Reviewerly/exHarmony huggingface dataset:
Revisiting Fully Homomorphic Encryption Schemes for Privacy-Preserving Computing

Authors:
raw_author_name: Aswani Kumar Aswani Cherukuri
raw_affiliation_string: Vellore Institute of Technology, India
raw_author_name: Firuz Kamalov
raw_affiliation_string: Canadian University, Dubai, UAE

Abstract: 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

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

Abstract: 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

Authors:
raw_author_name: Virendra Kumar Verma
raw_affiliation_string: Institute of Engineering and Rural Technology (IERT), Prayagraj, India
raw_author_name: Shrikant Tiwari
raw_affiliation_string: School of Computing Science and Engineering (SCSE), Galgotias University, India
raw_author_name: Bireshwar Dass Mazumdar
raw_affiliation_string: School of Computer Science Engineering and Technology(SCSET), Bennett University, India

Abstract: 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

Authors:
raw_author_name: Amit Kumar Tyagi
raw_affiliation_string: National Institute of Fashion Technology, New Delhi, India
raw_author_name: Shrikant Tiwari
raw_affiliation_string: Galgotias University, Greater Noida, India

Abstract: 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

Authors:
raw_author_name: Mani Deepak Choudhry
raw_affiliation_string: KGiSL Institute of Technology, India
raw_author_name: M. Sundarrajan
raw_affiliation_string: SRM Institute of Science and Technology, India
raw_author_name: M. Parimala Devi
raw_affiliation_string: Velalar College of Engineering and Technology, India
raw_author_name: Akshya Jothi
raw_affiliation_string: SRM Institute of Science and Technology, India

Abstract: 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

Authors:
raw_author_name: K. A. Venkatesh
raw_affiliation_string: Alliance University, India

Abstract: 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.
Connection between single-layer quantum approximate optimization algorithm interferometry and thermal distribution sampling

Authors:
raw_author_name: Pablo Díez-Valle
raw_affiliation_string: Instituto de Física Fundamental IFF-CSIC, Madrid, Spain
raw_author_name: Diego Porras
raw_affiliation_string: Instituto de Física Fundamental IFF-CSIC, Madrid, Spain
raw_author_name: Juan José García-Ripoll
raw_affiliation_string: Instituto de Física Fundamental IFF-CSIC, Madrid, Spain

Abstract: 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

Authors:
raw_author_name: Xiao, Tao
raw_affiliation_string: Nara Institute of Science and Technology, Ikoma, Japan
raw_author_name: Wang, Dong
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, China
raw_author_name: Hata, Hideaki
raw_affiliation_string: Shinshu University, Matsumoto, Japan
raw_author_name: McIntosh, Shane
raw_affiliation_string: University of Waterloo, Waterloo, Canada
raw_author_name: Matsumoto, Kenichi
raw_affiliation_string: Nara Institute of Science and Technology, Ikoma, Japan

Abstract: 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

Authors:
raw_author_name: Sadia Sultana Chowa
raw_affiliation_string: Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
raw_author_name: Sami Azam
raw_affiliation_string: Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
raw_author_name: Sidratul Montaha
raw_affiliation_string: Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
raw_author_name: Md Rahad Islam Bhuiyan
raw_affiliation_string: Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
raw_author_name: Mirjam Jonkman
raw_affiliation_string: Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia

Abstract: 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.
Automated Text Analysis of Organic Chemistry Students’ Written Hypotheses

Authors:
raw_author_name: Amber J. Dood
raw_affiliation_string: Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
raw_author_name: Field M. Watts
raw_affiliation_string: Department of Chemistry & Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
raw_author_name: Ginger V. Shultz
raw_affiliation_string: Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States

Abstract: Generating a testable hypothesis is a necessary skill for engaging in science, requiring both general reasoning skills and specific content knowledge of the phenomenon being investigated. While many students have the reasoning skills necessary for developing testable hypotheses in a general science context, it can be challenging for students to apply these skills to content areas where the phenomena being studied are unobservable, such as problems in organic chemistry. Generating hypotheses for experiments is a skill that students are expected to apply in chemistry teaching laboratories. Students can benefit from receiving real-time feedback to support developing this ability. However, providing feedback on writing can be challenging for instructors, especially in large-enrollment courses. For this study, we explored and compared the performance of several machine learning algorithms to classify organic chemistry students' written hypotheses for a science-general scenario and an organic chemistry-specific scenario. These models were trained to analyze students' written hypotheses for the presence of five features: (1) a testable prediction in a given situation; (2) a predicted change in the independent variable that is related to the dependent variable; (3) scientific content addressing the research question provided in the prompt; (4) a correct and completely defined independent variable; and (5) a correct and completely defined dependent variable. This work has implications for guiding future research on the feasibility of providing instructors with information about students' hypothesis-generating abilities that can support the delivery of real-time feedback.
Graph neural network recommendation algorithm based on improved dual tower model

Authors:
raw_author_name: Qiang He
raw_affiliation_string: School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, 610059, China
raw_author_name: Xinkai Li
raw_affiliation_string: School of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, China
raw_author_name: Biao Cai
raw_affiliation_string: School of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, China

