Reviewerly/exHarmony huggingface dataset:
FCNN: Simple neural networks for complex code tasks

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

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

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

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

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

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

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

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

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.
Revisiting Fully Homomorphic Encryption Schemes for Privacy-Preserving Computing

Homomorphic encryption (HME) is a sophisticated encryption technique that allows computations on encrypted data to be done without the requirement for decryption. This trait makes HME, appropriate for safe computation in scenarios involving sensitive data and also in cloud computing. The data is encrypted using a public key and the calculation is conducted on the encrypted data. The computed result is then decrypted with a private key to acquire the final output. It protects data while allowing complicated computations to be done on the encrypted data, resulting in a secure and efficient approach to analyse sensitive information. The ability of HME to do computations on encrypted data without decryption makes it a valuable tool for achieving privacy. This chapter is intended to give a clear idea about the various fully HME schemes present in the literature, as well as analysing and comparing the results of each of these schemes. The authors also provide applications and open-source tools of HME schemes, along with how HME can be used to establish and preserve privacy in various forms.
Safeguarding Privacy Through Federated Machine Learning Techniques

The chapter thoroughly addresses ML privacy threats like risks, attacks, and leaks. It explores the methods of differential privacy, homomorphic encryption, and SMPC. Federated learning is detailed, covering concepts, benefits, techniques (averaging, aggregation). Advancements include transfer learning, differential privacy, edge device use. Real cases show privacy's value in healthcare, finance, IoT. The conclusion touches on trends, regulations, privacy-utility balance. The chapter aims to overview privacy-preserving and federated ML, stressing their role in data security with insights for researchers, practitioners, policymakers for privacy-conscious ML. Valuable insights are provided for researchers, practitioners, and policymakers aiming for a privacy-conscious future in machine learning.
Role of Blockchain Technology in Protecting a Cloud Architecture

Cloud computing has revolutionized the way organizations manage and deliver services, offering unparalleled scalability and cost-efficiency. However, the increasing adoption of cloud-based solutions has also brought forth a myriad of security challenges, ranging from data breaches to unauthorized access and tampering. In recent years, blockchain technology has emerged as a promising solution to address some of these security concerns. This chapter presents an in-depth exploration of the application of blockchain technology in cloud security. It begins by providing an overview of both cloud computing and blockchain, emphasizing their individual strengths and weaknesses in safeguarding data and transactions. Subsequently, it delves into the various security challenges faced in cloud environments and how traditional security measures often fall short.
Blockchain Security in Edge Computing

In the past decade, edge computing and blockchain technology have been used in many applications with rapid growth. The chapter explores the intersection of blockchain technology and edge computing and investigates the security issues and solutions in this emerging domain. Edge computing refers to “the decentralized processing and storage of data at the network edge, closer to the data sources and end-users. It offers reduced latency, improved data privacy, and enhanced real-time decision-making capabilities.” However, it also introduces new security challenges due to the distributed and resource-constrained nature of edge devices. The integration of blockchain technology with edge computing holds promise in addressing these security issues. Blockchain can provide a decentralized and tamper-resistant ledger for recording and verifying transactions, ensuring data integrity, and establishing trust among edge devices and stakeholders. This chapter explores several use cases where blockchain and edge computing can synergistically enhance security.
Introduction to Cloud Computing and Cloud Services

Cloud computing and cloud services have revolutionized the way organizations access and utilize computing resources. This chapter provides an overview of cloud computing, its evolution, and the advantages it offers. It discusses the different types of cloud services and the importance of service-level agreements. The chapter also explores cloud deployment models and their considerations. It highlights the impact of cloud computing on various industries and the challenges of migration. Cloud computing and cloud services have revolutionized the IT landscape. They provide organizations with scalable, flexible, and cost-effective solutions that drive innovation and enable businesses to focus on their core competencies. By carefully evaluating their needs and requirements, considering factors such as security, compliance, and customization, organizations can select the most suitable cloud service type and deployment model. Embracing cloud technologies empowers businesses to unlock new opportunities, increase efficiency, and stay competitive in the rapidly evolving digital world.
A Systematic Review With Recommendations on Intelligent Systems in Cognitive Healthcare

