Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Social Media Comments based on Random Forest and Support Vector Machine

The rapid advancement of social media has enabled people from all around the world to create, express and share their feelings easily and freely. It also generates voluminous data in various fields including health, education, finance and security etc. The huge amount of data is beneficial for research and analysis. In this study, a model based on algorithm of Random Forest and SVM is developed to classify the sentiment in social media comments. Nearly 100 thousand posts data on Chinese Language are taken from social media comments, then trained and tested using emotion theories as well as machine learning techniques. Each post would be rated based on the sentiment behind it on a scale of positive and negative. The model is later used to analyze specific incident to strengthen its accuracy, then cluster valuable comments. The study would facilitate the decision-making process and various measures can be taken to enhance the management of government.
Documenting Regulatory Requirements Decision-Making as a Compliance Concern

Software practitioners must implement a growing list of regulatory and security mandates, but have no established tool or mechanism for demonstrating their due diligence or compliance efforts exists. Providing an approach does more than help software practitioners. External agencies and auditors also need tools or mechanisms to enforce compliance requirements. Consumers also benefit. Standardized approaches a mechanism for accountability regarding compliance without software organizations compromising its proprietary or sensitive information. Currently, perceptions, practices, or decision making on regulatory or security standard compliance is not a well researched area in academia. Our research aims to understand the practices and decision making software organizations apply toward regulatory compliance requirements during the software development process. Then, we take this improved understanding and apply it to building an approach that auditors or regulators can use to validate regulatory compliance throughout the entire software development process.
An Online Performance Index for the Kalman Filter

In this article, we propose a performance index (PI) on the point rather than batches to quantify the real-time performance of the Kalman filter (KF). To avoid the problem of describing KF performance that may require actual values, we attempt to find a new index that does not require actual values. The uncertainty is the main factor that makes the KF deviate from the optimal estimate. Uncertainty information is extracted from the observations, the effect of uncertainty is projected onto the KF, and the degree of deviation from the optimal estimate is then obtained, which is unitized to reflect the performance. The main advantage of the proposed PI is the ability to quantify the KF performance in real time without the need for actual values and without the need to know the type and source of uncertainty. Two numerical examples and a practice-oriented case study are given to illustrate the effectiveness of the proposed approach. The results show that the PI provides a good representation of the performance.
Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines

Near-term noisy intermediate-scale quantum circuits can efficiently implement implicit probabilistic models in discrete spaces, supporting distributions that are practically infeasible to sample from using classical means. One of the possible applications of such models, also known as Born machines, is probabilistic inference, which is at the core of Bayesian methods. This paper studies the use of Born machines for the problem of training binary Bayesian neural networks. In the proposed approach, a Born machine is used to model the variational distribution of the binary weights of the neural network, and data from multiple tasks is used to reduce training data requirements on new tasks. The method combines gradientbased meta-learning and variational inference via Born machines, and is shown in a prototypical regression problem to outperform conventional joint learning strategies.
Watermarking Models and Artificial Intelligence

Machine learning and deep learning are good bets for solving various intelligence-related problems. While it has practical applications in watermarking, it performs less well on more standard tasks like prediction, classification, and regression. This article offers the results of a thorough investigation into watermarking using modern tools like AI, ML, and DL. Watermarking's origins, some historical context, and the most fascinating and practical applications are also covered briefly.
Utilisation-aware VM placement policy for workload consolidation in cloud data centres

In recent years, the demand for cloud services has risen. Data centres must have a growing number of servers to accommodate rising demand for cloud services, and data centres consume a lot of energy. Virtual machine consolidation (VMC) is a strategy for reducing energy consumption in data centres by shutting down underutilised servers while maintaining service levels (SLA). The VMC process is separated into four policies: overloaded host detection, underloaded host detection, virtual machine selection, and virtual machine deployment. The utilisation-aware VM placement (UAVMP) technique is presented in this research work, which efficiently selects the destination host for VMs migrating from overload/underload hosts based on the host's utilisation and resource skewness. The performance is assessed using the CloudSim simulator. When we compare UAVMP results with power-aware best fit decreasing (PABFD), modified best fit decreasing (MBFD), first fit (FF) and least fit (LF), we find that UAVMP outperforms all.
A Blockchain-Based Collaborative Auditing Scheme for Cloud Storage

