Reviewerly/exHarmony huggingface dataset:
Adoption of Cloud Computing in the organizations: a bibliometric analysis of this technology within a digital transformation context

Purpose: The purpose of this study is identify and analyze articles on the adoption of Cloud Computing services in the business field. The study intends to demonstrate the increasing relevance and impact of Cloud Computing technology on business processes.Design/methodology/approach: The study adopted a Bibliometric Review methodology to collect and analyze data. A total of 1,330 articles were collected from the Scopus (Elsevier) database, and various aspects such as authors, journals, and countries were considered. The analysis includes the use of maps to visualize the co-occurrence of terms, co-citation of references, and bibliographic coupling.Findings: The investigation reveals that the adoption of Cloud Computing services in the business environment is a rapidly growing area of research. The study provides an overview of the theme and highlights the significance of Cloud Computing technology in enhancing business processes’ efficiency.Research limitations/implications: The study’s limitations include relying solely on articles available in the Scopus (Elsevier) database and focusing on the period between 2008 and 2020. Future research can expand the analysis by including a broader range of databases and considering a more recent timeframe.Practical implications: The findings of this study have practical implications for businesses, as they highlight the benefits of adopting Cloud Computing services. The technology offers low cost and flexible use, contributing to increased efficiency in business processes.Social implications: The adoption of Cloud Computing services can have significant social impacts by enabling businesses to provide enhanced value to their clients. The technology’s efficiency and flexibility contribute to improved service delivery and customer satisfaction.Originality/value: This study contributes to the advancement of knowledge in the field of Cloud Computing adoption in the business field. The bibliometric analysis provides a comprehensive overview of the research landscape and highlights the key contributions and trends in this area.
TTHE IMPLEMENTATION OF MATCHING GAMES TO IMPROVE INDONESIAN LEARNING OUTCOMES FOR THE FIRST-GRADE STUDENTS AT SDN 1 CRJ

The low Indonesian learning outcomes of the first-grade students at SDN 1 CRJ is caused by the implementation of teacher-centered learning methods like the lecture method. To overcome it, the Classroom Action Research in this paper applies the Matching Games game method to students. This paper reports an improvement of students’ learning outcomes in reading and writing Indonesian learning subjects in chapter 5 of "Teman Baru" with the entitled theme of "Bersikap Baik terhadap Teman" through the Matching Games method. There were three cycles of CAR applied to 12 students, which contains the planning, action, observation, and reflection stages for each cycle. The data analysis technique used in the research was descriptive, which compared cross-cycle test scores with indicators of success. The results indicate that the average score of students’ learning outcomes in cycle I was 66.66 with only 50% of students who are able to achieve the learning objectives. Whereas in cycle II it was 80.83 with 90% of students who achieve the learning objectives. Based on the research results explained previously, the researcher concludes the results of Indonesian learning outcomes in reading and writing that students can be improved by applying the Matching Games method for elementary students at grade 1.
Heterogeneous Hypergraph Neural Network for Social Recommendation using Attention Network

Graph neural networks (GNNs) have been used extensively as a backbone for social recommendation. However, their application to a diverse range of situations is still rather limited. This is because graph structures only leverage pairwise user relationships. They cannot capture the higher-order relationships so common in the real world, and ignoring the interest friends and strangers might have in similar items is severely hampering the expressiveness of the current graph-based recommendation models. Hence, in this paper, we outline a heterogeneous hypergraph neural network for social recommendation called HHGSA that incorporates an attention network to address these issues. The hypergraph is able to represent higher-order relationships through five motifs: friend and stranger item appeal, item similarity, user similarity based on interactions with items, and social relations. Two modules, the attentive vertex aggregation module, and the attentive hyperedge aggregation module, capture user and item attention. In addition, it has been discovered that similar items have identical appeal when displayed to users. A GNN aggregates the user embedding data, including information about the friend and stranger and item embeddings. Finally, information about users and items is aggregated for social recommendations. Extensive experiments on four datasets demonstrate that the HHGSA model outperforms a wide range of baselines and can significantly improve the accuracy of recommendations.
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation

