Reviewerly/exHarmony huggingface dataset:
A federated learning approach to QoS forecasting in cellular vehicular communications: Approaches and empirical evidence

Authors:
raw_author_name: Eirini Liotou
raw_affiliation_string: Institute of Communications and Computer Systems (ICCS), Greece
raw_author_name: Konstantinos V. Katsaros
raw_affiliation_string: Institute of Communications and Computer Systems (ICCS), Greece
raw_author_name: Amr Rizk
raw_affiliation_string: University of Duisburg–Essen, Germany

Abstract: QoS forecasting for cellular vehicular communications allows cooperative, connected and automated mobility applications to tailor their behavior to the expected communication conditions on the road. In a nutshell, vehicles may, for example, execute cooperative maneuvers if the communication quality of service is only above a certain quantitative level whereas if not they revert to the individual autonomous mode. In this paper, we propose and show empirical methods for estimating packet-based QoS metrics obtained from 5G network measurements with a direct application to vehicular applications. As many distributed vehicular applications possess strict QoS requirements, we focus here on bounding packet-based statistical QoS quantiles, specifically for latency and loss. Our approach is based on training regression neural networks in a federated learning fashion and show that it can obtain predictions on par with centralized training without the vehicles needing to transmit raw measurement data. In contrast to QoS prediction using physical layer information, We briefly discuss the embedding of such much simpler application-level service within the 5G architecture. We also validate our approach through recovering classical closed-form delay quantiles that are obtained from analytical models of simple queueing systems. We show that our approach goes beyond these simple models in that it provides quantile estimates for the complex scenario of cellular vehicle communications and under different application traffic patterns including empirical data traffic traces as well as 5G testbed measurements.
Group Behavior Recognition Algorithm for Basketball Video Based on Hidden Tag Mining Strategy under Gamification Teaching and Learning

Authors:
raw_author_name: Chao Hong
raw_affiliation_string: Ministry of Sports, Xiamen Institute of Technology, Xiamen, 361021, China

Abstract: The rapid development of Artificial Intelligence has facilitated the development of multimedia teaching and learning in the field of education, as well as providing intelligent methods for gamified teaching methods in the field of basketball. Currently, research on recognizing basketball video behavior primarily focuses on group behavior recognition methods for key individuals. However, there is currently no group behavior recognition method that can effectively distinguish features that reflect different events for temporal interactive behaviors such as dunks and steals in videos, as well as situations where key players are obstructed. The paper suggests a group behavior identification algorithm for basketball game videos based on the hidden tag mining technique in order to perform intelligent analysis in the fields of supplemental training and tactical analysis of gamified instruction. And this algorithm is integrated with the fusion of individual features to create the basketball video's group behavior identification algorithm with hidden label mining approach. According to the study's findings, the distribution of motion pattern features is more agglomerated and regular when the merging threshold is 0.6 and the best hyperparameter is 3. After the study applied the crucial individual feature extraction module, the overall group behavior identification accuracy increased by 6.44%. The group behavior recognition algorithm for basketball videos that incorporates individual features and hidden label mining techniques outperforms the most recent patio-temporal domain-based group behavior recognition algorithm by 15.56% when compared to the accuracy of commonly used algorithms. In summary, the study proposes a basketball video group behavior recognition algorithm with better performance. This algorithm achieves group behavior recognition in basketball game videos, promotes auxiliary training for students in gamified teaching mode, and provides a practical and feasible approach for intelligent analysis of other sports events. It also provides technical support for coaches and referees.
Revisiting code smell severity prioritization using learning to rank techniques