Cloud storage system has brought great convenience but also brings serious security problems. To ensure data security in the cloud, users usually authorize a third-party auditor (TPA) to verify their data integrity regularly. However, TPA may lead to security threats such as data disclosure and tampering. Fortunately, blockchain makes up for the lack of TPA. But there exists the problem of interaction difficulties and expensive deployment of smart contracts in the blockchain scenario. This paper proposes a blockchain-based collaborative public auditing scheme for remote data. In the proposed scheme, the cloud service provider (CSP) completes the challenge and verification work independently, reducing the communication overhead in the proof stage and improving the overall system efficiency. In addition, we consider allowing users to seek partners to reduce the cost of personal audits. Through the EigenTrust model evaluating the credibility of users, the users can select a high-quality partner to complete public auditing work, effectively avoiding collusion among malicious users. The proposed scheme can be proved secure and efficient according to the complete security analysis and comprehensive performance evaluation.
Utility-optimized location privacy scheme with geo-indistinguishability

Geographic location privacy protection is an important research content of privacy protection in recent years. Local differential privacy mechanism is one of the mainstream geographic location privacy protection models. among ε-Geoindistinguishability has become the basic research standard of local differential privacy geographic location protection scheme using distance measurement. Combining the ideas of ε-Geo-indistinguishability and Utility-Optimized Local Differential Privacy, we propose a differential privacy concept Utility-Optimized- ε-Geo-indistinguishability which meets the different degrees of privacy protection in different geographical locations. This is the first time in the field of local differential privacy geographic location protection based on distance measurement. At the same time, we propose a localized differential privacy mechanism called Utility-Optimized Planar Laplace Mechanism that can meet UtilityOptimized-𝜀 -Geo-indistinguishability. Theoretical analysis and experiments based on real data sets show that the experimental effect of our proposed mechanism is better than the existing local differential privacy geographic protection mechanism.
Interacting with actuated walls: Exploring applications and input types

Previous literature on small-scale shape displays demonstrated the benefit of shape-changing interfaces and adaptive environments. However, research with large-scale implementations is limited to specific equipment. Thus, there is little understanding of how they can enrich user experience in a larger scope, especially from an interactive perspective. We proposed using pin-array large-scale implementations made of linear actuators matrix as vertical structures to replace static and planar walls and conducted a two-fold exploration to extend the understanding of potential applications and showcase users’ behaviors and preferences to inspire researchers. We summarized 14 potential applications for shape-changing walls, and presented users’ interaction preferences with such a wall of different sizes and interaction distances. We concluded with recommendations and guidelines for practitioners to design large-scale actuated walls.
Regulatory Effect on Information Sharing of Industrial Internet Platforms Based on Three Differentiated Game Scenarios

This study constructs a regulatory system for information sharing on industrial internet platforms from a technical and socio-legal perspective. A differentiated game is used to construct decentralized, centralized, and cost-sharing contract decision-making scenarios to obtain the optimal level of efforts for information-sharing regulation. Through a comparative analysis, the optimal solutions under the three scenarios were derived. These solutions were then analyzed through numerical simulations using Matlab2016a. Our research demonstrates the following: (1) Centralized decision-making is most beneficial to the development of information-sharing regulatory systems. (2) Beyond a critical value for the local government cost subsidy coefficients, changes in these coefficients have a more pronounced effect on improving the economic efficiency of the regulatory system, and vice versa. That is, when the cost subsidy coefficient is higher than 1/2, increasing the cost subsidy coefficient has a more pronounced effect on improving the economic efficiency of the information-sharing regulatory system. (3) In all scenarios, an increase in the regulatory effort can effectively enhance the reputation of the information-sharing regulatory system. This study further extends the research on information-sharing regulations and provides a practical guide to industrial internet platforms.
Experiencing Tangible Privacy Control for Smart Homes with PriKey

Existing software-based smart home privacy mechanisms are frequently indirect and cumbersome to use. We developed PriKey, a tangible privacy mechanism for smart homes that offers intuitive, device-independent, sensor-based, and user-centric privacy control. To render our concept comprehensible, we implemented a demonstration consisting of Wizard-of-Oz prototypes that show the envisioned form factor, size, and portability of our system, as well as a larger functional prototype of PriKey, which enables control of privacy-invasive sensors integrated into two exemplary smart devices, i.e., a smart speaker and a tablet.
Underwater image super-resolution using improved SRCNN