Today's doctors and patients rely on online platforms to express opinions on health matters. Public health importance, especially after the COVID-19 pandemic in both developing and developed countries are focusing on building sustainable, resilient health systems and implementing policies to address prevailing healthcare challenges. Adoption of latest trends and technologies leads to the processing of extensive bigdata by heavy statistical evaluations through machine learning and deep learning techniques to predict prominent semantic patterns. The objective of this chapter is to spotlight on cognitive analysis to extract opinions expressed by users. Authors emphasize narrowing down the new potentials in the field of sentiment analysis through cognition and understanding variations in semantic orientation. The chapter explores new methods and procedures that benefit practitioners and researchers with respect to sentiment polarity using cognitive analytics to address the improvements in healthcare industry.
Key Insights into Developing Qualitative Concept Elicitation Work for Outcome Measures with Children and Young People

Abstract Qualitative concept elicitation can develop meaningful patient-reported outcome measures for children and young people; however, the methods used for concept elicitation are often underreported for this population. This paper provides in-depth insight into the methods used for concept elicitation with children and young people, with a focus on key stages of concept elicitation that are challenging or unique to doing this research with children. Drawing on our experiences of developing wellbeing measures for children and young people aged 6–15 years, we detail the processes followed in our qualitative concept elicitation work, covering issues related to sampling and recruitment, encouraging informed assent and freedom over children and young people’s involvement in concept elicitation, and the use of creative and participatory methods to develop measure items. We provide reflections on the approaches taken to navigate challenging aspects of concept elicitation with children and young people. Our reflections suggest that using existing links and online recruitment methods can help to navigate organisational gatekeepers, and using appropriate processes to develop study information and obtain informed assent can ensure that research is inclusive and that children have the freedom to decide whether to be involved. Our adaptation of a creative and participatory activity to generate concepts for measure items suggests that such approaches can be engaging and may help to give children greater control over their participation. In detailing our methods, we hope to have developed a useful resource for other researchers, while highlighting the value of transparent reporting in this area.
Connection between single-layer quantum approximate optimization algorithm interferometry and thermal distribution sampling

The quantum approximate optimization algorithm (QAOA) was originally proposed to find approximate solutions to combinatorial optimization problems on quantum computers. However, the algorithm has also attracted interest for sampling purposes since it was theoretically demonstrated under reasonable complexity assumptions that one layer of the algorithm already engineers a probability distribution beyond what can be simulated by classical computers. In this regard, a recent study has also shown that, in universal Ising models, this global probability distribution resembles pure but thermal-like distributions at a temperature that depends on the internal correlations of the spin model. In this work, through an interferometric interpretation of the algorithm, we extend the theoretical derivation of the amplitudes of the eigenstates and the Boltzmann distributions generated by a single-layer QAOA. We also review the implications of this behavior from practical and fundamental perspectives.
Quantifying and characterizing clones of self-admitted technical debt in build systems

Self-Admitted Technical Debt (SATD) annotates development decisions that intentionally exchange long-term software artifact quality for short-term goals. Recent work explores the existence of SATD clones (duplicate or near duplicate SATD comments) in source code. Cloning of SATD in build systems (e.g., CMake and Maven) may propagate suboptimal design choices, threatening qualities of the build system that stakeholders rely upon (e.g., maintainability, reliability, repeatability). Hence, we conduct a large-scale study on 50,608 SATD comments extracted from Autotools, CMake, Maven, and Ant build systems to investigate the prevalence of SATD clones and to characterize their incidences. We observe that: (i) prior work suggests that 41-65% of SATD comments in source code are clones, but in our studied build system context, the rates range from 62% to 95%, suggesting that SATD clones are a more prevalent phenomenon in build systems than in source code; (ii) statements surrounding SATD clones are highly similar, with 76% of occurrences having similarity scores greater than 0.8; (iii) a quarter of SATD clones are introduced by the author of the original SATD statements; and (iv) among the most commonly cloned SATD comments, external factors (e.g., platform and tool configuration) are the most frequent locations, limitations in tools and libraries are the most frequent causes, and developers often copy SATD comments that describe issues to be fixed later. Our work presents the first step toward systematically understanding SATD clones in build systems and opens up avenues for future work, such as distinguishing different SATD clone behavior, as well as designing an automated recommendation system for repaying SATD effectively based on resolved clones.
Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature's significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system.