Reviewerly/exHarmony huggingface dataset:
Detecting outliers in a univariate time series dataset using unsupervised combined statistical methods: A case study on surface water temperature

The surface water temperature is a vital ecological and climate variable, and its monitoring is critical. An extensive sensor network measures the ocean, but outliers pervade the monitoring data due to the sudden change in the water surface level. No single algorithm can identify the outlier efficiently. Hence, this work aims to propose and evaluate the performance of three statistical-based outlier detection algorithms for the water surface temperature: 1) the Standard Z-Score method, 2) the Modified Z-Score coupled with decomposition, and 3) the Exponential Moving Average with the Coupled Modified Z-Score and decomposition. A threshold was set to flag the outlier values. The models' performance was evaluated using the F-score method. Results showed that an increase in outlier detection might reduce the precision of identifying the actual outlier. Based on the results, the Exponential Moving Average with the Modified Z-Score gave the highest F-score value (= 0.83) compared to the other two individual methods. Therefore, this proposed algorithm is recommended to detect outliers efficiently in large surface water temperature datasets.
Blockchain enabled optimal Hopfield Chaotic Neural network based secure encryption technique for industrial internet of things environment

Industrial Internet of Things (IIoT) denotes a network of interlinked sensors, instruments, and other devices for industrial applications in the domains of manufacturing, logistics, transportation, etc. IIoT security is a major crucial research area for several applications. Image encryption techniques gained popularity in the recent years, thanks to increasing requirements for secure image transmission in IIoT environments. At the same time, conventional security solutions built for sensitive data protection are getting outdated in IIoT environment due to the participation of third party. Blockchain (BC) is one of the recent solutions used for security purpose which eliminates the involvement of a third party. With this motivation, the current research article presents a new BC-Enabled Shark Smell Optimization with Hopfield Chaotic Neural Network (SSO-HCNN) for secure encryption in IoT environment. The proposed SSO-HCNN model exploits a composite Chaotic Map (CM) which is integrated into staged logistic and tent maps to initially process the images and develop the variables needed for Arnold mapping. In addition, the SSO algorithm is developed with maximum PSNR and coefficient fitness function to select the optimum secret and public keys of the system amongst the random numbers. Besides, the diffusion phase utilizes HCNN to create a self-diffusion chaotic matrix whereas the jumbled image performs XOR operation using the keys to obtain the cipher image. In SSO-HCNN model, the cryptographic pixel value in the image is saved on BC thus guaranteeing the security and privacy of the images. To examine the superior performance of SSO-HCNN model over state-of-the-art methods, a set of simulations was conducted on benchmark test images. The simulation results of the proposed SSO-HCNN model were promising under different evaluation parameters.
Contrastive Graph Convolutional Networks with adaptive augmentation for text classification

Text classification is an important research topic in natural language processing (NLP), and Graph Neural Networks (GNNs) have recently been applied in this task. However, in existing graph-based models, text graphs constructed by rules are not real graph data and introduce massive noise. More importantly, for fixed corpus-level graph structure, these models cannot sufficiently exploit the labeled and unlabeled information of nodes. Meanwhile, contrastive learning has been developed as an effective method in graph domain to fully utilize the information of nodes. Therefore, we propose a new graph-based model for text classification named CGA2TC, which introduces contrastive learning with an adaptive augmentation strategy into obtaining more robust node representation. First, we explore word co-occurrence and document word relationships to construct a text graph. Then, we design an adaptive augmentation strategy for the text graph with noise to generate two contrastive views that effectively solve the noise problem and preserve essential structure. Specifically, we design noise-based and centrality-based augmentation strategies on the topological structure of text graph to disturb the unimportant connections and thus highlight the relatively important edges. As for the labeled nodes, we take the nodes with same label as multiple positive samples and assign them to anchor node, while we employ consistency training on unlabeled nodes to constrain model predictions. Finally, to reduce the resource consumption of contrastive learning, we adopt a random sample method to select some nodes to calculate contrastive loss. The experimental results on several benchmark datasets can demonstrate the effectiveness of CGA2TC on the text classification task.
The impact of COVID-19 on turnover intention among hotel employees: A moderated mediation model

