Reviewerly/exHarmony huggingface dataset:
Examining perceptions of utilizing augmented reality in hybrid learning environment: instructors and students perspectives

Authors:
raw_author_name: Chinaza Solomon Ironsi
raw_affiliation_string: Foreign Languages and English Preparatory School, Mersin, Turkey

Abstract: Purpose This study aims to explore the perceptions of language learners and instructors on using augmented reality (AR) in a hybrid learning environment. Design/methodology/approach Mixed-method research design was used to elicit information from 62 participants on the study’s objective. Data were collected and analyzed to examine the participants’ views on using AR in language teaching in a hybrid environment. Findings This study unveils that while most studies acknowledge AR as an effective instructional delivery, students and instructors perceive some setbacks. Other novel insights provided by this study reveal necessities to consider before implementing AR in classroom settings. Research limitations/implications This study provides insights into the widely reported effectiveness of AR in the English language-teaching domain. This study suggests that considering the dispositions of learners and instructors toward digitally enhanced learning, using AR without good teaching practices and approaches may not yield expected learning outcomes. Originality/value This study advances scientific knowledge on the use of AR in hybrid learning models by providing empirical evidence to show the perceived effectiveness of AR. It further provides a robust understanding of the pedagogical implications of using AR in classroom settings drawing from diverse lenses. This helps ensure that educational settings that integrate AR consider the novel findings of this study before such implementation.
Gene selection for high dimensional biological datasets using hybrid island binary artificial bee colony with chaos game optimization

Authors:
raw_author_name: Maha Nssibi
raw_affiliation_string: Laboratory MARS, LR17ES05, ISITCom, University of Sousse, Sousse, Tunisia
raw_author_name: Ghaith Manita
raw_affiliation_string: Laboratory MARS, LR17ES05, ISITCom, University of Sousse, Sousse, Tunisia
raw_author_name: Amit Chhabra
raw_affiliation_string: Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India
raw_author_name: Seyedali Mirjalili
raw_affiliation_string: Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia
raw_author_name: Ouajdi Korbaa
raw_affiliation_string: ISITCom, University of Sousse, Sousse, Tunisia

Abstract: Abstract Microarray technology, as applied to the fields of bioinformatics, biotechnology, and bioengineering, has made remarkable progress in both the treatment and prediction of many biological problems. However, this technology presents a critical challenge due to the size of the numerous genes present in the high-dimensional biological datasets associated with an experiment, which leads to a curse of dimensionality on biological data. Such high dimensionality of real biological data sets not only increases memory requirements and training costs, but also reduces the ability of learning algorithms to generalise. Consequently, multiple feature selection (FS) methods have been proposed by researchers to choose the most significant and precise subset of classified genes from gene expression datasets while maintaining high classification accuracy. In this research work, a novel binary method called i BABC-CGO based on the island model of the artificial bee colony algorithm, combined with the chaos game optimization algorithm and SVM classifier, is suggested for FS problems using gene expression data. Due to the binary nature of FS problems, two distinct transfer functions are employed for converting the continuous search space into a binary one, thus improving the efficiency of the exploration and exploitation phases. The suggested strategy is tested on a variety of biological datasets with different scales and compared to popular metaheuristic-based, filter-based, and hybrid FS methods. Experimental results supplemented with the statistical measures, box plots, Wilcoxon tests, Friedman tests, and radar plots demonstrate that compared to prior methods, the proposed i BABC-CGO exhibit competitive performance in terms of classification accuracy, selection of the most relevant subset of genes, data variability, and convergence rate. The suggested method is also proven to identify unique sets of informative, relevant genes successfully with the highest overall average accuracy in 15 tested biological datasets. Additionally, the biological interpretations of the selected genes by the proposed method are also provided in our research work.
A Novel Method for 3D Object Detection in Open-Pit Mine Based on Hybrid Solid-State LiDAR Point Cloud

Authors:
raw_author_name: Cheng Li
raw_affiliation_string: CRRC Zhuzhou Institute Co., Ltd., Zhuzhou, Hunan 412001, China; Institute of Railway and Urban Rail Transit of Tongji University, Shanghai 201804, China
raw_author_name: Teng Long
raw_affiliation_string: CRRC Zhuzhou Institute Co., Ltd., Zhuzhou, Hunan 412001, China
raw_author_name: Peijie Li
raw_affiliation_string: CRRC Zhuzhou Institute Co., Ltd., Zhuzhou, Hunan 412001, China