Social recommendation based on social network has achieved great success in improving the performance of the recommendation system. Since social network (user-user relations) and user-item interactions are both naturally represented as graph-structured data, Graph Neural Networks (GNNs) have thus been widely applied for social recommendation. Despite the superior performance of existing GNNs-based methods, there are still several severe limitations: (i) Few existing GNNs-based methods have considered a single heterogeneous global graph which takes into account user-user relations, user-item interactions, and item-item similarities simultaneously. That may lead to a lack of complex semantic information and rich topological information when encoding users and items based on GNN. (ii) Furthermore, previous methods tend to overlook the reliability of the original user-user relations which may be noisy and incomplete. (iii) More importantly, the item-item connections established by a few existing methods merely using initial rating attributes or extra attributes (such as category) of items, may be inaccurate or sub-optimal with respect to social recommendation. In order to address these issues, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation. GL-HGNN aims to learn a heterogeneous global graph that makes full use of user-user relations, user-item interactions and item-item similarities in a unified perspective. To this end, we design a Graph Learner (GL) method to learn and optimize user-user and item-item connections separately. Moreover, we employ a Heterogeneous Graph Neural Network (HGNN) to capture the high-order complex semantic relations from our learned heterogeneous global graph. To scale up the computation of graph learning, we further present the Anchor-based Graph Learner (AGL) to reduce computational complexity. Extensive experiments on four real-world datasets demonstrate the effectiveness of our model.
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy

Social media has drastically reshaped the world that allows billions of people to engage in such interactive environments to conveniently create and share content with the public. Among them, text data (e.g., tweets, blogs) maintains the basic yet important social activities and generates a rich source of user-oriented information. While those explicit sensitive user data like credentials have been significantly protected by all means, personal private attribute (e.g., age, gender, location) disclosure due to inference attacks is somehow challenging to avoid, especially when powerful natural language processing (NLP) techniques have been effectively deployed to automate attribute inferences from implicit text data. This puts users’ attribute privacy at risk. To address this challenge, in this article, we leverage the inherent vulnerability of machine learning to adversarial attacks, and design a novel text-space Adv ersarial attack for S ocial G ood, called Adv4SG . In other words, we cast the problem of protecting personal attribute privacy as an adversarial attack formulation problem over the social media text data to defend against NLP-based attribute inference attacks. More specifically, Adv4SG proceeds with a sequence of word perturbations under given constraints such that the probed attribute cannot be identified correctly. Different from the prior works, we advance Adv4SG by considering social media property, and introducing cost-effective mechanisms to expedite attribute obfuscation over text data under the black-box setting. Extensive experiments on real-world social media datasets have demonstrated that our method can effectively degrade the inference accuracy with less computational cost over different attribute settings, which substantially helps mitigate the impacts of inference attacks and thus achieve high performance in user attribute privacy protection.
Prediction-Retrodiction Measurements for Teleportation and Conditional State Transfer

Regular measurements allow predicting the future and retrodicting the past of quantum systems. Time-nonlocal measurements can leave the future and the past uncertain, yet establish a relation between them. We show that continuous time-nonlocal measurements can be used to transfer a quantum state via teleportation or direct transmission. Considering two oscillators probed by traveling fields, we analytically identify strategies for performing the state transfer perfectly across a wide range of linear oscillator-field interactions beyond the pure beam-splitter and two-mode-squeezing types.
“Discomforting VR”: Listening, Feeling, Contacting Virtual Reality Community

This paper asks what we can learn from discomfort in virtual reality (VR) beyond perception-altering or consciousness-raising technical affordances. I accomplish this by directing focus upon the everyday VR user and using ethnographic participant observation. I first define a working concept called “discomforting VR” within the context of a corporate marketing scheme, a concept that focuses on unsettling feelings generated by VR experiences. Second, I report on research methods that aim to take a grounded approach toward how to better listen to and feel users in VR. I present two distinct “ethnographic logs,” which are a narration from fieldwork in operation, and I argue that VR can best be understood as a technology that hosts significant community. These findings take us beyond understanding VR as a space offering an embodied sense of immersion/interaction. The paper is a contribution to those interested in the study of Social VR. It will serve as a resource to any who wish to think productively about VR as a focal point for contemporary digital life.
Cyber security laws and safety in e-commerce in India

