.
Back to the Past—An Experimental Investigation of the Effects of Immersive Historical Environments on Empathy and Morality

Abstract Immersive environments are known for fostering empathy via their technological properties that offer users the opportunity to get immersed in a virtual environment and take other perspectives. As empathy and perspective-taking are considered to be the basis of moral development, the present study examines the potential of historical immersive media applications for the individual's moral development according to Kohlberg's stage model (1958). An integrative approach was used to examine the role of technological immersion as well as the recipients’ immersion (presence, transportation) in eliciting empathy and promoting moral development and behavior. Therefore, an online experiment with one factorial between-subject design was conducted in which participants (N = 289) were exposed to historical media that differed only in their extent of technological immersion; participants either read a text, or watched a video, or a 360° video. In line with previous research, results showed that technological immersion positively affects presence, while transportation was not influenced by technological aspects. Furthermore, results revealed positive effects of transportation and presence on empathy which, in turn, was positively related to moral orientation and behavior. The study indicates that immersive historical environments can promote empathy and morality due to their immersive characteristics.
Investigating the impact of vulnerability datasets on deep learning-based vulnerability detectors

Software vulnerabilities have led to system attacks and data leakage incidents, and software vulnerabilities have gradually attracted attention. Vulnerability detection had become an important research direction. In recent years, Deep Learning (DL)-based methods had been applied to vulnerability detection. The DL-based method does not need to define features manually and achieves low false negatives and false positives. DL-based vulnerability detectors rely on vulnerability datasets. Recent studies found that DL-based vulnerability detectors have different effects on different vulnerability datasets. They also found that the authenticity, imbalance, and repetition rate of vulnerability datasets affect the effectiveness of DL-based vulnerability detectors. However, the existing research only did simple statistics, did not characterize vulnerability datasets, and did not systematically study the impact of vulnerability datasets on DL-based vulnerability detectors. In order to solve the above problems, we propose methods to characterize sample similarity and code features. We use sample granularity, sample similarity, and code features to characterize vulnerability datasets. Then, we analyze the correlation between the characteristics of vulnerability datasets and the results of DL-based vulnerability detectors. Finally, we systematically study the impact of vulnerability datasets on DL-based vulnerability detectors from sample granularity, sample similarity, and code features. We have the following insights for the impact of vulnerability datasets on DL-based vulnerability detectors: (1) Fine-grained samples are conducive to detecting vulnerabilities. (2) Vulnerability datasets with lower inter-class similarity, higher intra-class similarity, and simple structure help detect vulnerabilities in the original test set. (3) Vulnerability datasets with higher inter-class similarity, lower intra-class similarity, and complex structure can better detect vulnerabilities in other datasets.
Equivalent imputation methodology for handling missing data in compositional geochemical databases of geothermal fluids

Chemical, isotopic and thermal signatures, space-temporal concentration patterns, and geochemical conceptual models are used to gather insights of the internal structure of geothermal reservoirs. These investigations rely on historical and latest data dealing with the composition of the geothermal fluids discharged from either producer wells or surface manifestations. The creation of a compositional database is based on information compiled from the sample collection and handling, and the quantification results of chemical analyses. Sometimes, this integrated task may result incomplete due to some technical issues that might generate information gaps or missing data which are struggle to control in the field and lab works. To solve the low-quality data, the missing concentration values or the significant information gaps of geochemical databases, the use of machine learning techniques has been explored in the present study. Equivalent imputation models were proposed, for the first time, to rebuild a world solute-based geochemical database which exhibited some gaps and missing data in the records of the chemical composition of fluids from geothermal production wells. The application of these models enabled that missing records to be successfully recovered by using single and multiple imputation algorithms. The imputation models were efficiently evaluated by applying statistical equivalence metrics carried out between imputed and observed (measured) data. To avoid an arbitrary use of these models, and to keep the original geochemical signatures of the fluid composition patterns, the imputed results were assessed by conventional geochemistry tools (e.g., charge unbalances, fluid classification, and some water–rock interaction footprints). Results show that single imputation algorithms that model locally provide equivalent imputation values with acceptable confidence, not only in terms of statistical metrics but also improving charge unbalances in chemical analyses of anomalous samples without altering the original water–rock interaction signatures of the geothermal systems under evaluation. In contrast, multiple imputation algorithms only tend to obtain better charge unbalances by altering the original water–rock interaction signatures. The novel use of the imputation techniques for filling geochemical databases and recovering missing data will allow a better interpretation of hydrogeochemistry modeling in future geothermal exploration and exploitation studies.
Who cares about learning design? Near future superheroes and villains of an educational ethics of care