Abstract: In recent years, the mining industry has encountered challenges, such as a shortage of human resources, an ongoing emphasis on safety enhancements, and increased ecological preservation requirements. Autonomous mining trucks have emerged as a novel solution to effectively address these issues within open-pit mining operations. To meet the demanding conditions of open-pit mines, characterized by intense vibrations and extreme temperature variations, hybrid solid-state LiDAR has emerged as the primary choice for perception sensors. Recognizing the distinct data structure and distribution disparities between point clouds obtained through nonrepetitive scanning methods of hybrid solid-state LiDAR and traditional mechanical LiDAR, this paper proposed an innovative LiDAR 3D object detection model, PointPillars-HSL (PointPillars-Hybrid Solid-state LiDAR). This approach harmonizes the unique characteristics of open-pit mining environments and hybrid solid-state LiDAR point clouds. It optimizes the model’s preprocessing methodology, augments the dimensionality of pillar features, fine-tunes the loss function, and employs transfer learning techniques to reduce the reliance on specific datasets. The result is the effective deployment of a 3D object detection algorithm customized for hybrid solid-state LiDAR within the specific operational framework of open-pit mining. This achievement has yielded a noteworthy overall vehicle recognition rate of 89.72%.
GASx: Explainable Artificial Intelligence For Detecting GPS Spoofing Attacks

Authors:
raw_author_name: Xin Tian
raw_author_name: Sixiao Wei
raw_author_name: Dan Shen
raw_author_name: Genshe Chen
raw_author_name: Khanh Pham
raw_author_name: Erik Blasch

Abstract: Unmanned Aerial Systems heavily depend on the Global Positioning System (GPS) for navigation. However, the GPS signals are subject to different types of threats including GPS spoofing attacks. While many machine learning methods have been successfully applied to detect spoofing attacks in these unmanned systems, the focus has mainly been on developing accurate prediction models, without delving into the reasons behind the predictions. We believe that understanding the underlying factors leading to a signal being classified as spoofed is crucial for gaining insights and effectively mitigating the effects of spoofing. In this paper, we propose a machine learning approach that incorporates explainable artificial intelligence techniques, specifically Shapley Additive Explanations (SHAP), to analyze why a signal is classified as a spoofed signal. Our approach utilizes a tree-based ensemble model, achieving a high F1 score of 0.956 for three different types of spoofing attacks. By leveraging SHAP, our analysis uncovers distinctive characteristics associated with each type of spoofing, providing valuable insights into the factors contributing to a signal being classified as spoofed.
Conceptualizing Discussions on the Dark Web: An Empirical Topic Modeling Approach

Authors:
raw_author_name: Randa Basheer
raw_affiliation_string: Faculty of Information Technology and Communications, Syrian Virtual University, Damascus, Syria
raw_author_name: Bassel Alkhatib
raw_affiliation_string: Faculty of Informatics Engineering, Damascus University, Damascus, Syria

Abstract: Social networks on the Internet have become a home that attracts all types of human thinking to exchange knowledge and ideas and share businesses. On the other hand, it has also become a source for researchers to analyze this knowledge and frame it in patterns that define types of thoughts circulating on these networks and representing the communities around them. In particular, some social networks on the Dark Web attract a special kind of thinking centered around the malicious and illegal activities disseminated on websites and marketplaces on the Dark Web. These networks involve discussions to exchange and discourse information, tips, and advice on performing such business. Studying social networks on the Dark Web is still in its infancy. In this paper, we present a methodology for analyzing the content of social networks on the Dark Web using topic modeling methods. We demonstrate the needed stages for the topic modeling process, beginning with data preprocessing and feature extraction to topic modeling algorithms. We utilize and discuss the following four topic models: LDA, CTM, PAM, and PTM. We discuss the following four topic coherence measures as evaluation metrics: UMass, UCI, CNPMI, and CV, demonstrating the selection of the best number of topics for each model according to the most coherent produced topics. Furthermore, we discuss the limitations, challenges, and future work. Our proposed approach highlights the ability to discover the latent thematic patterns in conversations and messages in the common language used in social networks on the Dark Web, constructing topics as groups of terms and their associations. This paper provides researchers with a leading methodology for analyzing thought patterns on the Dark Web.
Unraveling the crypto market: A journey into decentralized finance transaction network

Authors:
raw_author_name: Andry Alamsyah
raw_affiliation_string: School of Economic and Business, Telkom University, Indonesia

