Reviewerly/exHarmony huggingface dataset:
Secure Medical Data on Cloud storage via DNA Homomorphic Encryption technique

Authors:
raw_author_name: Qiong Liu
raw_affiliation_string: Medical Support Center, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan, 572013, China
raw_author_name: Feng Zhou
raw_affiliation_string: Medical Support Center, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan, 572013, China
raw_author_name: Han Chen
raw_affiliation_string: Medical Support Center, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan, 572013, China

Abstract: Cloud computing offers considerable flexibility and financial savings to data owners for outsourcing their complicated data management systems from local sites to the commercial public cloud. However, an effective encryption scheme is needed to protect the private medical data of the user in the cloud without leaking it. Therefore, in this paper an efficient Deoxyribonucleic Acid Homomorphic Encryption algorithm (DNA-HE) has been proposed to encrypt and store the medical images in the cloud securely. Initially, the data is mapped to DNA sequences and then encrypted using homomorphic encryption. The key for the homomorphic algorithm is generated using Rider Optimization Technique which ensures security. It acts as a double encryption technique. The same procedure tends to decrypt the encrypted data. The efficiency of the proposed technique is assessed employing several parameters such as execution time, encryption time, and decryption time. The experimental results shows that the proposed DNA-HE system reduces encryption time of 10%, 20%, and 35% than OHE, HE and ECC algorithms.
Radiation-training system with a custom survey-meter mock-up in a browser-based mixed reality environment

Authors:
raw_author_name: Toshioh Fujibuchi
raw_affiliation_string: Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
raw_author_name: Kosuke Kaneko
raw_affiliation_string: Robert T.Huang Entrepreneurship Center, Kyushu University, Fukuoka, 819-0395, Japan
raw_author_name: Yoshihiro Okada
raw_affiliation_string: Innovation Center for Educational Resources of Kyushu University Library, Kyushu University, Fukuoka, 819-0395, Japan

Abstract: Training for radiation protection and control requires a visual understanding of radiation, which cannot be perceived by the human senses. Trainees must also master the effective use of measuring instruments. Traditionally, such training has exposed trainees to radiation sources. Here, we present a novel e-training strategy that enables safe, exposure-free handling of a radiation measuring tool called a survey meter. Our mixed reality radiation-training system merges the physical world with a digital one. Collaborating with a mixed reality headset (HoloLens 2), this system constructs a mock-up of a survey meter in real-world space. The HoloLens 2 employs a browser-based application to visualize radiation and to simulate/share the use of the survey meter, including its physical movements. To provide a dynamic learning experience, the system adjusts the survey-meter mock-up readings according to the operator's movements, distance from the radiation source, the response time of survey meter, and shielding levels. Through this approach, we expect that trainees will acquire practical skills in interpreting survey-meter readings and gain a visual understanding of radiation in real-world situations.
Robust sequential fusion Kalman estimators with asymptotic equivalence and stability for networked uncertain sensor systems

Authors:
raw_author_name: Chenjian Ran
raw_affiliation_string: Electronic Engineering College, Heilongjiang University, Harbin, 150080, People’s Republic of China
raw_author_name: Yuan Gao
raw_affiliation_string: Electronic Engineering College, Heilongjiang University, Harbin, 150080, People’s Republic of China
raw_author_name: Zili Deng
raw_affiliation_string: Electronic Engineering College, Heilongjiang University, Harbin, 150080, People’s Republic of China

Abstract: For networked uncertain sensor systems (NUSSs) with an improved unified sensor model with “hard” and “soft” sensors including five uncertainties plus two-step random measurement delays and colored measurement noises, applying the block matrix inversion formula, a new sequential inverse covariance fusion (SICF) approach is presented. It can sequentially compute the global inverse covariance according to the time order of the received data, but need not wait until all data are received. The corresponding minimax robust time-varying and steady-state sequential fusion (SF) Kalman estimators (predictor, filter, and smoother) are presented in the sense that their actual error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. They have lower computation complexities and higher accuracies equivalent to ones of the robust optimal batch-fusion (BF) Kalman estimators, and are suitable for real-time applications. Their asymptotic equivalence, absolute asymptotic stability, and accuracy relations are proved. Their strict complexity analysis is given. They constitute a novel universal robust SF estimation, convergence and stability theory for NUSSs. One simulation example applied to the uncertain ARMA signal processing verifies the effectiveness of the proposed approach and theory.
Simplicity in joy and detail in anger: Intertwining effect of cognitive and affective review disposition on review helpfulness

Authors:
raw_author_name: Yicheng Zhang
raw_affiliation_string: School of Management, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Xinqi Zhao
raw_affiliation_string: School of Management, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Ya Zhou
raw_affiliation_string: School of Management, Xiamen University, Xiamen, China