This article undertakes a critical appraisal of learning design and its relation to ethical ideas of care. We give an account of three personae of near future learning designers, developed using speculative methods, seeded with real-world data comprising job advertisements and validated with learning designers. The personae illustrate conflicts about the role of learning designers within the teaching and research missions of the academy and issues of care or lack thereof for these workers. The disembodied skills of learning designer job advertisements are contrasted with the bodies of (more than) real people that can suffer and care. We finish by contributing elements of a speculative job advertisement for a learning designer, who will help shape educational spaces of the near future by entangled care and unencumbered attentiveness.
AAC: Automatic Augmentation for Crowd Counting

Intelligent crowd management is important for city monitoring. In the task of crowd counting, insufficient training samples with labels are great challenges. In order to improve the performance of the counting model, data augmentation is an effective method. This paper proposes an automatic augmentation framework for counting (AAC) based on the deep reinforcement learning. We first pre-train the model and iteratively generate a data augmentation strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm on the divided validation dataset. When the optimal augmentation action for the model on a specific dataset is found, the model is fine-tuned by the augmented dataset. At the same time, we release a large-scale crowd counting dataset HaCrowd describing Hajj scenario. Finally, five typical crowd counting models are used to carry out augmentation experiments on four small datasets including HaCrowd. The experimental results show that AAC method can be used to generate flexible augmentation strategy for counting model aimed for specific datasets. With the augmentation dataset to train model, it can further improve the performance of counting model. The download link of the HaCrowd is (https://github.com/KAU-Smart-Crowd/HaCrowd).
Associative reasoning-based interpretable continuous decision making in industrial production process

Adaptive decision making in continuous production process is challenging for the uncertain characteristics of dynamics system state, decision-making time, as well as the correlation between the previous decision and the subsequent decision. In this paper, a data-driven continuous decision-making model with self-adaption and interpretability is proposed. For the uncertainty of industrial data, a new form of information granule with variable parameters is designed to describe the knowledge extracted from data. For the continuity of the decision-making process, a data-driven interpretable associative reasoning model is established by considering the similarity of knowledge and the attention mechanism. Aiming at the dynamic feature in the decision-making process, a data-driven associative learning method is proposed to obtain the adaptivity of the proposed model, which generates and updates routes according to the co-activation and simultaneous activities of the information granules. To reason decision results, the proposed model searches and records the reasoning routes according to the reasoning results and the objective at the decision points. Noisy chaotic time series data and a number of classification datasets are employed for experimental analyses, and the results indicate that the proposed method exhibits high reasoning accuracy, anti-noise ability, and interpretability. In addition, the experimental results based on industrial data indicate that the decision-making model is capable of giving a reasonable, effective and interpretable decision scheme and suitable for establishing a credible decision-making system.
Deep-Learning-Based Adaptive Model for Solar Forecasting Using Clustering

Accurate short-term solar forecasting is challenging due to weather uncertainties associated with cloud movements. Typically, a solar station comprises a single prediction model irrespective of time and cloud condition, which often results in suboptimal performance. In the proposed model, different categories of cloud movement are discovered using K-medoid clustering. To ensure broader variation in cloud movements, neighboring stations were also used that were selected using a dynamic time warping (DTW)-based similarity score. Next, cluster-specific models were constructed. At the prediction time, the current weather condition is first matched with the different weather groups found through clustering, and a cluster-specific model is subsequently chosen. As a result, multiple models are dynamically used for a particular day and solar station, which improves performance over a single site-specific model. The proposed model achieved 19.74% and 59% less normalized root mean square error (NRMSE) and mean rank compared to the benchmarks, respectively, and was validated for nine solar stations across two regions and three climatic zones of India.