Abstract: The explosive growth of decentralized finance (DeFi) has revolutionized the accessibility and functionality of financial services. To gain valuable insights into this evolving ecosystem, analyzing transaction data using network analysis methodology proves to be highly effective. Network analysis allows us to uncover intricate relationships, patterns, and communities within the DeFi market by examining participant transactions. We model nodes group representing actors or wallet addresses, and edges represent transactions between wallet addresses. This study focuses on three prominent DeFi token-based Ethereum protocols: DAI, UNI, and WBTC. We analyzed 5,802,742 transaction data spanning from January 2022 to January 2023 of those three tokens. Using network topology metrics, we uncover market size, average transaction of each wallet address, market density, and reachability (diameter). We also detect the presence of transaction clusters and grouping quality using the modularity metric. At last, we employ centrality calculation to identify the most important wallet address and their role in the market. We gain insight by comparing those three tokens and reveal valuable insight into patterns and relationships, network dynamics, decentralized nature, and the presence of intermediaries in the token economy market. Our objective is to uncover hidden dynamics and trace asset flows, which can provide valuable information to market participants, regulators, and innovators seeking to optimize the DeFi infrastructure, ensure stability, and mitigate risks. Leveraging the power of network analysis offers the potential to shape the future of DeFi by enhancing efficiency, security, and accessibility, thereby fostering financial inclusion and empowering individuals and businesses globally.
Slide-block: End-to-end amplified security to improve DevOps resilience through pattern-based authentication

Authors:
raw_author_name: Gopalakrishnan Sriraman
raw_affiliation_string: Department of Computing Science and Engineering, VIT Bhopal University, Sehore, MP, India
raw_author_name: Shriram R
raw_affiliation_string: Department of Computing Science and Engineering, VIT Bhopal University, Sehore, MP, India

Abstract: DevOps represents the fusion of cultural philosophies, tools, and practices that rapidly enhance an organization's capacity to deploy services and applications. Cloud-based tools, a subset of DevOps services, facilitate collaboration between development and operations teams within an organization. However, persistent challenges such as inadequate security management, substantial leakage of sensitive data, and system/service unavailability pose significant threats to sustainability. We propose an end-to-end enhanced security framework to fortify DevOps resilience by implementing authentication and vulnerability management through the Slide-Block methodology. Our approach comprises four sequential processes: pattern-based authentication, tri-level access control, privacy-focused data storage, and vulnerability management and correction. Initially, we establish candidate legitimacy through pattern-based authentication using the Magnificent Chacha-Poly 1305 algorithm. Subsequently, we devise effective access policies using the Enhanced Deep Deterministic Policy Gradient (EDDPG) algorithm, employing tri-level access control based on trust value, attributes, and roles for optimal user and developer selection via the African Vulture Optimization Algorithm (AVOA). Moreover, we encrypt data in transit and at rest using Mcha-Poly 1305, considering sensitivity, and store it in a blockchain to enhance data privacy. Our approach incorporates a sliding window blockchain for secure data transmission and storage. Finally, we identify and address attack and application-based issues using the Tweak Naive Bayes (Tweak-NB) algorithm and Intruder Vulnerability Scanner (IVS). Our Slide-Block framework demonstrates superior performance in detection rate, authentication time, packet loss, security strengthening, communication overhead, and latency compared to existing models.
Enhancing machine learning-based sentiment analysis through feature extraction techniques

Authors:
raw_author_name: Noura A. Semary
raw_affiliation_string: Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom, Egypt
raw_author_name: Wesam Ahmed
raw_affiliation_string: Department of Information Technology, Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt; Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom, Egypt
raw_author_name: Khalid Amin
raw_affiliation_string: Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom, Egypt
raw_author_name: Paweł Pławiak
raw_affiliation_string: Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, Poland; Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland
raw_author_name: Mohamed Hammad
raw_affiliation_string: Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom, Egypt; EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

Abstract: A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model's performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.
Global domain adaptation attention with data-dependent regulator for scene segmentation

Authors:
raw_author_name: Fei Lu
raw_affiliation_string: Institute of Information Engineering, Nanyang Vocational College of Agriculture, Nanyang, China

