Reviewerly/exHarmony huggingface dataset:
NLU-V: A Family of Instruction Set Extensions for Efficient Symmetric Cryptography on RISC-V

Authors:
raw_author_name: Elif Bilge Kavun
raw_affiliation_string: Faculty of Computer Science and Mathematics, University of Passau, 94032 Passau, Germany

Abstract: Cryptographic primitives nowadays are not only implemented in high-performance systems but also in small-scale systems, which are increasingly powered by open-source processors, such as RISC-V. In this work, we leverage RISC-V’s modular base instruction set and architecture to propose a generic instruction set extension (ISE) for symmetric cryptography. We adapt the work from Engels et al. in ARITH’13, the non-linear/linear instruction set extension (NLU), which presents a generic hardware/software co-design solution for efficient symmetric crypto implementations through a hardware unit extending the 8-bit AVR instruction set. These new instructions realize non-linear and linear layers, which are widely used to implement the block ciphers in symmetric cryptography. Our proposal modifies and extends the NLU instructions to a 32-bit RISC-V architecture; hence, we call the proposed ISE ‘NLU-V’. The proposed architecture is integrated into the open-source RISC-V implementation ‘Icicle’ and synthesized on a Xilinx Kintex-7 XC7K160T FPGA. The area overhead for the proposed NLU-V ISE is 1088 slice registers and 4520 LUTs. As case studies, the PRESENT and AES block ciphers are implemented using the new ISE on RISC-V in assembly. Our evaluation metric to showcase the performance gain, Z ‘time-area-product (TAP)’ (the execution time in clock cycles times code memory consumption), reflects the impact of the proposed family of instructions on the performance of the cipher implementations. The simulations show that the NLU-V achieves 89% gain for PRESENT and 68% gain for AES. Further, the NLU-V requires 44% less lines of code for the PRESENT and 23% less for the AES implementation.
Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Authors:
raw_author_name: Konstantinos I. Roumeliotis
raw_affiliation_string: Department of Informatics and Telecommunications, University of Peloponnese, Akadimaikou G. K. Vla-chou Street, 22131 Tripoli, Greece
raw_author_name: Nikolaos D. Tselikas
raw_affiliation_string: Department of Informatics and Telecommunications, University of Peloponnese, Akadimaikou G. K. Vla-chou Street, 22131 Tripoli, Greece
raw_author_name: Dimitrios K. Nasiopoulos
raw_affiliation_string: Department of Agribusiness and Supply Chain Management, School of Applied Economics and Social Sciences, Agricultural University of Athens, 11855 Athens, Greece

Abstract: This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations.
Mono‐MVS: textureless‐aware multi‐view stereo assisted by monocular prediction

Authors:
raw_author_name: Maoteng Zheng
raw_affiliation_string: National Engineering Research Center for Geographic Information System China University of Geosciences Wuhan China
raw_author_name: Peiyu Chen
raw_affiliation_string: School of Geography and Information Engineering China University of Geosciences Wuhan China
raw_author_name: Xiuguo Liu
raw_affiliation_string: School of Geography and Information Engineering China University of Geosciences Wuhan China

Abstract: Abstract The learning‐based multi‐view stereo (MVS) methods have made remarkable progress in recent years. However, these methods exhibit limited robustness when faced with occlusion, weak or repetitive texture regions in the image. These factors often lead to holes in the final point cloud model due to excessive pixel‐matching errors. To address these challenges, we propose a novel MVS network assisted by monocular prediction for 3D reconstruction. Our approach combines the strengths of both monocular and multi‐view branches, leveraging the internal semantic information extracted from a single image through monocular prediction, along with the strict geometric relationships between multiple images. Moreover, we adopt a coarse‐to‐fine strategy to gradually reduce the number of assumed depth planes and minimise the interval between them as the resolution of the input images increases during the network iteration. This strategy can achieve a balance between the computational resource consumption and the effectiveness of the model. Experiments on the DTU, Tanks and Temples, and BlendedMVS datasets demonstrate that our method achieves outstanding results, particularly in textureless regions.
Faithful quantum teleportation through common and independent qubit-noise configurations and multi-parameter estimation in the output of teleported state