This study examines the influence of COVID-19 event strength on the turnover intention of hotel employees by incorporating perceived operating performance and job insecurity as mediators and hotel size as a moderator. A moderated mediation model was employed to test the relationship between COVID-19 event strength and turnover intention. The study reveals that COVID-19 event strength might not significantly affect turnover intention through perceived operating performance, likely affecting job insecurity perception. We infer that such a finding might result from a series of policies implemented by authorities to prevent job insecurity perception of hotel employees during the COVID-19 pandemic. Furthermore, we also reveal that small- and middle-sized hotels mitigate the effect of job insecurity on turnover intention somewhat different from our expectations. We infer that most of the entrepreneurs and employees in such hotels are from the same town and unlikely to intensify the effect of job insecurity on turnover intention because of close friendships among them. This finding is closely related to cultural factors in China and has rarely been discussed in the existing literature.
Adaptive Dynamic Programming-Based Cooperative Motion/Force Control for Modular Reconfigurable Manipulators: A Joint Task Assignment Approach

This article develops a cooperative motion/force control (CMFC) scheme based on adaptive dynamic programming (ADP) for modular reconfigurable manipulators (MRMs) with the joint task assignment approach. By separating terms depending on local variables only, the dynamic model of the entire MRM system can be regarded as a set of joint modules interconnected by coupling torque. In addition, the Jacobian matrix, which reflects the interaction force of the MRM end-effector, can be mapped into each joint. Using this approach, both the motion and force tasks on the end-effector of the entire MRM system can be assigned to each joint module cooperatively. Then, by substituting the actual states of coupled joint modules with their desired ones, the norm-boundedness assumption on the interconnection of joint module can be relaxed. By using the measured input-output data of each joint module, a neural network (NN)-based robust decentralized observer, which guarantees the observation error to be asymptotically stable is established. An improved local value function is constructed for each joint module to reflect the interconnection. Then, the local Hamilton-Jacobi-Bellman equation is solved by constructing a local critic NN with a nested learning structure. Hereafter, the ADP-based CMFC is obtained by the assistance of force feedback compensation. Based on the Lyapunov stability analysis, the closed-loop MRM system is guaranteed to be uniformly ultimately bounded under the present ADP-based CMFC scheme. The simulation on a two-degree of freedom MRM system demonstrates the effectiveness of the present control approach.
AQ–ABS: Anti-Quantum Attribute-based Signature for EMRs Sharing with Blockchain

With the advancement of medical science, the implementation of Electronic Medical Records (EMRs) for enhancing the efficiency and reliability of healthcare services has become a widespread phenomenon. However, EMRs are stored in hospitals and medical institutions independently, leading to sharing challenges. Moreover, the highly sensitive EMRs are prone to be tampered with and abused, posing privacy and security threats. To address the aforementioned issues, we propose an Anti- Quantum Attribute-based Signature(AQ &#x2014; ABS) for Secure EMRs Sharing with Blockchain. Initially, We are the first ones to design an Attribute-based Signature (ABS) that can resist quantum attacks in E-health, called AQ &#x2014; ABS. Further, the owner and provider of the EMR encrypt and sign it via the AQ &#x2014; ABS scheme, then store EMR to a secure and distributed file storage system, i.e. Interplanetary File System (IPFS). Finally, the index hashes generated by IPFS and keywords are re-signed and stored in the consortium blockchain. Security analysis indicates that our proposed scheme fulfills the properties of signers&#x2019; anonymity, EMRs unforgeability, EMRs shareability, and fine-grained access control. Comprehensive experimental evaluation demonstrates that our proposed scheme performs low overhead as well as outperforms existing ABS and EMR systems.
WDM equipped universal linear optics for programmable neuromorphic photonic processors