Abstract: Review length and readability, are cognitive dispositions of reviews supposed to reflect diagnostic content and lead to favorable evaluation of review helpfulness. However, underlying these two cognitive dispositions of reviews, are discrepancies that make it difficult for a helpful review to simultaneously satisfy both of them. To resolve the discrepancies, this present study, drawn on cognitive tuning theory, proposes and validates the intertwining effect of cognitive dispositions (length and readability) and affective dispositions (joy and anger expression) in judging review helpfulness. Based on analysis of heterogeneous hotel review data, our findings indicate joy expression in reviews, decreased the positive impact of review length on review helpfulness, while enhancing the positive impact of review readability on helpfulness. Anger expression in reviews, shows reverse effects, enhancing the positive impact of review length on helpfulness. Our findings contribute to an in-depth understanding of review characteristics and review helpfulness judgment.
Cross-scale Vision Transformer for crowd localization

Authors:
raw_author_name: Shuang Liu
raw_affiliation_string: Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, 300387, China
raw_author_name: Yu Lian
raw_affiliation_string: Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, 300387, China
raw_author_name: Zhong Zhang
raw_affiliation_string: Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, 300387, China
raw_author_name: Baihua Xiao
raw_affiliation_string: The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
raw_author_name: Tariq S. Durrani
raw_affiliation_string: Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow Scotland, UK

Abstract: Crowd localization can provide the positions of individuals and the total number of people, which has great application value for security monitoring and public management, meanwhile it meets the challenges of lighting, occlusion and perspective effect. In recent times, Transformer has been applied in crowd localization to overcome these challenges. Yet such kind of methods only consider to integrate the multi-scale information once, which results in incomplete multi-scale information fusion. In this paper, we propose a novel Transformer network named Cross-scale Vision Transformer (CsViT) for crowd localization, which simultaneously fuses multi-scale information during both the encoder and decoder stages and meanwhile building the long-range context dependencies on the combined feature maps. To this end, we design the multi-scale encoder to fuse the feature maps of multiple scales at corresponding positions so as to obtain the combined feature maps, and meanwhile design the multi-scale decoder to integrate the tokens at multiple scales when modeling the long-range context dependencies. Furthermore, we propose Multi-scale SSIM (MsSSIM) loss to adaptively compute head regions and optimize the similarity at multiple scales. Specifically, we set the adaptive windows with different scales for each head and compute the loss values within these windows so as to enhance the accuracy of the predicted distance transform map. We perform comprehensive experiments on five public datasets, and the results obtained validate the effectiveness of our method.
Data Analytics for Imbalanced Dataset

Authors:
raw_author_name: Madhura Prabha R
raw_author_name: Sasikala S

Abstract: The primary issue in real-time big data classification is imbalanced datasets. Even though we have many balancing techniques to reduce imbalance ratio which is not suitable for big data that has scalability issues. This study is envisioned to explore different balancing techniques with experimental study. We tried comparing the effectiveness of various balancing strategies, including cutting-edge approaches for severely unbalanced data from online repositories. Here we apply SMOTE, SMOTE ENN and SMOTE Tomek balancing algorithms for dermatology, wine quality and diabetes datasets. After balancing the dataset, the balanced dataset is classified with AdaBoost and random forest algorithms. On three datasets, the outcomes show that the classification algorithm with the balancing technique improves the classification performance for imbalanced datasets. Experiment results showed that the SMOTE ENN technique produces higher classification with accuracy than the SMOTE and SMOTE Tomek techniques. The findings are analyzed with other factors like execution time and scalability. Though SMOTE Tomek produces 1.0 for a few datasets, its execution time is longer than SMOTE ENN. Therefore, SMOTE ENN with random forest classification produces 1.0 accuracy for all three datasets with less execution time. This experimental study analyses to create a novel ensemble technique for balancing highly imbalanced data.
Deepfake Detection using Deep Feature Stacking and Meta-learning

Authors:
raw_author_name: Sk Mohiuddin
raw_affiliation_string: Department of Computer Science, Asutosh College, Kolkata, 700026, India
raw_author_name: Samir Malakar
raw_affiliation_string: Department of Computer Science, UiT The Arctic University of Norway, Tromsø, 9019, Norway
raw_author_name: Erik Cuevas
raw_affiliation_string: Departamento de Electrónica, Universidad de Guadalajara, C.P, 44430, Mexico
raw_author_name: Ram Sarkar
raw_affiliation_string: Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