Authors:
raw_author_name: Seyed Mohammad Hosseiny
raw_affiliation_string: Physics Department, Faculty of Science, Vali-e-Asr University of Rafsanjan , Rafsanjan, Iran
raw_author_name: Jamileh Seyed-Yazdi
raw_affiliation_string: Physics Department, Faculty of Science, Vali-e-Asr University of Rafsanjan , Rafsanjan, Iran
raw_author_name: Milad Norouzi
raw_affiliation_string: Physics Department, Faculty of Science, Vali-e-Asr University of Rafsanjan , Rafsanjan, Iran

Abstract: Quantum teleportation allows the transmission of unknown quantum states over arbitrary distances. This paper studies quantum teleportation via two non-interacting qubits coupled to local fields and Ornstein Uhlenbeck noise. We consider two different qubit-noise configurations, i.e., common qubit-noise interactions and independent qubit-noise interactions. We introduce a Gaussian Ornstein Uhlenbeck process to take into account the noisy effects of the local external fields. Furthermore, we address the intrinsic behavior of classical fields toward single- and two-qubit quantum teleportation as a function of various parameters. Additionally, using a quantum estimation theory, we study single- and multi-parameter estimation of the teleported state output for single and two-qubit quantum teleportation scenarios. One important application of this work is obtaining more valuable information in quantum remote sensing.
Advances in AI and machine learning for predictive medicine

Authors:
raw_author_name: Alok Sharma
raw_affiliation_string: Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan

Abstract: The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate prediction. This review explores a revolutionary shift in predictive modeling through the application of deep learning (DL), specifically convolutional neural networks (CNNs). Using transformation methods such as DeepInsight, omics data with independent variables in tabular (table-like, including vector) form can be turned into image-like representations, enabling CNNs to capture latent features effectively. This approach not only enhances predictive power but also leverages transfer learning, reducing computational time, and improving performance. However, integrating CNNs in predictive omics data analysis is not without challenges, including issues related to model interpretability, data heterogeneity, and data size. Addressing these challenges requires a multidisciplinary approach, involving collaborations between ML experts, bioinformatics researchers, biologists, and medical doctors. This review illuminates these complexities and charts a course for future research to unlock the full predictive potential of CNNs in omics data analysis and related fields.
Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

Authors:
raw_author_name: Neda Meibodi
raw_affiliation_string: Department of Psychology, Philipps-Universität Marburg, Gutenbergstraße 18, Marburg, 35037, Hessen, Germany
raw_author_name: Hossein Abbasi
raw_affiliation_string: Department of Psychology, Philipps-Universität Marburg, Gutenbergstraße 18, Marburg, 35037, Hessen, Germany
raw_author_name: Anna Schubö
raw_affiliation_string: Department of Psychology, Philipps-Universität Marburg, Gutenbergstraße 18, Marburg, 35037, Hessen, Germany
raw_author_name: Dominik Endres
raw_affiliation_string: Department of Psychology, Philipps-Universität Marburg, Gutenbergstraße 18, Marburg, 35037, Hessen, Germany

Abstract: Abstract Attention can be biased by previous learning and experience. We present an algorithmic-level model of this selection history bias in visual attention that predicts quantitatively how stimulus-driven processes, goal-driven control and selection history compete to control attention. In the model, the output of saliency maps as stimulus-driven guidance interacts with a history map that encodes learning effects and a goal-driven task control to prioritize visual features. The model works on coded features rather than image pixels which is common in many traditional saliency models. We test the model on a reaction time (RT) data from a psychophysical experiment. The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms selection history effects on attention guidance. The model is able to capture individual differences between participants’ RTs and response probabilities per group. Moreover, we demonstrate that a model with a reduced set of maps performs worse, indicating that integrating history, saliency and task information are required for a quantitative description of human attention. Besides, we show that adding intertrial effect to the model (as another lingering bias) improves the model’s predictive performance.
Analyzing variational quantum landscapes with information content