Abstract Non-von-Neumann computing architectures and deep learning training models have sparked a new computational era where neurons are forming the main architectural backbone and vector, matrix and tensor multiplications comprise the basic mathematical toolbox. This paradigm shift has triggered a new race among hardware technology candidates; within this frame, the field of neuromorphic photonics promises to convolve the targeted algebraic portfolio along a computational circuitry with unique speed, parallelization, and energy efficiency advantages. Fueled by the inherent energy efficient analog matrix multiply operations of optics, the staggering advances of photonic integration and the enhanced multiplexing degrees offered by light, neuromorphic photonics has stamped the resurgence of optical computing brining a unique perspective in low-energy and ultra-fast linear algebra functions. However, the field of neuromorphic photonics has relied so far on two basic architectural schemes, i.e., coherent linear optical circuits and incoherent WDM approaches, where wavelengths have still not been exploited as a new mathematical dimension. In this paper, we present a radically new approach for promoting the synergy of WDM with universal linear optics and demonstrate a new, high-fidelity crossbar-based neuromorphic photonic platform, able to support matmul with multidimensional operands. Going a step further, we introduce the concept of programmable input and weight banks, supporting in situ reconfigurability, forming in this way the first WDM-equipped universal linear optical operator and demonstrating different operational modes like matrix-by-matrix and vector-by-tensor multiplication. The benefits of our platform are highlighted in a fully convolutional neural network layout that is responsible for parity identification in the MNIST handwritten digit dataset, with physical layer simulations revealing an accuracy of ∼94%, degraded by only 2% compared to respective results obtained when executed entirely by software. Finally, our in-depth analysis provides the guidelines for neuromorphic photonic processor performance improvement, revealing along the way that 4 bit quantization is sufficient for inputs, whereas the weights can be implemented with as low as 2 bits of precision, offering substantial benefits in terms of driving circuitry complexity and energy savings.
Challenges, experiments, and computational solutions in peer review

Improving the peer review process in a scientific manner shows promise.
Improving the Financial Security of National Health Insurance using Cloud-Based Blockchain Technology Application

Since Bitcoin's success, there has been a greater emphasis on researching the use of blockchain in a wide range of applications, such as agriculture, education, government, voting systems, etc. This generation of Blockchain applications is termed blockchain 3.0. Blockchain technology provides enhanced security, auditability, privacy, accountability, and many more features over a centralized system. Fraud in insurance is an international problem affecting all insurance subdomains in both developed and developing countries. Fraud, corruption, and many other data-related problems are threatening the financial security of the National Health Insurance Scheme (NHIS) in Ghana and if not attended to, the core aims of the scheme to provide universal healthcare services will not be realized. Consequently, the objective of this work is to design and implement a blockchain-based solution to protect the NHIS from plummeting financially. This paper presents the conceptual view of the proposed system, sequence and use case diagrams, data management framework, smart notification system, and smart claim processing system. The system was evaluated using the DeLone & McLean Information Systems Success Model. It was used to validate the behavioural aspect of the system. The study discovered a significant influence of information quality and user satisfaction. On the contrary, system quality seems to have an almost significant influence on the use and user satisfaction of the proposed system.
Integrating augmented reality in language learning: pre-service teachers’ digital competence and attitudes through the TPACK framework

Although the use of Augmented Reality (AR) in language learning has increased over the last two decades, there is still little research on the preparation of pre-service teachers as AR content creators. This paper focuses on analyzing the digital competence and attitudes of teacher candidates to integrate AR in the foreign language classroom. For this purpose, eighty-five college students were assigned into different teams to create their own AR-based projects which aimed at teaching English and content to young learners. The teacher candidates employed several software development kits (SDKs) to construct collaborative AR projects in a five-week period, including discursive and illustrative representations of the learning content. In this research based on a mixed method, quantitative and qualitative data were gathered trough AR project presentations and surveys encompassing two validated scales, the Technological Pedagogical Content Knowledge (TPACK) framework and the Augmented Reality Applications Attitudes Scale (ARAAS). The statistical data and qualitative findings revealed that the participants lacked practical knowledge on AR content creation and implementation in Education. The major problems were related to the TPK (Technological Pedagogical Knowledge) intersection since participants had been previously trained in AR technology just as recipients and not as content creators and educators.The online version contains supplementary material available at 10.1007/s10639-022-11123-3.
Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images

Breast cancer detection based on the deep learning approach has gained much interest among other conventional-based CAD systems as the conventional based CAD system's accuracy results seems to be inadequate. The convolution neural network, a deep learning approach, has emerged as the most promising technique for detecting cancer in mammograms. In this paper we delve into some of the CNN classifiers used to detect breast cancer by classifying mammogram images into benign, cancer, or normal class. Our study evaluated the performance of various CNN architectures such as AlexNet, VGG16, and ResNet50 by training some of them from scratch and some using transfer learning with pre-trained weights. The above model classifiers are trained and tested using mammogram images from the mini-DDSM dataset which is publicly available. The medical dataset contains limited samples of data due to low patient volume; this can lead to overfitting issue, so to overcome this limitation data augmentation process is applied. Rotation and zooming techniques are applied to increase the data volume. The validation strategy used here is 90:10 ratio. AlexNet showed an accuracy of 65 percent, whereas VGG16 and ResNet50 showed an accuracy of 65% and 61%, respectively when fine-tuned with pre-trained weights. VGG16 performed significantly worse when trained from scratch, whereas AlexNet outperformed others. VGG16 and ResNet50 performed well when transfer learning was applied.
Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS

Aiming at the problems of low mapping accuracy, slow path planning efficiency, and high radar frequency requirements in the process of mobile robot mapping and navigation in an indoor environment, this paper proposes a four-wheel drive adaptive robot positioning and navigation system based on ROS. By comparing and analyzing the mapping effects of various 2D-SLAM algorithms (Gmapping, Karto SLAM, and Hector SLAM), the Karto SLAM algorithm is used for map building. By comparing the Dijkstra algorithm with the A* algorithm, the A* algorithm is used for heuristic searches, which improves the efficiency of path planning. The DWA algorithm is used for local path planning, and real-time path planning is carried out by combining sensor data, which have a good obstacle avoidance performance. The mathematical model of four-wheel adaptive robot sliding steering was established, and the URDF model of the mobile robot was established under a ROS system. The map environment was built in Gazebo, and the simulation experiment was carried out by integrating lidar and odometer data, so as to realize the functions of mobile robot scanning mapping and autonomous obstacle avoidance navigation. The communication between the ROS system and STM32 is realized, the packaging of the ROS chassis node is completed, and the ROS chassis node has the function of receiving speed commands and feeding back odometer data and TF transformation, and the slip rate of the four-wheel robot in situ steering is successfully measured, making the chassis pose more accurate. Simulation tests and experimental verification show that the system has a high precision in environment map building and can achieve accurate navigation tasks.
The role of generative adversarial networks in brain MRI: a scoping review

The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.
A MCDM-based framework for blockchain consensus protocol selection

Blockchain is one of the most disruptive technologies introduced in Bitcoin, which engaged great attention from the industry and academia and determined a rapid growth of other Distributed Ledger Technologies (DLTs). In the complex architecture of a DLT system, a consensus protocol plays a key role by ensuring that all participants agree on the data integrity without any central authority. A wide range of consensus protocols have been designed with different concepts and properties (e.g., lower energy consumption, better scalability, smaller latency, higher throughput, etc.). The key requirements for consensus protocols passing from one blockchain system to another often differ significantly, and there is no one-fit-all protocol. Therefore, selecting the most suitable consensus protocol for a particular DLT system is essential, but at the same time a challenging step, as decision-makers need to make a trade-off between conflicting requirements. This paper introduces a framework for selecting the most suitable consensus protocols depending on the identified criteria, priorities, and other requirements by incorporating Multi-Criteria Decision-Making (MCDM) techniques. We demonstrate its potential by identifying the preferable consensuses for the three most common types of existing blockchain systems and on an actual application for bike renting. Moreover, the collected data and tools are freely available, ensuring full replicability, reusability, and further development.
Diaframe: automated verification of fine-grained concurrent programs in Iris

Fine-grained concurrent programs are difficult to get right, yet play an important role in modern-day computers. We want to prove strong specifications of such programs, with minimal user effort, in a trustworthy way. In this paper, we present Diaframe—an automated and foundational verification tool for fine-grained concurrent programs.
Interval Type-3 Fuzzy Control for Automated Tuning of Image Quality in Televisions

In this article, an intelligent system utilizing type-3 fuzzy logic for automated image quality tuning in televisions is presented. The tuning problem can be formulated as controlling the television imaging system to achieve the requirements of production quality. Previously, the tuning process has been carried out by experts, by manually adjusting the television imaging system on production lines to meet the quality control standards. In this approach, interval type-3 fuzzy logic is utilized with the goal of automating the tuning of televisions manufactured on production lines. An interval type-3 fuzzy approach for image tuning is proposed, so that the best image quality is obtained and, in this way, meet quality requirements. A system based on type-3 fuzzy control is implemented with good simulation results. The validation of the type-3 fuzzy approach is made by comparing the results with human experts on the process of electrical tuning of televisions. The key contribution is the utilization of type-3 fuzzy in the image tuning application, which has not been reported previously in the literature.