Abstract: <h2>Abstract</h2> Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to detect deepfake has become a pressing need. This paper aims to address the problem of deepfake detection by identifying deepfake forgeries in video sequences. In this paper, a solution to the said problem is presented, which at first uses a stacking based ensemble approach, where features obtained from two popular deep learning models, namely Xception and EfficientNet-B7, are combined. Then by selecting a near-optimal subset of features using a ranking based approach, the final classification is performed to classify real and fake videos using a meta-learner, called multi-layer perceptron. In our experimentation, we have achieved an accuracy of 96.33% on Celeb-DF (V2) dataset and 98.00% on the FaceForensics++ dataset using the meta-learning model both of which are higher than the individual base models. Various types of experiments have been conducted to validate the robustness of the current method.
Fuzzy Logic Controller for UAV With Gains Optimized Via Genetic Algorithm

Authors:
raw_author_name: Omar Rodríguez-Abreo
raw_affiliation_string: Space Robotics Laboratory, Department of Systems Engineering and Automation, Universidad de Málaga, C/Ortiz Ramos s/n, 29071 Málaga, Spain.
raw_author_name: Juvenal Rodríguez-Reséndiz
raw_affiliation_string: Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico.
raw_author_name: A. García-Cerezo
raw_affiliation_string: Space Robotics Laboratory, Department of Systems Engineering and Automation, Universidad de Málaga, C/Ortiz Ramos s/n, 29071 Málaga, Spain.
raw_author_name: Jose R. Garcia-Martinez
raw_affiliation_string: Facultad de Ingeniería en Electrónica y Comunicaciones, Universidad Veracruzana, Poza Rica, Ver. 93390, Mexico.

Abstract: A gains optimizer of a fuzzy controller system for an Unmanned Aerial Vehicle (UAV) based on a metaheuristic algorithm is developed in the present investigation. The contribution of the work is the adjustment by the Genetic Algorithm (GA) to tune the gains at the input of a fuzzy controller. First, a typical fuzzy controller was modeled, designed, and implemented in a mathematical model obtained by Newton-Euler methodology. Subsequently, the control gains were optimized using a metaheuristic algorithm. The control objective is that the UAV consumes the least amount of energy. With this basis, the Genetic Algorithm finds the necessary gains to meet the design parameters. The tests were performed using the Matlab-Simulink environment. The results indicate an improvement, reducing the error in tracking trajectories from 30% in some tasks and following trajectories that could not be completed without a tuned controller in other tasks.
A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

Authors:
raw_author_name: None Sibi Shaji
raw_affiliation_string: School of Computational Sciences & IT, Garden City University, Bangalore, India
raw_author_name: None Balasubramanian Meenakshi Sundaram
raw_affiliation_string: Department of Computer Science & Engineering, New Horizon College of Engineering, Bangalore, India

Abstract: Fake profile identification on social media platforms is essential for preserving a reliable online community. Previous studies have primarily used conventional classifiers for fake account identification on social networking sites, neglecting feature selection and class balancing to enhance performance. This study introduces a novel multistage stacked ensemble classification model to enhance fake profile detection accuracy, especially in imbalanced datasets. The model comprises three phases: feature selection, base learning, and meta-learning for classification. The novelty of the work lies in utilizing chi-squared feature-class association-based feature selection, combining stacked ensemble and cost-sensitive learning. The research findings indicate that the proposed model significantly enhances fake profile detection efficiency. Employing cost-sensitive learning enhances accuracy on the Facebook, Instagram, and Twitter spam datasets with 95%, 98.20%, and 81% precision, outperforming conventional and advanced classifiers. It is demonstrated that the proposed model has the potential to enhance the security and reliability of online social networks, compared with existing models.
Constructions of correlation immnue S-boxes with high nonlinearity

Authors:
raw_author_name: Yanhan Ji
raw_affiliation_string: State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
raw_author_name: Zhuo Ma
raw_affiliation_string: State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
raw_author_name: Luyang Li
raw_affiliation_string: Xi'an University of Posts and Telecommunications, Xi'an 710121, China

Abstract: S-boxes play a central role role in the design of symmetric cipher schemes. For stream cipher applications, a S-box should satisfy several criteria such as high nonlinearity, balanceness, correlation immunity, and so on. In this paper, by using disjoint linear codes, a class of S-boxes possessing high nonlinearity and 1st-order correlation immunity is given. It is shown that the constructed correlation immune S-boxes can possess currently best known nonlinearity, which is confirmed by the example 1st-order correlation immune (12, 3) S-box with nonlinearity 2000. In addition, two other frameworks concerning the criteria of balanced and resiliency are obtained respectively.
The design and use of a digital PMS, and the emergence of gaming logic: the experience of the Italian Ministry of Culture