Authors:
raw_author_name: Lei Liu
raw_affiliation_string: Key Laboratory of Physical Electronics and Devices, Ministry of Education, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
raw_author_name: Guancheng Lin
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, Hubei 430072, China
raw_author_name: Lin Zhu
raw_affiliation_string: School of Computer, Wuhan Qingchuan University, Wuhan, Hubei 430204, China
raw_author_name: Zhen Yang
raw_affiliation_string: School of Computer Science and Technology, Shandong University, Qingdao, Shandong 266237, China
raw_author_name: Peilin Song
raw_affiliation_string: Key Laboratory of Physical Electronics and Devices, Ministry of Education, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
raw_author_name: Xin Wang
raw_affiliation_string: School of Computer Science, Wuhan University, Wuhan, Hubei 430072, China
raw_author_name: Wenhua Hu
raw_affiliation_string: School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, Hubei 430070, China

Abstract: Code Smell Severity Prioritization (CSSP) is crucial in helping software developers minimize software maintenance costs and enhance software quality, particularly when faced with limited refactoring resources. Traditional code smell prioritization methods rely heavily on manual and semi-automatic approaches based on developer experience, often demanding considerable time and effort from experienced experts. Leveraging automated machine learning techniques can effectively overcome these limitations. However, most existing machine learning-based CSSP works have only considered limited pointwise Learning To Rank (LTR) algorithms and have used inappropriate metrics (e.g., Accuracy, Spearman, and MAE) to assess the performance of models. To address these limitations, we make a comprehensive comparison of 41 pointwise, 4 pairwise, and 4 listwise LTR algorithms for CSSP on four code smell severity datasets. Furthermore, we propose the adoption of Severity@20% and Cumulative Lift Chart (CLC) as the primary evaluation metrics to assess CSSP models more effectively. The results show that: (1) The ordinal Bagging (O-Bagging) algorithm demonstrates the highest performance for CSSP, achieving superior results in Severity@20% and CLC. (2) The ordinal classification method can help the top-performing base classification algorithms Bagging and XGBoost achieve better performance for CSSP tasks. (3) A higher (lower) Accuracy, higher (lower) Spearman, and lower (higher) MAE do not reliably indicate better (worse) performance for CSSP. This further underscores that Accuracy, Spearman, and MAE are unsuitable metrics for evaluating CSSP models’ effectiveness. To summarize, our study suggest that developers employ the O-Bagging algorithm for CSSP, with Severity@20% and CLC serving as the primary evaluation metrics.
Deep neural network-based robust hologram watermarking using guided attack module

Authors:
raw_author_name: EunSeong Lee
raw_affiliation_string: Department of Computer Engineering, Kwangwoon University, Kwangwoon-ro 20, Seoul 101897, Republic of Korea
raw_author_name: Donggyu Sim
raw_affiliation_string: Department of Computer Engineering, Kwangwoon University, Kwangwoon-ro 20, Seoul 101897, Republic of Korea
raw_author_name: Young-Ho Seo
raw_affiliation_string: Department of Electronic Materials Engineering, Kwangwoon University, Kwangwoon-ro 20, Seoul 101897, Republic of Korea

Abstract: A digital hologram (DH) is an ultra-high-value video content that includes three-dimensional information in two-dimensional data. Therefore, for the distribution of this content, the intellectual property rights must be protected. This paper proposes robust hologram watermarking based on deep neural networks using guided attack modules and attack rate control techniques. The proposed method robustly embeds the watermark into the original hologram to cope with various attacks while keeping the invisibility that the watermarked hologram is similar to the original hologram. Previous work considered PSNR during training. However, hologram exists in the complex domain. Thus SNR for complex numbers should be considered to confirm the reconstruction results.. In addition, we propose an enhanced network with increased depth to improve performance compared with previous work. Experiments show that the proposed method outperforms the previous work regarding robustness and invisibility against various attacks, such as Crop Out, Salt & Pepper Noise, and Rotation. Therefore, this study is expected to be widely used in various fields, such as digital image authentication, copyright protection, and data security.
Defense against adversarial attacks based on color space transformation

Authors:
raw_author_name: Haoyu Wang
raw_affiliation_string: School of Cyberspace Security, Beijing University of Posts and Telecommunications, China
raw_author_name: Chunhua Wu
raw_affiliation_string: School of Cyberspace Security, Beijing University of Posts and Telecommunications, China
raw_author_name: Kangfeng Zheng
raw_affiliation_string: School of Cyberspace Security, Beijing University of Posts and Telecommunications, China