Abstract: In this era of information explosion, recommendation systems play a key role in helping users to uncover content of interest among massive amounts of information. Pursuing a breadth of recall while maintaining accuracy is a core challenge for current recommendation systems. In this paper, we propose a new recommendation algorithm model, the interactive higher-order dual tower (IHDT), which improves current models by adding interactivity and higher-order feature learning between the dual tower neural networks. A heterogeneous graph is constructed containing different types of nodes, such as users, items, and attributes, extracting richer feature representations through meta-paths. To achieve feature interaction, an interactive learning mechanism is introduced to inject relevant features between the user and project towers. Additionally, this method utilizes graph convolutional networks for higher-order feature learning, pooling the node embeddings of the twin towers to obtain enhanced end-user and item representations. IHDT was evaluated on the MovieLens dataset and outperformed multiple baseline methods. Ablation experiments verified the contribution of interactive learning and high-order GCN components.
Milestones in speaker recognition

Authors:
raw_author_name: R. Sharma
raw_affiliation_string: Indian Institute of Information Technology (IIIT) Dharwad, Karnataka, India
raw_author_name: D. Govind
raw_affiliation_string: Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram, Andhra Pradesh, India
raw_author_name: J. Mishra
raw_affiliation_string: Indian Institute of Technology (IIT) Dharwad, Karnataka, India
raw_author_name: A. K. Dubey
raw_affiliation_string: Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram, Andhra Pradesh, India
raw_author_name: K. T. Deepak
raw_affiliation_string: Indian Institute of Information Technology (IIIT) Dharwad, Karnataka, India
raw_author_name: S. R. M. Prasanna
raw_affiliation_string: Indian Institute of Technology (IIT) Dharwad, Karnataka, India

Abstract: Abstract This article reviews significant research in the domain of speaker recognition, i.e., the task of determining the speaker’s identity from its speech. Unlike conventional review articles, this document strives to be concise and selective, provide a historical context, and reach a wider audience. In this endeavour, a summary of selected key works of every decade is provided which highlights the theme(s) of research of that period. At first, an overview of the humble beginnings of the 1960s and 70s is provided, followed by the key developments in the 80s and 90s. The prime focus of the research community in the 2000s is then discussed, leading to various non-conventional features, modelling techniques, and hybrid or fusion systems. The developments of the last decade (the 2010s), such as the i-vector-based systems, are then discussed. Modern speaker recognition based on Artificial Intelligence (AI), such as the x-vector system, and refinements of the i-vector-based systems using deep neural networks, are then discussed. The article concludes with a concise discussion of the evolving recent trends and allied research in speaker recognition.
Uncertainty-aware human-like driving policy learning with deep Bayesian inverse reinforcement learning

Authors:
raw_author_name: Di Zeng
raw_affiliation_string: State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, People's Republic of China
raw_author_name: Ling Zheng
raw_affiliation_string: State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, People's Republic of China
raw_author_name: Yinong Li
raw_affiliation_string: State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, People's Republic of China

Abstract: The application of deep reinforcement learning in driving policy learning for automated vehicles is limited by the difficulty of designing reward functions. Most existing inverse reinforcement learning (IRL) methods make a structure assumption on the reward function and omit the uncertainty of neural networks. In view of this, this paper proposes a novel deep Bayesian IRL method that addresses both the reward function designing and the uncertainty-measuring issues by learning an approximate posterior distribution over the reward function. Furthermore, we propose to train uncertainty-aware human-like driving policies by maximising the predicted reward and penalising its uncertainty. Finally, the proposed methods were validated in simulated highway driving scenarios. The results show that AVRL models uncertainty and learns reward functions significantly outperforming the existing IRL method applied in automated driving. It was also found that penalising the uncertainty of the reward function during policy training improves the success rate and human likeness of the learned policy.
Minimal time required to charge a quantum system

Authors:
raw_author_name: Dario Rosa
raw_affiliation_string: Basic Science Program, Korea University of Science and Technology (UST), Daejeon-34113, Korea; Center for Theoretical Physics of Complex Systems, Institute for Basic Science (IBS), Daejeon-34126, Korea
raw_author_name: Dominik Šafránek
raw_affiliation_string: Center for Theoretical Physics of Complex Systems, Institute for Basic Science (IBS), Daejeon-34126, Korea

Abstract: We introduce a quantum charging distance as the minimal time that it takes to reach one state (charged state) from another state (depleted state) via a unitary evolution, assuming limits on the resources invested into the driving Hamiltonian. For pure states it is equal to the Bures angle, while for mixed states its computation leads to an optimization problem. Thus, we also derive easily computable bounds on this quantity. The charging distance tightens the known bound on the mean charging power of a quantum battery, it quantifies the quantum charging advantage, and it leads to an always achievable quantum speed limit. In contrast with other similar quantities, the charging distance does not depend on the eigenvalues of the density matrix, it depends only on the corresponding eigenspaces. This research formalizes and interprets quantum charging in a geometric way, and provides a measurable quantity that one can optimize to maximize the speed of charging of future quantum batteries.