Reviewerly/exHarmony huggingface dataset:
Multimodal Sarcasm Detection: A Deep Learning Approach

In the modern era, posting sarcastic comments on social media became the common trend. Sarcasm is often used by people to taunt or pester others. It is frequently expressed through inflexion, tonal stress in speech or in the form of lexical, pragmatic, and hyperbolic features present in the text. Most of the existing work has been focused on either detecting sarcasm in textual data using text features or audio data using audio features. This article proposed a novel approach by combining textual and audio features together to detecting sarcasm in conversational data. This hybrid method takes a combined vector of extracted audio and text features from their respective models as the input. This combined features will compensated the shortcomings of only text features and vice-versa. The obtained result of hybrid model outperforms both the individual model significantly.
Spatio-temporal graph-based CNNs for anomaly detection in weakly-labeled videos

Abnormal event detection in videos plays an essential role for public security. However, most weakly supervised learning methods ignore the relationship between the complicated spatial correlations and the dynamical trends of temporal pattern in video data. In this paper, we provide a new perspective, i.e., spatial similarity and temporal consistency are adopted to construct Spatio-Temporal Graph-based CNNs (STGCNs). For the feature extraction, we use Inflated 3D (I3D) convolutional networks to extract features which can better capture appearance and motion dynamics in videos. For the spatio graph and temporal graph, each video segment is regarded as a vertex in the graph, and attention mechanism is introduced to allocate attention for each segment. For the spatial-temporal fusion graph, we propose a self-adapting weighting to fuse them. Finally, we build ranking loss and classification loss to improve the robustness of STGCNs. We evaluate the performance of STGCNs on UCF-Crime datasets (total 128 h) and ShanghaiTech datasets (total 317,398 frames) with the AUC score 84.2% and 92.3%, respectively. The experimental results also show the effectiveness and robustness with other evaluation metrics.
Exploiting compressed sensing (CS) and RNA operations for effective content-adaptive image compression and encryption

Compressed sensing (CS) has been applied for image compression and encryption, for it may compress and encrypt images simultaneously. However, some CS-based image encryption algorithms still suffer from low security and compression performance. To solve these problems, a content-adaptive compression-encryption based on CS and RiboNucleic Acid (RNA) is proposed in this paper. Firstly, the 4D laser chaos system was used to construct a measurement matrix for image compression. Secondly, an Improved Fractal Sorting Matrix (IFSM) and its iterative approach are introduced based on the Fractal Sorting Matrix (FSM) for permutation. It has the same characteristics as FSM, but with a greater permutation effect and a broader range of applications. Finally, an adaptive RNA diffusion is designed based on the unique codon of RNA, which includes codon substitution and calculation operations. This adaptive operations make the algorithm more difficult to crack. Additionally, chaotic sequences driven by the hash value of plain image are utilized in the whole encryption, which enhances the correlation between the algorithm and plain image and improves the ability to resist known-plaintext and chosen-plaintext attacks. Experimental results and performance analyses demonstrate that the proposed scheme has excellent security and compression property.
Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement

Real-time kinodynamic trajectory planning in dy-namic environments is critical yet challenging for autonomous driving. In this paper, we propose an efficient trajectory plan-ning system for autonomous driving in complex dynamic sce-narios through iterative and incremental path-speed optimization. Exploiting the decoupled structure of the planning prob-lem, a path planner based on Gaussian process first generates a continuous arc-length parameterized path in the Frenét frame, considering static obstacle avoidance and curvature constraints. We theoretically prove that it is a good generalization of the well-known jerk optimal solution. An efficient s-t graph search method is introduced to find a speed profile along the generated path to deal with dynamic environments. Finally, the path and speed are optimized incrementally and iteratively to ensure kinodynamic feasibility. Various simulated scenarios with both static obstacles and dynamic agents verify the effectiveness and robustness of our proposed method. Experimental results show that our method can run at 20 Hz. The source code is released as an open-source package.
Organizational and team culture as antecedents of protection motivation among IT employees

