Reviewerly/exHarmony huggingface dataset:
A selection of level of supplier in supply chain management using binary coded genetic algorithm with a case study towards Pareto optimality

In this work, authors discuss the way of selecting level of supplier by using the concept of binary coded genetic algorithm. For the best solution due to involvement of multi objective functions, the process of Tournament selection is widely discussed. In addition to this, authors involve fuzzy parameters due to the aspiration levels of Decision maker in analysis part for more clarity towards optimality. As a case study towards pareto optimality, the theory of non-dominance of solutions is properly discussed with the help of Pareto frontier. At last the values of objective functions based on quality, cost and service levels following an example are being analyzed with a significant view towards optimality. Based on the optimal solutions, the level of supplier selection is properly discussed.
Blockchain applicability in the management of urban water supply and sanitation systems in Spain

Blockchain constitutes a disruptive technology that is currently changing business models and the way organizations operate. This paper explores the applicability of blockchain technology, and of distributed ledger technologies in general, to urban water supply and sanitation services in Spain. The potential of this technology for improving processes in this sector is assessed through a specific methodology of strategic analysis developed for this purpose. First, the technical, legal and managerial factors that condition the potential use of this technology to address the global, operational and governance challenges faced by urban water management are explored. Second, strategic analysis tools (e.g. value chain, factors of competition, and benchmarking) are used to model a water utility organization as a set of processes and characterize blockchain as an enabling technology with both benefits (traceability, immutability, and disintermediation) and limitations. Based on cost-benefit analysis, use cases in which the implementation of a blockchain could improve the organization's performance can be discerned. The results identify the processes and sub-processes of urban water utility management for which the use of a blockchain could improve performance: comprehensive maintenance, the management of serious incidents, and supplier management. Essentially, the methodology developed identifies management processes whose requirements are efficiently met through automation derived from blockchain solutions. Regardless of the management models currently in place in Spain, the traceability and disintermediation benefits of blockchain solutions can help to overcome governance and efficiency challenges associated with the management of urban water supply and sanitation services.
Ownership Guided C to Rust Translation

Abstract Dubbed a safer C, Rust is a modern programming language that combines memory safety and low-level control. This interesting combination has made Rust very popular among developers and there is a growing trend of migrating legacy codebases (very often in C) to Rust. In this paper, we present a C to Rust translation approach centred around static ownership analysis. We design a suite of analyses that infer ownership models of C pointers and automatically translate the pointers into safe Rust equivalents. The resulting tool, Crown , scales to real-world codebases (half a million lines of code in less than 10 s) and achieves a high conversion rate.
Toward Full Accounting for Leakage Exploitation and Mitigation in Dynamic Encrypted Databases

Encrypted databases have garnered considerable attention for their ability to safeguard sensitive data outsourced to third parties. However, recent studies have revealed the vulnerability of encrypted databases to leakage-abuse attacks on their search module, prompting the development of countermeasures to address this issue. While most studies have focused on static databases, limited research has been conducted on dynamic encrypted databases. To bridge this gap, this paper focuses on undertaking a comprehensive examination of leakage exploitation in dynamic encrypted databases, with the aim of providing effective mitigations. Our investigation begins with two attacks that can be employed to recover encrypted queries. The first attack, known as an active attack, involves injecting encoded files and utilizing correlated file volume information. The second attack, referred to as a passive attack, identifies unique relational characteristics of queries across database updates, assuming certain background knowledge of the plaintext databases. To mitigate these attacks, a two-layer encrypted database hardening approach is proposed, which obfuscates both search indexes and files in a continuous way. Doing so allows us to eliminate the unique characteristics emerging after data updates constantly. We conduct a series of experiments to confirm the severity of our attacks and the effectiveness of our countermeasures.
Sustainability-Driven Consumer Experience Design of an Online Retail System for Household Appliances