Abstract: Deep Learning algorithms have achieved state-of-the-art performance in various important tasks. However, recent studies have found that an elaborate perturbation may cause a network to misclassify, which is known as an adversarial attack. Based on current research, it is suggested that adversarial examples cannot be eliminated completely. Consequently, it is always possible to determine an attack that is effective against a defense model. We render existing adversarial examples invalid by altering the classification boundaries. Meanwhile, for valid adversarial examples generated against the defense model, the adversarial perturbations are increased so that they can be distinguished by the human eye. This paper proposes a method for implementing the abovementioned concepts through color space transformation. Experiments on CIFAR-10, CIFAR-100, and Mini-ImageNet demonstrate the effectiveness and versatility of our defense method. To the best of our knowledge, this is the first defense model based on the amplification of adversarial perturbations.
FCNN: Simple neural networks for complex code tasks

Authors:
raw_author_name: Xuekai Sun
raw_affiliation_string: Information Engineering University, China
raw_author_name: Tieming Liu
raw_affiliation_string: Information Engineering University, China
raw_author_name: Chunling Liu
raw_affiliation_string: Information Engineering University, China
raw_author_name: Weiyu Dong
raw_affiliation_string: Information Engineering University, China

Abstract: Program analysis using deep learning has become a focus of research, and representing code as model input is a major challenge. While abstract syntax trees (ASTs) have proven effective, using them directly as model input introduces issues of long-term dependency. Approaches based on AST paths objectively alleviate the vanishing gradient problem caused by large-scale syntax trees, but still face limitations. This paper introduces a novel approach to code analysis using fully connected neural networks (FCNN). We propose a new context structure that cleverly preserves the up-down relationships between path nodes. Simultaneously, it employs an encoding method based on node embeddings, mitigating model sparsity. Despite its simplicity, FCNN demonstrates versatility in handling various tasks associated with code analysis. Our work underscores the importance of improvements and innovations in code representation compared to using more advanced and complex deep learning models in the field of code analysis. Evaluation on two common code analysis tasks, namely code classification and code similarity detection, validates the effectiveness of the proposed approach. In code classification, FCNN achieves an F1 score of 0.45, surpassing all comparison baselines. In code similarity detection, FCNN attains an F1 score of 0.91, outperforming RtvNN and CDLH by 35.82% and 10.98%, respectively.
Enhancing multi-modal fusion in visual dialog via sample debiasing and feature interaction

Authors:
raw_author_name: Chenyu Lu
raw_affiliation_string: School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China
raw_author_name: Jun Yin
raw_affiliation_string: College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China
raw_author_name: Hao Yang
raw_affiliation_string: 2012 Labs, Huawei Technologies CO., LTD, China
raw_author_name: Shiliang Sun
raw_affiliation_string: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China

Abstract: Visual dialog aims to accomplish multiple rounds of dialog by fusing information extracted from images, captions, and previous question-answer pairs. As a vision-language task, visual dialog encounters challenges related to language bias and vision bias. These biases create an imbalance in multi-modal fusion, resulting in shortcut learning and significantly compromising the model’s robustness. Moreover, existing multi-modal fusion methods in visual dialog exhibit a low data interaction frequency, leading to insufficient fusion. To overcome the balance and sufficiency issues in multi-modal fusion, we propose a novel Parallel Attention Fusion visual dialog model with Counterfactual Sample debiasing (CS-PAF). Specifically, CS-PAF consists of two core ingredients: (i) a counterfactual sample generation module for model debiasing; and (ii) a parallel attention fusion network that enhances sufficiency in multi-modal data interaction. Notably, in contrast to other debiasing methods, our counterfactual sample generation applies contrastive learning to circumvent the high cost of manual annotations and ensure seamless integration with other models. Extensive comparisons with state-of-the-art approaches, along with comprehensive ablation and transferability studies across multiple datasets, substantiate the superiority and effectiveness of our CS-PAF. Our implement code is available at https://github.com/chenyulu2000/cspaf.
Audio Steganalysis Using Fractal Dimension and Convolutional Neural Network (CNN) Model