The rapid development of technology and information systems has led to higher information security-related issues in an organization. The age of remote working (i.e., telecommuting) has further increased information security related incidents that need to be adequately addressed. This paper extends the protection motivation theory by drawing insights from organizational and institutional theory literature to examine how organizational culture and subcultures such as team culture impact information security compliance. The primary objective of this study is to understand the impact of the dimensions of organizational culture and team culture on employees' perceived threats and coping motivation associated with information security compliance. The study applied structural equation modeling to analyze survey responses of 341 IT employees in the United States. The result of the study indicates that both organization and team culture impacts employees' perception to appraise threat and coping, which in turn impacts behavioral intention to comply with information security policies. The findings of this study contribute to the information security compliance research by demonstrating the importance of developing an information security culture within an organization and its subgroups.
Blockchain-based identity management and access control framework for open banking ecosystem

Financial institutions are following an open banking (OB) trend for service innovation and integration. OB allows third-party service providers (TSPs) to access user financial data for purposes of finding the best deals and improving user experiences. The trust in third parties required for OB ecosystem success raises questions about digital identity integration, data sharing, and privacy preservation. Decentralized applications (DApps) for classifying and protecting data privacy, recording user consent, tracking TSP access actions, managing application programming interfaces (APIs), and preserving self-sovereign identities already exist, but in some countries such as Taiwan they have yet to be integrated into an operable three-phase OB approach. After identifying all major requirements of primary OB participants, we develop a blockchain-based identity management and access control (BIMAC) framework that shares some advantages of both traditional banking and blockchain technology. The BIMAC infrastructure applies smart contracts and a stateless authentication mechanism to form a reliable personal information transaction security control (PITSC) platform that offers such functionalities as decentralized third-party login (TPL), the ability to open bank accounts online, data authorization/revocation, integrated payouts, and TSP access monitoring. System performance evaluation results indicate that the frequent execution functions of the proposed framework have lower computation costs than the average transaction cost of public Ethereum.
Temporal knowledge graph question answering via subgraph reasoning

Knowledge graph question answering (KGQA) has recently received a lot of attention and many innovative methods have been proposed in this area, but few have been developed for temporal KGQA. Most of the existing temporal KGQA methods focus on semantic or temporal level matching and lack the ability to reason about time constraints. In this paper we propose a subgraph-based model for answering complex questions over temporal knowledge graphs (TKG), inspired by human cognition. Our method, called SubGraph Temporal Reasoning (SubGTR), consists of three main modules: implicit knowledge extraction, relevant facts search, and subgraph logic reasoning. First, the question is reformulated using background knowledge stored in the temporal knowledge graph to acquire explicit time constraints. Then, the TKG is being searched to identify relevant entities and obtain an initial scoring of them. Finally the time constraints are quantified and applied using temporal logic to reach to the final answer. To evaluate our model we experiment against temporal QA benchmarks. We observe that existing benchmarks contain many pseudo-temporal questions, and we propose Complex-CronQuestions, which a filtered version of CronQuestions and which can better demonstrate the model’s inference ability for complex temporal questions. Experimental results show that SubGTR achieves state-of-the-art performance on both CronQuestions and Complex-CronQuestions. Moreover, our model shows better performance in handling the entity cold-start problem compared to existing temporal KGQA methods.
A Fair and Safe Usage Drug Recommendation System in Medical Emergencies by a Stacked ANN

The importance of online recommender systems for drugs, medical professionals, and hospitals is growing. Today, the majority of people use online consultations for drug recommendations for all types of health issues. Emergencies such as pandemics, floods, or cyclones can be helped by the medical recommender system. In the era of machine learning (ML), recommender systems produce more accurate, quick, and reliable clinical predictions with minimal costs. As a result, these systems maintain better performance, integrity, and privacy of patient data in the decision-making process and provide precise information at any time. Therefore, we present drug recommender systems with a stacked artificial neural network (ANN) model to improve the fairness and safety of treatment for infectious diseases. To reduce side effects, drugs are recommended based on a patient’s previous health profile, lifestyle, and habits. The proposed system produced results with 97.5% accuracy. A system such as this could be useful in recommending safe medicines to patients, especially during health emergencies.
Graph neural network-enabled manufacturing method classification from engineering drawings