In recent years, industrialization and economic development in countries around the world have led to an ever-increasing demand for energy. Renewable energies are attracting attention, but they still often use mineral resources such as coal, petroleum, and natural gas, and onshore resources are depleting day by day. These energy and metal resources, such as copper, support Japan's industries and affluent lifestyle, and if Japan continues to rely on imports for most of these resources, it will become difficult for Japan to secure a stable supply of these energies and resources. Therefore, mining of mineral resources on the seafloor is essential to solve these problems, and research on seafloor resource surveys and mining is underway. Because direct human exploration and mining of seafloor resources are naturally dangerous, underwater robots are used to explore and mine seafloor resources. However, due to light absorption and turbidity in water, the underwater image of an underwater robot is sometimes less visible, making exploration unsatisfactory. Therefore, there is a need for higher-resolution underwater images of underwater robots. In this study, we perform super-resolution of underwater images using an improved SRCNN to support research on underwater images of underwater robots. The conventional SRCNN method uses the ReLU function as the activation function, but the improved SRCNN uses the PReLU function and FReLU function, which are extended activation functions of the ReLU function, to improve accuracy.
Breast Abnormalities' Classification Using Convolutional Neural Network

Deep learning (DP) holds great promise in many areas, especially in the medical field. Abnormalities in the breast threaten patients' lives, so it is crucial to go through the correct diagnosis. Thus, the participation of a convolutional neural network (CNN) in image analysis and classification could support a proper diagnosis. A set of mammograms are collected in the CBIS-DDSM dataset to train different CNN architectures and various models to classify the labeled images into mass and calcification as a first step and benign or malignant as a second step. The first task is accomplished using two different models, the first one is a CNN and the second includes the VGG16, both achieved good results on the validation with accuracies of 88% and 90% respectively. Regarding the second task, it is performed using CNN only. The accuracy does not exceed 66% due to the limitation in the number of mammograms.
UNIVERSITY TEACHER’S RESEARCH STRATEGIES FOR HYBRID LEARNING REALIZATION

The article identifies the problem of developing University teachers’ skills of how to apply research strategies to study the conditions in which hybrid learning at the university would be successful. It is shown that the spread of hybrid methodology in higher education has led to the complication in educational process realization and required teachers to search for appropriate teaching tools and methods serving to handle the new challenges. The issue of hybrid learning organization at the university is one of the most widely discussed and urgent in modern education. The spread of hybrid methodology in higher education has led to the complication in educational process realization and required teachers to search for appropriate teaching tools and methods serving to handle the new challenges. Moreover, it required teachers to develop their educational design skills, skills of evaluation and research skills to study hybrid educational practice. The purpose of the paper is to explain the foundation and describe the research strategies that University teachers need to successfully organize and implement hybrid learning. In accordance with the key focus of University teachers’ activity (solving professional issues), there are four types of research strategies: a strategy aimed at researching the external environment and the surrounding society; a strategy aimed at researching the subject of education; a strategy aimed at researching the activities of the subject of education; a strategy aimed at researching the teacher personality and their activities. Hybrid learning organization calls for a teacher’s ability to use research strategies and analysis techniques as a key element in finding the answers to the questions about what is happening, why it is happening, what could happen, what should be done. The article was prepared within the fundamental research of State Assignment implementation on the topic “Professional competence of a teacher for the implementation of hybrid learning” (Supplementary Agreement of the Ministry of Education of Russia and the Federal State Educational Institution of Higher Education “OMSPU” No. 073-03-2022-035/3 от 08.06.2022).
Monte Carlo Tree Search for Automatic Differential Characteristics Search: Application to SPECK

The search for differential characteristics on block ciphers is a difficult combinatorial problem. In this paper, we investigate the performances of an AI-originated technique, Single Player Monte-Carlo Tree Search (SP-MCTS), in finding good differential characteristics on ARX ciphers, with an application to the block cipher SPECK. In order to make this approach competitive, we include several heuristics, such as the combination of forward and backward searches, and achieve significantly faster results than state-of-the-art works that are not based on automatic solvers. We reach 9, 11, 13, 13 and 15 rounds for SPECK32, SPECK48, SPECK64, SPECK96 and SPECK128 respectively. In order to build our algorithm, we revisit Lipmaa and Moriai’s algorithm for listing all optimal differential transitions through modular addition, and propose a variant to enumerate all transitions with probability close (up to a fixed threshold) to the optimal, while fixing a minor bug in the original algorithm.
Browsing Behavioral Intent Prediction on Product Recommendation Pages of E-commerce Platform

User behavior data has always been the key for e-commerce platforms to make decisions and improve experience, especially when predicting users’ behavioral intent. Nowadays, the Product Recommendation Page (PRP) has played an increasingly significant role of e-commerce platforms with the popularity of recommendation systems. However, past research on predicting user behavioral intent across e-commerce platforms may not be applicable to PRPs, where users have different characteristics. In this research, users’ browsing behavioral intent of PRPs is studied and predicted. A large amount of user data of PRPs is collected and processed, and the corresponding dataset is built. After that, a user interest analysis method is proposed while five browsing intent prediction models are applied and compared. The method distinguishes users with different browsing interest degrees, and the models can better predict users’ browsing behavior intent within different interest groups. A validation experiment on the large-scale dataset shows that the proposed method can predict user browsing intent with a decent performance.
zkChain: A privacy‐preserving model based on zk‐SNARKs and hash chain for efficient transfer of assets