Authors:
raw_author_name: Adrián Pérez-Salinas
raw_affiliation_string: 〈aQaL〉 Applied Quantum Algorithms, Universiteit Leiden, Leiden, Netherlands
raw_author_name: Hao Wang
raw_affiliation_string: 〈aQaL〉 Applied Quantum Algorithms, Universiteit Leiden, Leiden, Netherlands
raw_author_name: Xavier Bonet-Monroig
raw_affiliation_string: Instituut-Lorentz, Universiteit Leiden, Niels Bohrweg 2, 2333 CA, Leiden, Netherlands

Abstract: Abstract The parameters of the quantum circuit in a variational quantum algorithm induce a landscape that contains the relevant information regarding its optimization hardness. In this work, we investigate such landscapes through the lens of information content, a measure of the variability between points in parameter space. Our major contribution connects the information content to the average norm of the gradient, for which we provide robust analytical bounds on its estimators. This result holds for any (classical or quantum) variational landscape. We validate the analytical understating by numerically studying the scaling of the gradient in an instance of the barren plateau problem. In such instance, we are able to estimate the scaling pre-factors in the gradient. Our work provides a way to analyze variational quantum algorithms in a data-driven fashion well-suited for near-term quantum computers.
SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis

Authors:
raw_author_name: Fangzhou He
raw_affiliation_string: Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, People’s Republic of China
raw_author_name: Weijun Xu
raw_affiliation_string: Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
raw_author_name: Yong Zhang
raw_affiliation_string: Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
raw_author_name: Li Jiang
raw_affiliation_string: Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China

Abstract: Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model’s inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72 ms per instance on devices equipped only with CPUs. The model’s number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field.
Developer Ecosystems for Software Safety

Authors:
raw_author_name: Christoph Kern
raw_affiliation_string: Google

Abstract: How to design and implement information systems so that they are safe and secure is a complex topic. Both high-level design principles and implementation guidance for software safety and security are well established and broadly accepted. For example, Jerome Saltzer and Michael Schroeder's seminal overview of principles of secure design was published almost 50 years ago, and various community and governmental bodies have published comprehensive best practices about how to avoid common software weaknesses. This article argues, based on experience at Google, that focusing on developer ecosystems is both practical and effective, and can achieve a drastic reduction in the rate of common classes of defects across hundreds of applications being developed by thousands of developers.
The Nexus of Smart Contracts and Digital Twins Transforming Green Finance With Automated Transactions in Investment Agreements

Authors:
raw_author_name: K. Balaji
raw_affiliation_string: Presidency University, India

Abstract: In recent years, the integration of digital twins and smart contracts has become a revolutionary force in the field of green finance. The purpose of this chapter is to examine how smart contracts and digital twins are interwoven, particularly in relation to how they have significant influence in the field of green finance. It looks into the way these innovations work together to transform agreements regarding investments by making automated transactions possible and maximising effectiveness and transparency. This investigation demonstrates how working together can simplify complicated financing processes and increase responsibility and dependability for sustainable investment projects. The chapter demonstrates the real-world use of digital twins and smart contracts via case studies and theoretical insights, highlighting their revolutionary influence on changing the face of green finance.
Blockchain in Human Resource Management

Authors:
raw_author_name: Saumendra Das
raw_affiliation_string: GIET University, Gunupur, India
raw_author_name: Koppala Venugopal
raw_affiliation_string: Aditya Institute of Technology and Management, India
raw_author_name: N. Saibabu
raw_affiliation_string: Aditya Institute of Technology and Management, India