While millions of scanned engineering drawings are received every year, the online quotation companies for custom mechanical parts have experienced a surging need to increase their processing efficiency by replacing the currently manual inspection process with an automatic system. Previous work has used traditional, and data-driven computer-vision approaches to detect symbols and text information from the drawings. However, there lacks a unified framework to determine the associated manufacturing processes as a critical step for realizing an automatic quoting system. In this paper, we propose a computational framework to automatically determine the manufacturing method appropriate to produce each queried engineering drawing, such as lathing, sheet metal bending, and milling. We present a data-driven framework that directly processes the raster images with a series of pre-processing steps and accurately determines the corresponding manufacturing methods for the queried part with a graph neural network. We propose a novel line tracing algorithm to transform complex geometries in engineering drawings into vectorized line segments with minimal information loss. To extract the shape contours, we use an efficient image segmentation network to remove the information tables, followed by a sequential graph neural network to detect and eliminate dimension lines. Finally, we propose a novel graph neural network with updated graph connections to hierarchically distill graph descriptors and classify the engineering drawing by its appropriate manufacturing method. Our framework has been validated on industry datasets. We verify that our framework can effectively classify the engineering drawings with an accuracy of 90.78%. We further assess our framework by comparing it against state-of-the-art image classification algorithms(+17.20%).
Factors Influencing YouTube as a Learning Tool and Its Influence on Academic Achievement in a Bilingual Environment Using Extended Information Adoption Model (IAM) with ML Prediction—Jordan Case Study

YouTube usage as a learning tool is evident among students. Hence, the goal of this study is to examine the various factors that influence the use of YouTube as a learning tool, which influences academic achievement in a bilingual academic context. Using survey data from 704 YouTube users from Jordan’s bilingual academic institutes, the research model was empirically validated. Using Amos 20, structural equation modeling (SEM) was performed to assess the study hypotheses. SEM permits concurrent checking of the direct and indirect effects of all hypotheses. Confirmatory factor analysis (CFA) was used to validate the instrument items’ properties in addition to machine learning methods: ANN, SMO, the bagging reduced error pruning tree (RepTree), and random forest. The empirical results offer several key findings: academic achievement (AA) is influenced by the information adoption (IA) of YouTube as a learning tool. Information adoption (IA) is influenced by information usefulness (IU). Source credibility (SC) and information quality (IQ) both influence information usefulness (IU), while information language (IL) does not. Information quality (IQ) is influenced by intrinsic, contextual, and accessibility information quality. This study adds to the literature by empirically testing and theorizing the effects of YouTube as a learning tool on the academic achievement of Jordanian university students who are studying in bilingual surroundings.
Traditional and context-specific spam detection in low resource settings

Social media data has a mix of high and low-quality content. One form of commonly studied low-quality content is spam. Most studies assume that spam is context-neutral. We show on different Twitter data sets that context-specific spam exists and is identifiable. We then compare multiple traditional machine learning models and a neural network model that uses a pre-trained BERT language model to capture contextual features for identifying spam, both traditional and context-specific, using only content-based features. The neural network model outperforms the traditional models with an F1 score of 0.91. Because spam training data sets are notoriously imbalanced, we also investigate the impact of this imbalance and show that simple Bag-of-Words models are best with extreme imbalance, but a neural model that fine-tunes using language models from other domains significantly improves the F1 score, but not to the levels of domain-specific neural models. This suggests that the strategy employed may vary depending upon the level of imbalance in the data set, the amount of data available in a low resource setting, and the prevalence of context-specific spam vs. traditional spam. Finally, we make our data sets available for use by the research community.
An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

Analyzing the sentiment of Arabic texts is still a big research challenge due to the special characteristics and complexity of the Arabic language. Few studies have been conducted on Arabic sentiment analysis (ASA) compared to English or other Latin languages. In addition, most of the existing studies on ASA analyzed datasets collected from Twitter. However, little attention was given to the huge amounts of reviews for governmental or commercial mobile applications on Google Play or the App Store. For instance, the government of Saudi Arabia developed several mobile applications in healthcare, education, and other sectors as a response to the COVID-19 pandemic. To address this gap, this paper aims to analyze the users’ opinions of six applications in the healthcare sector. An improved sentiment classification approach was proposed for measuring user satisfaction toward governmental services’ mobile apps using machine learning models with different preprocessing methods. The Arb-AppsReview dataset was collected from the reviews of these six mobile applications available on Google Play and the App Store, which includes 51k reviews. Then, several feature engineering approaches were applied, which include Bing Liu lexicon, AFINN, and MPQA Subjectivity Lexicon, bag of words (BoW), term frequency-inverse document frequency (TF-IDF), and the Google pre-trained Word2Vec. Additionally, the SMOTE technique was applied as a balancing technique on this dataset. Then, five ML models were applied to classify the sentiment opinions. The experimental results showed that the highest accuracy score (94.38%) was obtained by applying a support vector machine (SVM) using the SMOTE technique with all concatenated features.
Social Scripts and Expectancy Violations: Evaluating Communication with Human or AI Chatbot Interactants

