Reviewerly/exHarmony huggingface dataset:
Feature Selection Using New Version of V-Shaped Transfer Function for Salp Swarm Algorithm in Sentiment Analysis

(1) Background: Feature selection is the biggest challenge in feature-rich sentiment analysis to select the best (relevant) feature set, offer information about the relationships between features (informative), and be noise-free from high-dimensional datasets to improve classifier performance. This study aims to propose a binary version of a metaheuristic optimization algorithm based on Swarm Intelligence, namely the Salp Swarm Algorithm (SSA), as feature selection in sentiment analysis. (2) Methods: Significant feature subsets were selected using the SSA. Transfer functions with various types of the form S-TF, V-TF, X-TF, U-TF, Z-TF, and the new type V-TF with a simpler mathematical formula are used as a binary version approach to enable search agents to move in the search space. The stages of the study include data pre-processing, feature selection using SSA-TF and other conventional feature selection methods, modelling using K-Nearest Neighbor (KNN), Support Vector Machine, and Naïve Bayes, and model evaluation. (3) Results: The results showed an increase of 31.55% to the best accuracy of 80.95% for the KNN model using SSA-based New V-TF. (4) Conclusions: We have found that SSA-New V3-TF is a feature selection method with the highest accuracy and less runtime compared to other algorithms in sentiment analysis.
Analyzing Health Data Breaches: A Visual Analytics Approach

This research studies the occurrence of data breaches in healthcare provider settings regarding patient data. Using visual analytics and data visualization tools, we study the distribution of healthcare breaches by state. We review the main causes and types of breaches, as well as their impact on both providers and patients. The research shows a range of data breach victims. Network servers are the most popular location for common breaches, such as hacking and information technology (IT) incidents, unauthorized access, theft, loss, and improper disposal. We offer proactive recommendations to prepare for a breach. These include, but are not limited to, regulatory compliance, implementing policies and procedures, and monitoring network servers. Unfortunately, the results indicate that the probability of data breaches will continue to rise.
Augmented and Virtual Reality to Enhance the Didactical Experience of Technological Heritage Museums

The way that the new generations approach cultural contents changed dramatically. The audiovisual language substituted traditional media. Museums face now an important challenge to survive as cultural referents in this new paradigm: the introduction of new audiovisual languages in their exhibitions and the provision of attractive online content. The work presents a case study of the use of augmented reality and virtual reality (AR/VR) in a technological heritage museum, with a double approach: on the one hand the development of AR to enhance the real visit to the museum; and on the other, the provision of VR to ease online visits to the museum for those that do not want to or cannot visit it. The results show that young visitors massively appreciate the use of these technologies. Using AR contents also contributes to the preservation of the original artifacts without damage. Furthermore, multimedia content provides some contextual information, improving the learning experience. Regarding the VR application, it is thought as a complement to the AR experience. It was developed as a virtual reproduction of the museum visit that can be experienced from any location, thus contributing to a higher diffusion of the museum contents.
A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms

In the field of data mining, clustering has shown to be an important technique. Numerous clustering methods have been devised and put into practice, and most of them locate high-quality or optimum clustering outcomes in the field of computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. This research provides a modern, thorough review of both classic and cutting-edge clustering methods. The taxonomy of clustering is presented in this review from an applied angle and the compression of some hierarchical and partitional clustering algorithms with various parameters. We also discuss the open challenges in clustering such as computational complexity, refinement of clusters, speed of convergence, data dimensionality, effectiveness and scalability, data object representation, evaluation measures, data streams, and knowledge extraction; scientists and professionals alike will be able to use it as a benchmark as they strive to advance the state-of-the-art in clustering techniques.
Synthetic ID Card Image Generation for Improving Presentation Attack Detection