INTRODUCTION: In the field of information system design, mobile-based platforms have emerged as a significant and indispensable component in recent years. This research examines the incorporation of sustainable responsibility by a household appliance brand into the consumer experience of a retail mobile application through experience design.OBJECTIVES: The primary objective of this study is to develop strategies for consumer experience design driven by sustainability, thereby fostering the adoption of sustainable consumption behaviours that yield positive implications for the environment, public life, and brand values.METHODS: A combination of quantitative and qualitative methods was employed, encompassing preliminary user interviews, questionnaire surveys, and in-depth user interviews. Initially, design research explored consumers' comprehension of sustainability, sustainable consumption demands, behavioural habits, and experiential expectations. Subsequently, design strategies were derived through insight clustering analysis, considering functional information, interaction experience, and marketing interaction.RESULTS: The study illustrated sustainable consumer experience design through the interface and interaction design of a retail mobile application, serving as a touchpoint for an online retail system. The overall effectiveness of the design solution was assessed through usability and consumer experience tests and the results were positive and evidently conducive to improve the retail mobile application.CONCLUSION: Based on the evaluation of the design proposal, three key elements were identified for the experience design of retail mobile systems driven by sustainable responsibility: accentuating the sustainable attributes of products, enhancing consumer autonomy and competence, and aligning marketing endeavours with sustainable values.
Integration of cultural data from digital repositories: anoverview of the DPLA Hubs

Introduction: The Digital Public Library of America (DPLA) is an initiative aimed at expanding the reach of its content and the possibilities of improving and using data to generate new knowledge from data integration. The search for integration, however, results in a series of possibilities and challenges for digital repositories, providers and data servers.Objective:To present an overview of the technological solutions underlying data collection and availability from the DPLA partner repositories, named Hubs, to point out aspects of the interoperability opportunities and challenges they face in the search for integrated access. Methodology:This is a bibliographical, qualitative and exploratory research, with analysis based on the discussion found in the literature and the results obtained in the analysis of the platforms. Results: The technological solutions adopted both by the DPLA to collect and aggregate data and by the Hubs, data providers, which use different solutions to aggregate data from the various contributing cultural institutions and provide them to the DPLA, are presented as results. Conclusion:The studyconcluded that despite the technical, semantic and managerial challenges, great efforts have been undertaken to adopt solutions and best practices aimed at the engagement of data providers. Both the Hubs and their contributing institutions present programs to encourage and support digitization and documentation, as well as guidelines to guarantee the quality and legal rights of access to content, which is essential to improve users' experience and multi-dimension the possibilities to transform information into knowledge
Rectifying Unfairness in Recommendation Feedback Loop

The issue of fairness in recommendation systems has recently become a matter of growing concern for both the academic and industrial sectors due to the potential for bias in machine learning models. One such bias is that of feedback loops, where the collection of data from an unfair online system hinders the accurate evaluation of the relevance scores between users and items. Given that recommendation systems often recommend popular content and vendors, the underlying relevance scores between users and items may not be accurately represented in the training data. Hence, this creates a feedback loop in which the user is not longer recommended based on their true relevance score but instead based on biased training data. To address this problem of feedback loops, we propose a two-stage representation learning framework, B-FAIR, aimed at rectifying the unfairness caused by biased historical data in recommendation systems. The framework disentangles the context data into sensitive and non-sensitive components using a variational autoencoder and then applies a novel Balanced Fairness Objective (BFO) to remove bias in the observational data when training a recommendation model. The efficacy of B-FAIR is demonstrated through experiments on both synthetic and real-world benchmarks, showing improved performance over state-of-the-art algorithms.
Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems

Learning reinforcement learning (RL)-based recommenders from historical user-item interaction sequences is vital to generate high-reward recommendations and improve long-term cumulative benefits. However, existing RL recommendation methods encounter difficulties (i) to estimate the value functions for states which are not contained in the offline training data, and (ii) to learn effective state representations from user implicit feedback due to the lack of contrastive signals.
Taxicab Correspondence Analysis and Taxicab Logratio Analysis: A Comparison on Contingency Tables and Compositional Data

In this paper, we attempt to see further by relating theory with practice: First, we review the principles on which three interrelated well developed methods for the analysis and visualization of contingency tables and compositional data are erected: Correspondence analysis based on Benzécri’s principle of distributional equivalence, Goodman’s RC association model based on Yule’s principle of scale invariance, and compositional data analysis based on Aitchison’s principle of subcompositional coherence. Second, we introduce a novel index named intrinsic measure of the quality of the signs of the residuals for the choice of the method. The criterion is based on taxicab singular value decomposition, on which the package TaxicabCA in R is developed. We present a minimal R script thatcan be executed to obtain the numerical results and the maps in this paper. Third, we introduce a flexible method based on the novel index for the choice of the constant to be added to contingency tables with zero counts so that logratio methods can be applied.
A Bilateral Game Approach for Task Outsourcing in Multi-access Edge Computing

Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of servers’ resource allocation has been widely studied. The limited computational resources of edge servers (ESs) cannot meet the different demands of terminal entities (TEs). This makes it a challenge to efficiently schedule computational tasks on ESs. In this paper, we consider a MEC resource transaction market with multiple ESs and multiple TEs, which are interdependent and mutually influence each other. This paper aims to investigate the dynamic tasks allocation problem between TEs and ESs and to meet the optimal benefits for both parties in MEC system. However, this many-to-many interaction requires resolving several problems, including task allocation, TEs’ selection on ESs and conflicting interests of both parties. A bilateral game framework is applied to tackle the tasks allocation problem by modeling the problem as two noncooperative games: the supplier and customer side games. The existence and uniqueness of the Nash equilibrium in the aforementioned games are proved. A distributed task outsourcing algorithm (DTOA) is designed to determine the equilibrium. Our simulation results have demonstrated the superior performance of DTOA in increasing the ESs’ profit and TEs’ payoffs, as well as flattening the peak and off-peak loads.
An intuitionistic fuzzy-based intelligent system for semantic interoperability and privacy preservation in healthcare systems

An individual cannot depend on a single caregiver for the course of their entire lifespan. Each caregiver maintains several kinds of health records in various forms. There are differences in the formats, standards, nomenclature, and notation. The only way to fix this is by implementing semantic interoperability. There are precise and imprecise relationships (synonymy, hypernymy, hyponymy, meronymy, and holonymy) among various terms used in healthcare. The intuitionistic fuzzy-based semantic interoperability paradigm is suggested in this article as a solution to this form of uncertainty. Protected health information, as defined by Health Insurance Portability and Accountability Act, must be preserved for all medical records. To ensure privacy, AES and ElGamal encryption methods are used. The suggested solution performs better compared to existing semantic interoperability and privacy techniques.
The metaverse revolution: The end of traditional classrooms?

Journal Article The metaverse revolution: The end of traditional classrooms? Get access Ambiro Puji Asmaroini, Ambiro Puji Asmaroini Universitas Muhammadiyah Ponorogo, 63471, Indonesia Search for other works by this author on: Oxford Academic PubMed Google Scholar Candra Hermawan, Candra Hermawan Universitas Negeri Malang, 65114, IndonesiaUniversitas 17 Agustus 1945 Banyuwangi, 68416 Indonesia Search for other works by this author on: Oxford Academic PubMed Google Scholar Raup Padillah Raup Padillah Universitas PGRI Banyuwangi, 41482, Indonesia Address correspondence to Raup Padillah, E-mail: raup.padillah@gmail.com https://orcid.org/0009-0008-2613-4046 Search for other works by this author on: Oxford Academic PubMed Google Scholar Journal of Public Health, fdad132, https://doi.org/10.1093/pubmed/fdad132 Published: 20 July 2023 Article history Received: 10 June 2023 Accepted: 03 July 2023 Published: 20 July 2023
On the Audibility of Electric Guitar Tonewood

Electric guitar manufacturers have used tropical woods in guitar production for decades claiming it as beneficiary to the quality of the instruments.These claims have often been questioned by guitarists but now, with many voices raising concerns regarding the ecological sustainability of such practices, the topic becomes even more important.Efforts to find alternatives must begin with a greater understanding of how tonewood affects the timbre of an electric guitar.The presented study examined how the sound of a simplified electric guitar changes with the use of various wood species.Multiple sounds were recorded using a specially designed test setup and their analysis showed differences in both spectral envelope and the generated signal level.The differences between the acoustic characteristics of tones produced by the tonewood samples explored in the study were larger than the just noticeable differences reported for the respective characteristics in the literature.To verify these findings an informal listening test was conducted which showed that sounds produced with different tonewoods were distinguishable to the average listener.
Improved RRT∗ Algorithm for Automatic Charging Robot Obstacle Avoidance Path Planning in Complex Environments

A new and improved RRT * algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm.This algorithm enables the robot to avoid obstacles, find the optimal path, and complete automatic charging docking.It maintains the global completeness and path optimality of the RRT algorithm while also improving the iteration speed and quality of generated paths in both 2D and 3D path planning.After finding the optimal path, the B-sample curve is used to optimize the rough path to create a smoother and more optimal path.In comparison experiments, the new algorithm yielded reductions of 35.5%, 29.2%, and 11.7% in search time and 22.8%, 19.2%, and 9% in path length for the 3D environment.Finally, experimental validation of the automatic charging of electric vehicles was conducted to further verify the effectiveness of the algorithm.The simulation experimental validation was carried out by kinematic modeling and building an experimental platform.The error between the experimental results and the simulation results is within 10%.The experimental results show the effectiveness and practicality of the algorithm.
Cloud Security Using The Smart Contracts