Authors:
raw_author_name: Otasowie Owolafe
raw_affiliation_string: Federal University of Technology, Akure, Nigeria
raw_author_name: Aderonke F. Thompson
raw_affiliation_string: Federal University of Technology, Akure, Nigeria

Abstract: The rate at which secret messages are being transmitted through various digital signal media is alarming; these operations are done in an unsuspicious manner and users transmit these messages without knowledge of the embedded secret messages. Audio steganalysis deals with detecting the presence of secret messages in audio messages. Some of the existing steganalysis methods are laden with having prior knowledge of the steganography methods adopted in embedding the secret message in an audio signal, which reduces the detection efficiency. Consequently, this research developed a Higuchi-based audio steganalysis method that detects secret messages without having prior knowledge of the embedding techniques used. The algorithm reduces the fractal dimension of the audio signal to extract relevant features, while convolutional neural network was used as classifier. The research records high accuracy (96%) when compared with previous research. The accuracy of the developed system shows its effectiveness in detecting embedded messages without prior knowledge of the deployed steganography method.
Security, Privacy, and Technical Challenges in a Blockchain-Internet of Things-Based Environment

Authors:
raw_author_name: Amit Kumar Tyagi
raw_affiliation_string: National Institute of Fashion Technology, New Delhi, India
raw_author_name: Meghna Manoj Nair
raw_affiliation_string: Tandon School of Engineering, New York University, USA
raw_author_name: Khushboo Tripathi
raw_affiliation_string: Amity University, Gurugram, India

Abstract: Blockchain and internet of things (IoT) is a combination that has massive potential to bring about revolutionary changes in various useful applications/ industries by enabling secure and transparent data exchange, decentralized control, and improved efficiency. However, this convergence also requires significant security, privacy, and technical challenges that must be addressed for successful implementation and widespread adoption. This chapter explains the security, privacy, and technical challenges that arise in a blockchain-IoT-based environment. Firstly, it examines the security issues related to the distributed nature of blockchain networks and the vulnerabilities that can be exploited in IoT devices and communication channels. Further, the authors discuss the importance of cryptographic techniques, secure key management, and access control mechanisms to protect data integrity and prevent unauthorized access.
Transformative Effects of Blockchain and IoT in Shaping Industry 5.0 Environments

Authors:
raw_author_name: Shabnam Kumari
raw_affiliation_string: SRM Institute of Science and Technology, Chennai, India
raw_author_name: Mahmoud Ragab
raw_affiliation_string: King Abdulaziz University, Saudi Arabia

Abstract: In the era of Industry 5.0, characterized by the convergence of physical and digital worlds, the integration of blockchain technology and the internet of things (IoT) has emerged as a pivotal paradigm shift. This chapter explores the perspectives, issues, and challenges associated with the amalgamation of Blockchain and IoT to foster a transformative environment for industries. This chapter begins by providing an overview of Industry 5.0 and its key principles, emphasizing the need for robust and secure data handling in this context. It then explains the application of blockchain as a distributed ledger technology to enhance the IoT ecosystem by enabling trust, security, and transparency in data transactions. Various use cases and real-world applications of this fusion (of IoTs and blockchain) are discussed, ranging from supply chain management to smart cities and healthcare. Despite its promising potential, the integration of blockchain and IoT also presents several critical issues and challenges.
Navigating Cloud Security Risks, Threats, and Solutions for Seamless Business Logistics

Authors:
raw_author_name: Shalbani Das
raw_affiliation_string: Amity University, Kolkata, India