Currently, it is ever more common to access online services for activities which formerly required physical attendance. From banking operations to visa applications, a significant number of processes have been digitised, especially since the advent of the COVID-19 pandemic, requiring remote biometric authentication of the user. On the downside, some subjects intend to interfere with the normal operation of remote systems for personal profit by using fake identity documents, such as passports and ID cards. Deep learning solutions to detect such frauds have been presented in the literature. However, due to privacy concerns and the sensitive nature of personal identity documents, developing a dataset with the necessary number of examples for training deep neural networks is challenging. This work explores three methods for synthetically generating ID card images to increase the amount of data while training fraud-detection networks. These methods include computer vision algorithms and Generative Adversarial Networks. Our results indicate that databases can be supplemented with synthetic images without any loss in performance for the print/scan Presentation Attack Instrument Species (PAIS) and a loss in performance of 1% for the screen capture PAIS.
A Medical Image Visualization Technique Assisted with AI-Based Haptic Feedback for Robotic Surgery and Healthcare

A lesson learned during the pandemic is that social distancing saves lives. As it was shown recently, the healthcare industry is structured in a way that cannot protect medical staff from possible infectious diseases, such as COVID-19. Today’s healthcare services seem anachronistic and not convenient for both doctors and patients. Although there have been several advances in recent years, especially in developed countries, the need for a holistic change is imperative. Evidently, future technologies should be introduced in the health sector, where Virtual Reality, Augmented Reality, Artificial Intelligence, and Tactile Internet can have vast applications. Thus, the healthcare industry could take advantage of the great evolution of pervasive computing. In this paper, we point out the challenges from the current visualization techniques and present a novel visualization technique assisted with haptics which is enhanced with artificial intelligent algorithms in order to offer remote patient examination and treatment through robotics. Such an approach provides a more detailed method of medical image data visualization and eliminates the possibility of diseases spreading, while reducing the workload of the medical staff.
Domain consistency regularization for unsupervised multi-source domain adaptive classification

Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA), domain shift in MUDA exists not only between the source and target domains but also among multiple source domains. Most existing MUDA algorithms focus on extracting domain-invariant representations among all domains whereas the task-specific decision boundaries among classes are largely neglected. In this paper, we propose an end-to-end trainable network that exploits domain Consistency Regularization for unsupervised Multi-source domain Adaptive classification (CRMA). CRMA aligns not only the distributions of each pair of source and target domains but also that of all domains. For each pair of source and target domains, we employ an intra-domain consistency to regularize a pair of domain-specific classifiers to achieve intra-domain alignment. In addition, we design an inter-domain consistency that targets joint inter-domain alignment among all domains. To address different similarities between multiple source domains and the target domain, we design an authorization strategy that assigns different authorities to domain-specific classifiers adaptively for optimal pseudo label prediction and self-training. Extensive experiments show that CRMA tackles unsupervised domain adaptation effectively under a multi-source setup and achieves superior adaptation consistently across multiple MUDA datasets.
Blockchain-Based Decentralized Application: A Survey

Blockchain-based decentralized applications (DApp) draw more attention with the increasing development and wide application of blockchain technologies. A wealth of funds are invested into the crowd-funding of various types of DApp. As reported in August 2022, there are more than 5,000 DApps with more than 1.67 million daily Unique Active Wallets (users). However, the definition, architectures, and classifications of the DApps are still not cleared up till now. This survey aims to provide a comprehensive overview of DApps for further research. First, the definitions and typical architectures of DApps are presented. Then we collect 3,118 popular DApps and categorize them into different types, and summarize their typical advantages and challenges. Finally, we provide an overview of the recent research problems of DApps from the perspectives of economics, security, and performance and then figure out promising research opportunities in the future.
Standard Evolution of 5G-Advanced and Future Mobile Network for Extended Reality and Metaverse

eXtend Reality (XR) and Metaverse are important service scenarios to be supported by 5G-Advanced (5G-A) and future mobile networks. However, there are still many challenges and gaps for 5G-A to guarantee the immersive experience of XR and Metaverse. This article analyzes the challenges to support XR in 5G-A, e.g., lossless audio compression transmission, full-view high-resolution video transmission, and synchronization between multi-modal transmission. Then an overall system architecture of 5G-A for enabling XR is proposed with the concept of application-network coordination. And the latest advances of 3GPP standard on network architecture enhancements for XR are overviewed, including network-assistant service optimization, Quality of Service (QoS) enhancements, and power saving optimization. Finally, the future standard and research directions for Metaverse are analyzed, including integrated architecture with communication-sensing-computing, immersive experience enhancements for new media types, intelligent network, security and privacy aspects.
Performance Enhancement of a Spin-Wave-Based Reservoir Computing System Utilizing Different Physical Conditions