As artificial intelligence (AI) agents like chatbots play larger roles in daily life, questions arise regarding how people evaluate their communication. Perspectives applying communication scripts to human-AI interactions propose that outcomes are determined by messages and the embedded cues therein. The expectancy violations perspective posits that message characteristics are less important than whether they are expected or unexpected. A pilot study established baseline expectancies about humans’ and chatbots’ conversational contingency and response latencies. A 2 (contingency: more/less contingent responses) × 2 (latency: fast/slow responses) × 2 (communicator identity: human/chatbot) experiment then tested predictions derived from human-human communication scripts and expectancy violations using textual variations in an e-commerce chat. Communicators showing greater conversational contingency and faster responses were most credible, whether they were human or chatbots, but chatbots were consistently less socially attractive than humans. Results show that humans and chatbots are evaluated similarly regarding the functional, but not the relational aspects of communication. There was greater support for the communication script perspective than the expectancy violations perspective regarding interactions with chatbots.
Dense open-set recognition based on training with noisy negative images

Deep convolutional models often produce inadequate predictions for inputs which are foreign to the training distribution. Consequently, the problem of detecting outlier images has recently been receiving a lot of attention. Unlike most previous work, we address this problem in the dense prediction context. Our approach is based on two reasonable assumptions. First, we assume that the inlier dataset is related to some narrow application field (e.g. road driving). Second, we assume that there exists a general-purpose dataset which is much more diverse than the inlier dataset (e.g. ImageNet-1 k). We consider pixels from the general-purpose dataset as noisy negative samples since most (but not all) of them are outliers. We encourage the model to recognize borders between the known and the unknown by pasting jittered negative patches over inlier training images. Our experiments target two dense open-set recognition benchmarks (WildDash 1 and Fishyscapes) and one dense open-set recognition dataset (StreetHazard). Extensive performance evaluation indicates competitive potential of the proposed approach.
Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey

Recommender Systems (RS) are established to deal with the preferences of users to enhance their experience and interest in innumerable online applications by streamlining the stress persuaded by the reception of excessive information through the recommendation methods. Although researches have put a lot of efforts in making recommendation processes accurate, specific, and personalized; different issues like cold start, data sparsity or gray sheep etc., still pop up in one or the other form of challenges. Recently, exploitation of Knowledge Graph (KG)-based data as Side Information in recommendation methods has revealed as a sign of resolution to the corresponding challenges; and thus, acquired incredible focus, applicability, and popularity. The incorporation of KG in recommendation has not only effectively alleviated the contrasting challenges, but also has provided specific, accurate, personalized and explainable recommendations about the target items to the end users. In this paper, we explore well-known RSs, popular knowledge repositories, benchmark datasets, recommendation methods, and future research dimensions about the current research. Intuitively, we investigate recommendation methods and associated datasets with respect to the corresponding application scenarios in a categorical way.
HeBERT and HebEMO: A Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition

This paper introduces HeBERT and HebEMO. HeBERT is a Transformer-based model for modern Hebrew text, which relies on a BERT (Bidirectional Encoder Representations for Transformers) architecture. BERT has been shown to outperform alternative architectures in sentiment analysis, and is suggested to be particularly appropriate for MRLs. Analyzing multiple BERT specifications, we find that while model complexity correlates with high performance on language tasks that aim to understand terms in a sentence, a more-parsimonious model better captures the sentiment of entire sentence. Either way, out BERT-based language model outperforms all existing Hebrew alternatives on all common language tasks. HebEMO is a tool that uses HeBERT to detect polarity and extract emotions from Hebrew UGC. HebEMO is trained on a unique Covid-19-related UGC dataset that we collected and annotated for this study. Data collection and annotation followed an active learning procedure that aimed to maximize predictability. We show that HebEMO yields a high F1-score of 0.96 for polarity classification. Emotion detection reaches F1-scores of 0.78-0.97 for various target emotions, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even among English-language models of emotion detection.