Abstract: This chapter explores cloud security challenges faced by businesses adopting cloud technologies for logistics operations. It provides a roadmap to strengthen cloud security, safeguard digital assets, and foster solid business-CSP relationships. The chapter covers hazards, solutions (e.g., threat intelligence platforms, MFA, intrusion detection, encryption), shared responsibility with CSPs, and selecting reliable providers. Practical guidelines for a comprehensive cloud security strategy are offered, including risk assessments, access controls, incident response, and employee training. Understanding these aspects helps companies proactively secure their data and ensure seamless logistics in the cloud. By gaining a deep understanding of the risks, threats, and solutions associated with cloud security in business logistics, companies can proactively safeguard their critical data and ensure the seamless and secure operation of their logistical processes in the cloud.
Blockchain Technology in Cloud Security

Authors:
raw_author_name: Manivannan Karunakaran
raw_affiliation_string: Jain University (Deemed), India
raw_author_name: Kiran Bellam
raw_affiliation_string: Prairie View A&M University, USA

Abstract: As cloud computing becomes ubiquitous, the need for robust security measures intensifies. Traditional centralized access control systems in cloud environments pose vulnerabilities such as single points of failure, transparency deficits, and privacy concerns. This chapter explores the integration of blockchain technology as a transformative approach to enhancing cloud security. Blockchain, renowned for its decentralized ledger, immutability, and cryptographic security, offers innovative solutions to these challenges. The chapter discusses the motivation behind adopting decentralized access control, the fundamentals of blockchain, and its role in identity management. Real-world use cases demonstrate the practical application of blockchain in cloud security. Challenges, including scalability and regulatory compliance, are addressed. This chapter provides a comprehensive introduction to decentralized access control in cloud security, emphasizing its potential to revolutionize the field and contribute to the development of secure and resilient cloud systems.
Enhanced Security in Blockchain-Based Cyber Physical Systems

Authors:
raw_author_name: N. Ambika
raw_affiliation_string: St. Francis College, India

Abstract: Consortium blockchain provides pooled trust for the proposed method. It uses a unique certificateless authentication method based on the multi-signature scheme to guarantee safety. Software, and physical components comprise the cyber-physical system. Each piece functions on various time-based and three-dimensional stages and constantly interacts with other members. The framework eliminates the centralized trust model, i.e., the dependence on a single PKI certificate authority (CA) for public keys, works over an associate consortium, and uses smart-edge computation. Different industry partners cooperatively confirm both the gadget's character and information. It prevents disasters by reducing the impact of potentially malevolent associates. It agrees to a novel contract without requiring manifold authorisations, eliminating the need for a waiting period after each block addition. It adjusts, putting away information at the conveyed hash table. The suggestion adds more reliability to the transaction by 4.17%. The suggestion conserves energy by 12.5%.
Cloud Security Risks, Threats, and Solutions for Business Logistics

Authors:
raw_author_name: Shrikant Tiwari
raw_affiliation_string: Galgotias University, India
raw_author_name: Ramesh S. Wadawadagi
raw_affiliation_string: Nagarjuna College of Engineering, India
raw_author_name: Virendra Kumar Verma
raw_affiliation_string: Institute of Engineering and Rural Technology (IERT), Prayagraj, India

Abstract: Cloud computing has revolutionized the landscape of modern business logistics by offering scalable and cost-effective solutions for data storage, processing, and application deployment. However, with this newfound convenience comes a plethora of security challenges that businesses must address to protect their valuable assets and sensitive information. This chapter aims to provide a comprehensive overview of the security risks and threats associated with cloud adoption in the logistics industry, along with effective solutions to mitigate them by presenting an in-depth analysis of the primary security risks that businesses may encounter when leveraging cloud-based logistics solutions. Additionally, it discusses the risks posed by shared infrastructure, third-party integrations, and data jurisdiction concerns. Moreover, the chapter highlights the growing role of emerging technologies in bolstering cloud security measures. These technologies offer sophisticated threat detection and proactive response capabilities, enabling logistics companies to stay ahead of evolving cyber threats.