In today’s information technology age, the issue of cyber Security is a complicated and fascinating area of law. The phenomenal growth and development of e-commerce in India is astounding. However, with the rising dependence on internet commerce, the dangers of fraud and security and trust problems have become severe impediments. Creating robust legal and regulatory frameworks that meet the growing concerns about online fraud, data security, and intellectual property protection in both local and international business contexts is critical. The e-commerce sector, like any expanding business, confronts various obstacles, primarily due to an inadequate and inefficient legal and regulatory framework that fails to guarantee the rights and duties of all players engaged sufficiently. To protect user data, tackle cyber threats, and maintain customer trust, e-commerce enterprises must comply with legal regulations. In India, cybersecurity governance falls under the Information Technology Act of 2000, regulating e-commerce, electronic contracts, data protection, and cybercrimes. The imminent passage of the Personal Data Protection Bill, 2019, is expected after ongoing review. The Indian Penal Code addresses unauthorized access, hacking, identity theft, phishing, and computer virus dissemination. The Reserve Bank of India oversees online payment and financial security, mandating two-factor authentication, encryption, and secure payment channels. CERT-In coordinates national cybersecurity incidents, while electronic signatures and digital certificates hold legal recognition. Intellectual Property Laws regulate online violations of patents, copyrights, and trademarks. The Indian government also enforces cybersecurity standards for enterprises and organizations, covering IT infrastructure and incident response. Nonetheless, further steps must be taken to improve the efficiency of India’s cyber security regulations. This research study uses a doctrinal and analytical approach to examine India’s present Cyber Security Laws and Guidelines. It assesses their effectiveness in addressing legal concerns with Security, privacy, and data protection inside the country. It also evaluates the legal structure that governs the link between e-commerce and Cyber Laws in India. This research will provide a thorough overview of the present condition of cyber security regulations in India, setting the way for prospective reforms and progress in this critical area.
Attention-Based Model for Sentiment Analysis

With the evolution of deep learning, work has been carried out in various fields of NLP. With sentiment analysis, the reviews generated on various platforms can be easily analyzed and could be classified based on the polarity. In this work, IMDB movie dataset has been used and results are compared with the existing baseline techniques. A model is implemented using attention-based mechanism. BiLSTM is used in extracting the global features from the text and for overcoming the gradient issues. This model helps in understanding the contextual relationship in the words just like the human brain. With the use of attention model, the problems of long-term dependencies are resolved thus giving better performance. Evaluation parameters are computed based on the accuracy, precision, recall, and F-score. Results of this model prove to be better than the previous existing techniques.
Robust Few-Shot Graph Anomaly Detection via Graph Coarsening

Graph anomaly detection is an important aspect of anomaly detection, especially the graph-level anomaly detection, which can be used into biomolecular research or drug molecular detection and so on. It is necessary to identify anomalies within a limited sample size as the real-world scenarios always lack of anomalous graph labels. Graph-level anomaly detection with few samples mainly faces the following problems: 1) There are not enough samples for the model to effectively learn anomalies; 2) There is noise or irrelevant information in the graph, which makes it difficult to quickly learn the key structural information of the graph. To address these issues, we propose a Robust Meta-learning-based Graph Anomaly Detection Framework via Graph Coarsening (RCM-GAD). Specifically, we employ meta-learning to effectively extract and integrate abnormal information from similar networks. Then, we use the Graph Coarsening module to obtain the key structural information of the graph for anomaly detection. We apply this framework to detect anomalies at both the graph-level and subgraph-level. We conduct experiments on four datasets, demonstrating the superiority of our proposed framework, RCM-GAD, over state-of-the-art baselines in graph-level and subgraph-level anomaly detection tasks.
Implicit Offensive Speech Detection Based on Multi-feature Fusion

As social media platforms become increasingly strict in censorship of user posts, aggressive and insulting language has gradually shifted to implicit expressions using homophones, metaphors, and other forms of camouflage. This not only intensifies the satirical attacks of negative posts but also leads to a significant decrease in the effectiveness of models designed to detect offensive speech. This paper aims to achieve high accurate detection of implicit offensive speech. We conducted targeted investigations on the speech characteristics on Weibo, which is one of the largest social media platform in China. Based on the identified features of implicit offensive speech, including semantic, emotional, metaphorical, and fallacy characteristics, this paper constructs a BERT-based Multi-Task learning model named BMA (BERT-Mate-Ambiguity) to accurately detect the implicit offensive speech in the real world. Additionally, this paper establishes a dataset based on posts of Weibo that contain implicit offensive speech and conducts various comparative, robustness, and ablation experiments. The effectiveness of the model is demonstrated by comparing it to existing models that perform well in this field. Finally, this paper discusses some of its limitations and proposes future research work.
A Session Recommendation Model Based on Heterogeneous Graph Neural Network