Abstract: Most semantic segmentation works have obtained accurate segmentation results through exploring the contextual dependencies. However, there are several major limitations that need further investigation. For example, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. Moreover, local convolutions are commonly used in deep learning models to learn attention and capture local patterns in the data. These convolutions operate on a small neighborhood of the input, focusing on nearby information and disregarding global structural patterns. To address these concerns, we propose a Global Domain Adaptation Attention with Data-Dependent Regulator (GDAAR) method to explore the contextual dependencies. Specifically, to effectively capture both the global distribution information and local appearance details, we suggest using a stacked relation approach. This involves incorporating the feature node itself and its pairwise affinities with all other feature nodes within the network, arranged in raster scan order. By doing so, we can learn a global domain adaptation attention mechanism. Meanwhile, to improve the features similarity belonging to the same segment region while keeping the discriminative power of features belonging to different segments, we design a data-dependent regulator to adjust the global domain adaptation attention on the feature map during inference. Extensive ablation studies demonstrate that our GDAAR better captures the global distribution information for the contextual dependencies and achieves the state-of-the-art performance on several popular benchmarks.
Detecting unknown vulnerabilities in smart contracts using opcode sequences

Authors:
raw_author_name: Peiqiang Li
raw_affiliation_string: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, People's Republic of China
raw_author_name: Guojun Wang
raw_affiliation_string: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, People's Republic of China
raw_author_name: Xiaofei Xing
raw_affiliation_string: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, People's Republic of China
raw_author_name: Xiangbin Li
raw_affiliation_string: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, People's Republic of China

Abstract: Unknown vulnerabilities, also known as zero-day vulnerabilities, are vulnerabilities in software, systems, or networks that have not yet been publicly disclosed or fixed. If these vulnerabilities are ever discovered by hackers, intentionally or unintentionally, they pose a major threat to network security. This is particularly true in the blockchain field, as smart contracts hold a lot of money, and if they are discovered and exploited by hackers, the financial losses to users will be even greater. However, the current research on smart contract vulnerabilities mainly focuses on known vulnerabilities, and the research on unknown vulnerabilities has been limited. Based on this, we introduce a machine learning-based method for detecting unknown vulnerabilities in smart contracts. First, the method obtains the opcode sequences executed by smart contract transactions in the EVM by instrumenting Geth and replaying the Ethereum transactions. Next, we employ an n-gram model and a vector weight penalty mechanism to extract the opcode sequence features. We then use machine learning algorithms to detect unknown vulnerabilities based on the similarity principle. Finally, we test the effectiveness of our method with four machine learning models: the K-Nearest Neighbor algorithm (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The SVM model performs best at detecting unknown vulnerabilities, with an accuracy of 96%, a precision of 91%, a recall of 100%, and an F1-score of 95%. We also discuss the benefits of the method: timely detection of attacks due to unknown vulnerabilities, thus reducing user losses.
Hybrid Prompt Recommendation Explanation Generation combined with Graph Encoder

Authors:
raw_author_name: Tianhao Wang
raw_affiliation_string: College of Information Science and Engineering, Hunan Normal University, 36 Lushan Road, Changsha, 410081, Hunan, China
raw_author_name: Sheng Wu
raw_affiliation_string: School of Computer and Communication Engineering, Changsha University of Science and Technology, 45 Chiling Road, Changsha, 410076, Hunan, China
raw_author_name: Lidan Kuang
raw_affiliation_string: School of Computer and Communication Engineering, Changsha University of Science and Technology, 45 Chiling Road, Changsha, 410076, Hunan, China
raw_author_name: You Wang
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou, 866 Yuhangtang Road, Hangzhou, 310027, ZheJiang, China
raw_author_name: Jin Zhang
raw_affiliation_string: School of Computer and Communication Engineering, Changsha University of Science and Technology, 45 Chiling Road, Changsha, 410076, Hunan, China

Abstract: Abstract Recommendation systems have been effectively utilized in various fields, but their internal decision-making methods are still largely unknown. This opaque decision-making method can greatly affect users’ trust in the recommendation system. Therefore, finding a way to explain the reasons for model decisions has become an urgent task. Previous studies often used LSTM and other models to generate recommendation explanations and explain the reasons for recommendations in text form. However, traditional methods cannot effectively use the ID information of users and items, and the text generated is highly repetitive. To solve this problem, this paper uses the method of prompt learning combined with a graph encoder to design a recommendation explanation generation model. In order to narrow the semantic gap between the ID information of users and items and natural language and capture high-level interaction information, this paper designs a graph encoder based on user similarity to learn the interactive semantic information of user and item IDs, and to construct a continuous prompt. Then, the discrete prompt composed of discrete features of users and items is combined with the continuous prompt to construct a hybrid prompt to input into the pre-trained model to generate the recommended explanation. This paper experiments on three publicly available datasets and compares them with several state-of-the-art methods to demonstrate the personalization and text quality of the generated explanations.