In today's digital age, it's becoming increasingly common for individuals and businesses alike to opt for cloud-based storage over traditional on-premise storage. However, this shift towards cloud computing also brings with it a host of new security challenges. Data stored on public cloud infrastructure is susceptible to various types of vulnerabilities, threats, and attacks that can compromise its confidentiality, availability, and integrity. To address these concerns, we propose an innovative architecture that integrates multiple entities - including multiple public clouds, a private cloud, smart contracts, and the data owner, client, or user. Our decentralized cloud architecture provides a robust form of security for data by incorporating smart contracts between the data owner and the private cloud, as well as between the client and the data owner. By utilizing this approach, we can help ensure that data is protected from unauthorized access, while also providing users with a highly flexible and scalable solution for managing their data in the cloud. With our architecture, businesses and individuals can enjoy the benefits of cloud-based storage while also maintaining the highest levels of security and peace of mind.
Enhancing Qubit Readout with Autoencoders

In addition to the need for stable and precisely controllable qubits, quantum computers take advantage of good readout schemes. Superconducting qubit states can be inferred from the readout signal transmitted through a dispersively coupled resonator. This work proposes a readout classification method for superconducting qubits based on a neural network pretrained with an autoencoder approach. A neural network is pretrained with qubit readout signals as autoencoders in order to extract relevant features from the data set. Afterward, the pretrained-network inner-layer values are used to perform a classification of the inputs in a supervised manner. We demonstrate that this method can enhance classification performance, particularly for short- and long-time measurements where more traditional methods present inferior performance.
Artificial intelligence for video game visualization, advancements, benefits and challenges

In recent years, the field of artificial intelligence (AI) has witnessed remarkable progress and its applications have extended to the realm of video games. The incorporation of AI in video games enhances visual experiences, optimizes gameplay and fosters more realistic and immersive environments. In this review paper, we systematically explore the diverse applications of AI in video game visualization, encompassing machine learning algorithms for character animation, terrain generation and lighting effects following the PRISMA guidelines as our review methodology. Furthermore, we discuss the benefits, challenges and ethical implications associated with AI in video game visualization as well as the potential future trends. We anticipate that the future of AI in video gaming will feature increasingly sophisticated and realistic AI models, heightened utilization of machine learning and greater integration with other emerging technologies leading to more engaging and personalized gaming experiences.
Resource Construction and Ensemble Learning based Sentiment Analysis for the Low-resource Language Uyghur

&lt;p&gt;To address the problem of scarce low-resource sentiment analysis corpus nowadays, this paper proposes a sentence-level sentiment analysis resource conversion method HTL based on the syntactic-semantic knowledge of the low-resource language Uyghur to convert high-resource corpus to low-resource corpus. In the conversion process, a k-fold cross-filtering method is proposed to reduce the distortion of data samples, which is used to select high-quality samples for conversion; finally, the Uyghur sentiment analysis dataset USD is constructed; the Baseline of this dataset is verified under the LSTM model, and the accuracy and F1 values reach 81.07% and 81.13%, respectively, which can provide a reference for the construction of low-resource language corpus nowadays. The accuracy and F1 values reached 81.07% and 81.13%, respectively, which can provide a reference for the construction of today’s low-resource corpus. Meanwhile, this paper also proposes a sentiment analysis model based on logistic regression ensemble learning, SA-LREL, which combines the advantages of several lightweight network models such as TextCNN, RNN, and RCNN as the base model, and the meta-model is constructed using logistic regression functions for ensemble, and the accuracy and F1 values reach 82.17% and 81.86% respectively in the test set, and the experimental results show that the method can effectively improve the performance of Uyghur sentiment analysis task.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
How Fault-Tolerant Quantum Computing Benefits from Cryo-CMOS Technology

Given the limited space and cooling capacity in dilution refrigerators, it is challenging to scale the number of qubits for a fault-tolerant quantum computer (QC). In this paper, we study a custom-scaled CMOS technology to overcome the constraints in the dilution refrigerators. With Cryo-Design/ Technology CoOptimization (Cryo-DTCO) in an advanced node, one can then reduce the control power from 26.8 mW/ qubit to 8.4 mW/ qubit $(\sim 0.31 \times)$. Projections suggest this may be sufficient to enable error corrections via surface codes for fault-tolerant computing.