Authors:
raw_author_name: Deborah Agostino

Abstract: The analysis of digital transformation processes by governments has increasingly attracted attention from academics and practitioners, but it has been less frequently investigated from an accounting perspective. This chapter investigates the complex process of designing and using a digital performance measurement system (PMS) in a central government, empirically exploring the case of the Italian Ministry of Culture, which enacted a reform to provide central and peripheral units with a system to monitor the reputation of state museums, leveraging real-time user-generated data (UGD). The study shows that the design and use of a digital PMS is characterised by the complexity of choosing and interpreting the algorithm to analyse UGD, which may stimulate gaming logic by the actors who are held accountable through the digital PMS. These findings contribute to a broader discussion on the accounting and accountability implications of adopting a digital PMS in the public sector.
Copyright and COVID

Authors:
raw_author_name: Sean M. Flynn

Abstract: The rise of the COVID-19 pandemic revived a focus of scholars and policy makers on the issue of patents and access to medicines (and vaccines). But the COVID-19 pandemic displayed as well how other intellectual property rights, including copyright and trade secrets, may also complicate public health measures. This chapter documents many instances where non-patent forms of intellectual property created barriers to the global scale-up of access to vaccines, treatments, and the ability to contain the COVID-19 virus. It concludes by explaining some of the existing flexibilities in the multilateral intellectual property system that may be used to respond to these problems.
Cryptography in cloud: An in-depth investigation into encryption mechanisms for safeguarding cloud-based data

Authors:
raw_author_name: None Malathi P

Abstract: Cloud computing is one of the most significant computing models, which provides easy access of resources. It is an alternative for costlier data framework and networking models. The services are provided to the data users based on their requirements. More industries are depending on the cloud, because of its fast computations and robust nature. However, there are several security concerns and threats in cloud storages. Moreover, large amounts of data are shared between people and organizations through the cloud. The data owner doesn’t have control over the data in the cloud. To address the security and privacy issues in the cloud, this thesis aims at overcoming this trade-off, while considering security.
AI-Assisted Programming Tasks Using Code Embeddings and Transformers

Authors:
raw_author_name: Sotiris Kotsiantis
raw_affiliation_string: Department of Mathematics, University of Patras, 265 04 Patras, Greece
raw_author_name: Vassilios Verykios
raw_affiliation_string: School of Science and Technology, Hellenic Open University, 263 35 Patras, Greece
raw_author_name: Manolis Tzagarakis
raw_affiliation_string: Department of Economics, University of Patras, 265 04 Patras, Greece

Abstract: This review article provides an in-depth analysis of the growing field of AI-assisted programming tasks, specifically focusing on the use of code embeddings and transformers. With the increasing complexity and scale of software development, traditional programming methods are becoming more time-consuming and error-prone. As a result, researchers have turned to the application of artificial intelligence to assist with various programming tasks, including code completion, bug detection, and code summarization. The utilization of artificial intelligence for programming tasks has garnered significant attention in recent times, with numerous approaches adopting code embeddings or transformer technologies as their foundation. While these technologies are popular in this field today, a rigorous discussion, analysis, and comparison of their abilities to cover AI-assisted programming tasks is still lacking. This article discusses the role of code embeddings and transformers in enhancing the performance of AI-assisted programming tasks, highlighting their capabilities, limitations, and future potential in an attempt to outline a future roadmap for these specific technologies.
A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8

Authors:
raw_author_name: Haoyu Wang
raw_affiliation_string: Graduate School, Space Engineering University, Beijing 101416, China
raw_author_name: Haitao Yang
raw_affiliation_string: Space Engineering University, Beijing 101416, China
raw_author_name: Hang Chen
raw_affiliation_string: Space Engineering University, Beijing 101416, China; University of Lorraine, CNRS, CRAN UMR 7039, 54000 Nancy, France
raw_author_name: Jinyu Wang
raw_affiliation_string: Graduate School, Space Engineering University, Beijing 101416, China
raw_author_name: Yifan Xu
raw_affiliation_string: Graduate School, Space Engineering University, Beijing 101416, China

Abstract: Aiming at the characteristics of remote sensing images such as a complex background, a large number of small targets, and various target scales, this paper presents a remote sensing image target detection algorithm based on improved YOLOv8. First, in order to extract more information about small targets in images, we add an extra detection layer for small targets in the backbone network; second, we propose a C2f-E structure based on the Efficient Multi-Scale Attention Module (EMA) to enhance the network’s ability to detect targets of different sizes; and lastly, Wise-IoU is used to replace the CIoU loss function in the original algorithm to improve the robustness of the model. Using our improved algorithm for the detection of multiple target categories in the DOTAv1.0 dataset, the mAP@0.5 value is 82.7%, which is 1.3% higher than that of the original YOLOv8 algorithm. It is proven that the algorithm proposed in this paper can effectively improve target detection accuracy in remote sensing images.