Abstract As a distributed public ledger technology in a peer‐to‐peer network, blockchain has been widely used in a variety of Internet interaction systems in recent years, such as market supervision, property rights protection, and digital identity. However, in blockchain environment, all historical transaction data are open and transparent, and adversaries may illegally access private transaction data, which makes privacy attacks a core issue that hinders the popularization and application of blockchain. Different privacy‐preserving technologies such as zerocoin and zerocash have been proposed. However, the large amount of proofs and the slow verification speed hinder the effective application of zero‐knowledge proofs (ZKPs) in blockchain. This article proposes a privacy‐preserving model based on zk‐SNARKs and hash chain for efficient transfer of assets, called zkChain. Different from traditional privacy‐preserving schemes, this scheme constructs a smaller proof based on hash chain, and the verification speed of the proof is faster. This scheme constructs ZKP based on the one‐way hash function and zk‐SNARKs. The proof is publicly recorded in the ledger after being verified by miners, and users use the proof to send privacy‐preserving transactions. In the above process, the zkChain scheme can achieve effective privacy protection of account balance and account address. We evaluated the computational and storage costs of the zkChain scheme based on libsnark, and establish a test network to evaluate the processing performance of the zkChain scheme.
Diversity in Monographs: Selectors, Acquisitions, Publishers, and Vendors

In 2020 the University of Kansas Libraries began a year-long diversity audit of the circulating monograph collection. The study, which utilized the checklist method evaluated the Libraries’ holdings based on a curated list of award winning and nominated titles. In addition to determining if specific titles were part of the collection, the study also sought to learn how these books were acquired. The study found that the library owned 60% of the 6,671 titles checked and of those with a known acquisitions method, 59% came via an approval plan. Titles and publishers not profiled by GOBI were significantly less likely to be in the collection as they were not included in the approval plan or highlighted by a slip notification. The Libraries’ reliance on GOBI and efficient and streamlined selection and acquisition processes has resulted in a less diverse collection.
A new hybrid framework based on deep neural networks and JAYA optimization algorithm for feature selection using SVM applied to classification of acute lymphoblastic Leukaemia

ABSTRACTABSTRACTIdentification of Acute lymphoblastic leukemia (ALL) in microscopic images is one of the most challenging tasks in medical image analysis. Despite a wide variety of image processing and deep learning techniques, the task of extracting features from ALL images and selecting the proper features from these redundant and high-dimensional feature space set, and then detecting ALL cells is still a complex issue. In this study, we present a new hybrid two-layer framework to construct an appropriate subset of features. In the first layer of the proposed method, image segmentation is implemented using an improved modified First-Spike-based approach integrated with a Gaussian function. Then a developed deep residual architecture is employed to extract the features. In the second layer, a powerful and reliable meta-heuristic algorithm known as the JAYA optimization algorithm is adopted for feature selection due to its computational efficiency. Finally, a support vector machine (SVM) is used to classify ALL images. To show the effectiveness of the proposed model, it is applied on microscopic images of blood samples from ALL images (ALL-IDB) and ISBI-2019 C-NMC dataset. The results show the superiority of the model to be an appropriate choice for future biomedical imaging tasks.KEYWORDS: LeukemiaJAYA algorithmdeep learningclassificationresidual learning Disclosure statementNo potential conflict of interest was reported by the author(s).Data avaliabilityAll code and data required to reproduce the experiments and results are available at https://github.com/alinowshad/DeepLeukemiaNet.Additional informationNotes on contributorsSaeed FallahiDr. Saeed Fallahi, he received his B.Sc. degree in Mathematics from Mazandaran University in 2007. He received his M.Sc. and Ph.D. degree in applied Mathematics (Optimization) from Guilan University in 2010 and 2015. He is currently working as an Assistant Professor in the Department of Mathematics and Computer Science at Salman Farsi University of Kazerun, Iran. His research interests include Optimization, Neural Network, Machine Learning, Mathematical programming and Operations research. Email: saeedf808@gmail.com, s.fallahi@kazerunsfu.ac.ir
Educational Module for Studying PC Architecture Using 3D Technologies

This paper presents the 3D project that can be used in Education for visual/distance learning of students in the course “Computer Architecture”. The paper presents the use of 3D technologies in the educational process: students can independently assemble / disassemble and connect components in a virtual format, which is fully consistent with the real process. Each component module is accompanied by a description.