Abstract: This chapter aims to provide fresh ideas from philosophy to help human resources managers adopt blockchain technology in their work. In order to categorize the 25 significant items, the authors examined the 126 publications that were made available for acceptance between 2016 and 2021 in the Scopus and Web of Science databases. Using bibliometric data, the headings, titles, and keyword phrases of each of those 25 papers have all been meticulously analysed. Depending on the bibliometric data gathered, word relationship network maps were produced using VOS viewer. There are four topic groupings that were made using the linked visualisations. It has been decided that the ideas of employee-systems interface (ESI) and blockchain framework (BCF) for HR are going to encourage the adoption and application of distributed computing technologies in the area of the administration of human resources. After reading all 25 articles, the authors thoroughly and in-depth examined the key terms that supported the two philosophical subjects and determined the validity of the results.
Advanced Data Analytics in Green Finance

Authors:
raw_author_name: Afzalur Rahman
raw_affiliation_string: Presidency University, India

Abstract: This chapter explores how cutting-edge technologies, such as big data analytics, digital twins, and blockchain, are revolutionizing the green finance industry. It examines how these technologies' convergence improves sustainable financial practices efficiency, transparency, and decision-making while providing a fresh viewpoint on their complementary effects. The chapter describes the distinct functions of digital twins in asset optimization and risk management, blockchain in guaranteeing security and trust, and sophisticated data analytics in facilitating well-informed financial decisions.
Foundations of Blockchain and Digital Twins

Authors:
raw_author_name: Ravi Prakash B.
raw_affiliation_string: Presidency University, India

Abstract: The rapid evolution of transformative technologies, including blockchain and digital twins, is reshaping numerous industries. Blockchain, recognized for its distributed ledger capabilities, is gaining widespread acceptance owing to its secure and transparent data management features. Simultaneously, digital twins, virtual replicas of physical assets, are proving instrumental in offering real-time insights and enabling remote monitoring and control. This chapter explores the convergence of these two technologies, emphasizing their potential in the real world. Commencing with an overview of blockchain and digital twins, the chapter proceeds to scrutinize their architecture and consensus mechanisms. Subsequently, it delves into the concept of digital twins and their versatile applications across various sectors. Finally, it articulates the advantages derived from the integration of blockchain and digital twins.
Unlocking Efficiency

Authors:
raw_author_name: Saumendra Das
raw_affiliation_string: GIET University, India

Abstract: Blockchain technology has gained considerable attention in recent years due to its potential to revolutionize various industries. Human resources (HR) management in large enterprises is an area that can greatly benefit from the transparency, security, and efficiency offered by blockchain technology. This study explores the concept of blockchain as a service (BaaS) in enterprise HR management and its potential to unlock efficiency, streamline processes, and enhance data security elaborating a comprehensive exploration of the theoretical underpinnings, practical application, and potential ramifications of BaaS within HR operations, highlighting the profound transformative prospects inherent in this emerging technology. The integration of blockchain as a service (BaaS) in enterprise human resources management is the focus of this study. The findings suggest that BaaS has the potential to revolutionize HR management by offering a secure, transparent, and efficient platform for various HR processes, from recruitment and onboarding to payroll and talent management.
Blockchain Technology for Sustainable Investment

Authors:
raw_author_name: Ritu Gupta
raw_affiliation_string: Maharaja Agrasen institute of Technology, India
raw_author_name: None Kiran
raw_affiliation_string: Apeejay Institute of Management and Engineering Technical Campus, India

Abstract: The purpose of this review is to provide a comprehensive analysis of how blockchain technology can enhance sustainable investment practices. The authors aim to assess the current applications, benefits, and challenges of blockchain in sustainable investment and identify opportunities for future development. They conducted a thorough review of existing literature and case studies to analyze the current landscape of blockchain technology in sustainable investment. This involved examining various blockchain platforms and applications, as well as assessing their impact on transparency, security, and efficiency in sustainable investment. This analysis reveals that blockchain technology offers significant advantages for sustainable investment, including improved transparency, reduced fraud, and increased accessibility for smaller investors. However, challenges such as scalability and regulatory hurdles persist. The review also highlights successful cases and real-world examples of blockchain implementation in sustainable investment.