In recent years, the amount of data generated by online platforms has grown exponentially, making it challenging for users to process large amounts of information. As a result, recommendation systems greatly improve user experience and retention by efficiently matching the content or products that users are interested in. However, most existing models only consider long-term preferences, ignoring the dynamic users’ preferences. To address this issue, this paper proposes a novel recommendation method that leverages heterogeneous information networks to capture both long-term and short-term preferences. Our model involves two components: a heterogeneous graph neural network that captures the long-term preferences of users and an attention mechanism that focuses on short-term preferences. The heterogeneous graph neural network extracts relevant information from the input sessions to learn feature representations, and the attention mechanism weights the features according to each user’s current interests. We evaluate our method on two public datasets, and our results show that our model outperforms existing approaches in terms of accuracy and robustness. In conclusion, our proposed method provides a useful framework for developing more efficient and proactive recommendation systems that can adapt to users’ ever-changing preferences. The incorporation of both long-term and short-term preferences allows our model to provide more accurate and personalized recommendations, improving the user experience.
User Feedback-Based Counterfactual Data Augmentation for Sequential Recommendation

The sequential recommendation is a prominent task that aims to provide accurate recommendations by leveraging users’ historical behavior. However, the challenge of data sparsity poses a significant obstacle in achieving effective sequential recommendations. In this paper, we propose a User Feedback-based Counterfactual data augmentation method for Sequential Recommendation (UFC4-SRec) to address this challenge. Our approach focuses on expanding the dataset for sequential recommendation tasks by employing counterfactual inference techniques. The UFC4-SRec method consists of two main components: a counterfactual generator and a recommender. The counterfactual generator is responsible for generating counterfactual examples based on users’ feedback. By incorporating users’ preferences for items, the generated counterfactual data are designed to be closer to their actual preferences. On the other hand, the recommender employs various sequential recommendation models to provide recommendation results. To guide the counterfactual generator, the recommender imitates reinforcement learning by computing reward values based on the quality of the generated data. To evaluate the effectiveness of our method, we conduct experiments on three real-world datasets. The experimental results demonstrate that our UFC4-SRec approach significantly improves the performance of sequential recommendation tasks. Moreover, it effectively addresses the data sparsity problem commonly encountered in sequential recommendations.
A Sparse Matrix Optimization Method for Graph Neural Networks Training

Graph neural networks (GNN) have shown great application potential in scientific research applications, biomedicine, and other fields, which exhibit superior feature representation capabilities for graph data with non-Euclidean structures. These capabilities are enabled efficiently by sparse matrix-matrix multiplication (SPMM) and sparse matrix-vector multiplication (SPMV) that operate on sparse matrix representations of graph structures. However, SpMM has the characteristics of high memory occupation and irregular memory access, which leads to low storage and computational efficiency. To address the above issues, this paper proposes a sparse matrix optimization method, including a sparse matrix format and a performance model. The format, namely BMCOO, divides the sparse matrix into multiple blocks and adopts the bitmap to compress the position information of non-zero elements in each block. This paper further designs an SpMV algorithm in BMCOO format on GPU. In addition, a multi-channel SpMV performance model is constructed to predict the execution time of SpMV by combining the sparse matrix scale and system architecture parameters. Then the performance model fine-tunes the graph partitioning of the GNN training process. Experiments on the SuiteSparse and the Open Graph Benchmark datasets verify the effectiveness and superiority of the proposed method.
Greco-Roman Antiquity in The Library for Reading journal of 1834-1840

The article attempts to identify the characteristics of classical antiquity representation in The Library for Reading journal in the 1830s, when it was the most popular Russian periodical, with its editor Josef Senkovsky being one of the famous writers and eminent scholars. The study draws on 8 literary publications and 24 research papers related to Greco-Roman antiquity. The analysis of literary works accepted for publication by Senkovsky shows that they were to lead the reader to reflect on three subjects: the features of intellectual pastime in classical antiquity and modernity; the transient nature of power of both an ambitious individual and the great empire; and the similarities, differences, and accomplishments of antiquity and modern times. The works of belles-lettres reflected the ambiguous perception of classical antiquity in the 1830s: on the one hand, viewing its heritage as a “golden standard”, and on the other – picturing it as a bygone and distant epoch through the motifs of catastrophe and decrepitude. The analyzed research publications on antiquity are distinguished by a striking variety of topics and the editor’ desire to make the papers clear and fascinating for readers by presenting classical antiquity not as a boring collection of dead forms, but as full of life and struggle. Moreover, the research articles shaped the image of the classical studies as a dynamic and developing scholarly discipline filled with discoveries, acute disputes, and unsolved riddles. All scholarly publications on Greco-Roman antiquity wrapped thoughtful and profound reflections targeting the highly educated reading elite in a popular narrative addressed to the general audience. The key questions they raised were the significance of classical texts as reliable sources for studying ancient history and understanding ancient Greek and Roman worldviews, as well as the relationships between antiquity and the Ancient East or antiquity and modernity.