We numerically study how to enhance reservoir computing performance by thoroughly extracting the spin-wave device potential for higher-dimensional information generation. The reservoir device has a 1-input exciter and 120-output detectors on the top of a continuous magnetic garnet film for spin-wave transmission. For various nonlinear and fading-memory dynamic phenomena distributing in the film space, small in-plane magnetic fields are used to prepare stripe domain structures and various damping constants at the film sides and bottom are explored. The ferromagnetic resonant frequency and relaxation time of spin precession clearly characterizes the change in spin dynamics with the magnetic field and damping constant. The common input signal for reservoir computing is a 1-GHz cosine wave with random 6-valued amplitude modulation. A basic 120-dimensional reservoir output vector is obtained from time-series signals at the 120-output detectors under each of three magnetic field conditions. Then, 240- and 360-dimensional reservoir output vectors are also constructed by concatenating two and three basic ones, respectively. In nonlinear autoregressive moving average (NARMA) prediction tasks, the computational performance is enhanced as the dimension of the reservoir output vector becomes higher and a significantly low prediction error is achieved for the tenth-order NARMA task using the 360-dimensional vector and optimum damping constant. The results are clear evidence that the collection of diverse output signals efficiently increases the dimensionality of the integrated reservoir state vector (i.e. reservoir-state richness) and thereby contributes to high computational performance. This paper demonstrates that performance enhancement through various configuration settings is a practical approach for on-chip reservoir computing devices with small numbers of real output nodes.
The effect of using smart e-learning app on the academic achievement of eighth-grade students

The purpose of this study is to determine whether smart applications can help students in the United Arab Emirates (UAE) acquire higher levels of proficiency in scientific ideas. To do this, 120 eighth-graders (the last year of the second cycle) from schools in UAE city of Al Ain participated in a quasi-experimental study. These students were divided into two equal groups at random: a test group that studied with the aid of technology, and a control group that learned through the conventional classroom technique. A test on scientific concepts and intelligent applications based on the educational program served as study aids. The findings of the scientific-concept accomplishment test revealed statistically significant differences between the mean scores for the two groups, favoring the experimental group. As a result of the type of smart application used, particularly Alef platform in comparison to the Boclips and Connect platforms, the results also showed statistically significant differences between the mean scores for the experimental group students’ achievement level regarding scientific concepts. The researcher advises incorporating smart applications into the educational process in light of these findings, particularly when instructing scientific topics.
Enhanced Pre-Trained Xception Model Transfer Learned for Breast Cancer Detection

Early detection and timely breast cancer treatment improve survival rates and patients’ quality of life. Hence, many computer-assisted techniques based on artificial intelligence are being introduced into the traditional diagnostic workflow. This inclusion of automatic diagnostic systems speeds up diagnosis and helps medical professionals by relieving their work pressure. This study proposes a breast cancer detection framework based on a deep convolutional neural network. To mine useful information about breast cancer through breast histopathology images of the 40× magnification factor that are publicly available, the BreakHis dataset and IDC(Invasive ductal carcinoma) dataset are used. Pre-trained convolutional neural network (CNN) models EfficientNetB0, ResNet50, and Xception are tested for this study. The top layers of these architectures are replaced by custom layers to make the whole architecture specific to the breast cancer detection task. It is seen that the customized Xception model outperformed other frameworks. It gave an accuracy of 93.33% for the 40× zoom images of the BreakHis dataset. The networks are trained using 70% data consisting of BreakHis 40× histopathological images as training data and validated on 30% of the total 40× images as unseen testing and validation data. The histopathology image set is augmented by performing various image transforms. Dropout and batch normalization are used as regularization techniques. Further, the proposed model with enhanced pre-trained Xception CNN is fine-tuned and tested on a part of the IDC dataset. For the IDC dataset training, validation, and testing percentages are kept as 60%, 20%, and 20%, respectively. It obtained an accuracy of 88.08% for the IDC dataset for recognizing invasive ductal carcinoma from H&amp;E-stained histopathological tissue samples of breast tissues. Weights learned during training on the BreakHis dataset are kept the same while training the model on IDC dataset. Thus, this study enhances and customizes functionality of pre-trained model as per the task of classification on the BreakHis and IDC datasets. This study also tries to apply the transfer learning approach for the designed model to another similar classification task.
WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery

Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks. Nevertheless, the huge data volume along with stringent requirements on latency and reliability under limited bandwidth resources makes untethered wireless VR delivery increasingly challenging. Such bottlenecks, therefore, motivate this work to seek the potential of using semantic communication, a new paradigm that promises to significantly ease the resource pressure, for efficient VR delivery. To this end, we propose a novel framework, namely WIreless SEmantic deliveRy for VR (WiserVR), for delivering consecutive 360{\deg} video frames to VR users. Specifically, deep learning-based multiple modules are well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery. Among them, we dedicatedly develop a concept of semantic location graph and leverage the joint-semantic-channel-coding method with knowledge sharing to not only substantially reduce communication latency, but also to guarantee adequate transmission reliability and resilience under various channel states. Moreover, implementation of WiserVR is presented, followed by corresponding initial simulations for performance evaluation compared with benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of WiserVR.
A rule-based deep fuzzy system with nonlinear fuzzy feature transform for data classification

In today's fuzzy community, the blending of fuzzy model and deep learning has become one hot topic for the development of more sophisticated and high-powered fuzzy systems. In this study, a rule-based deep fuzzy system with nonlinear fuzzy feature transform for data classification named by NFFT-DFSC is proposed, borrowing the “hierarchically stacked thought” originated from deep learning. Firstly, two fuzzy models with nonlinear fuzzy feature transform function and with decision-making function, and their neuro-fuzzy network implementations are proposed. Subsequently, we stack multiple fuzzy models with nonlinear fuzzy feature transform function and the fuzzy model with decision-making function together in a cascade way, which results in the formation of NFFT-DFSC. Here the stacked fuzzy models with nonlinear fuzzy feature transform function snoops and transforms features of raw data continuously to finally obtain the high-level latent fuzzy features, while the fuzzy model with decision-making function completes classification by exploring the multiple prototypes used to represent the high-level latent fuzzy features of each class. Besides, batch normalization operation and mini-batch gradient descent optimization rooted from the training of deep neural networks are also used to enhance the generalization of NFFT-DFSC and learn its parameters in end-to-end manner, respectively. Experiments between NFFT-DFSCs with three nonlinear mapping functions and 15 benchmark classifiers involving gaussian kernel support vector machine (GSVM), Takagi-Sugeno-Kang classifier (TSK), deep neural network (DNN) and state-of-the-art deep TSK ones on synthetic datasets and nineteen diverse real-world datasets demonstrate that the proposed NFFT-DFSC is a more competitive classifier on classification accuracy.
A Unified Framework for Learned Sparse Retrieval

Learned sparse retrieval (LSR) is a family of first-stage retrieval methods that are trained to generate sparse lexical representations of queries and documents for use with an inverted index. Many LSR methods have been recently introduced, with Splade models achieving state-of-the-art performance on MSMarco. Despite similarities in their model architectures, many LSR methods show substantial differences in effectiveness and efficiency. Differences in the experimental setups and configurations used make it difficult to compare the methods and derive insights. In this work, we analyze existing LSR methods and identify key components to establish an LSR framework that unifies all LSR methods under the same perspective. We then reproduce all prominent methods using a common codebase and re-train them in the same environment, which allows us to quantify how components of the framework affect effectiveness and efficiency. We find that (1) including document term weighting is most important for a method's effectiveness, (2) including query weighting has a small positive impact, and (3) document expansion and query expansion have a cancellation effect. As a result, we show how removing query expansion from a state-of-the-art model can reduce latency significantly while maintaining effectiveness on MSMarco and TripClick benchmarks. Our code is publicly available (Code: https://github.com/thongnt99/learned-sparse-retrieval ).