Reviewerly/exHarmony huggingface dataset:
Yoga dataset: A resource for computer vision-based analysis of Yoga asanas

The practice of yoga has been shown to have numerous benefits for both physical and mental health, and it has gained popularity worldwide as a form of exercise and relaxation. However, yoga postures can be complex and challenging, especially for beginners who may struggle with proper alignment and positioning. To address this issue, there is a need for a dataset of different yoga postures that can be used to develop computer vision algorithms capable of recognizing and analyzing yoga poses. For this we created the image and video datasets of different yoga asana using the mobile device Samsung Galaxy M30s. The dataset contains images and videos of effective (right) and ineffective postures for 10 Yoga asana, with a total of 11,344 images and 80 videos. The image dataset is organized into 10 subfolders, each with "Effective (right) Steps" and "Ineffective (wrong) Steps" folders. The video dataset has 4 videos for each posture, with 40 videos demonstrating effective (right) postures and 40 demonstrating ineffective (wrong) postures. This dataset benefits app developers, machine learning researchers, Yoga instructors, and practitioners, who can use it to develop apps, train computer vision algorithms, and improve their practice. We strongly believe that this type of dataset would provide the foundation for the development of new technologies that can help individuals improve their Yoga practice, such as posture detection and correction tools or personalized recommendations based on individual abilities and needs.
Dissecting the mixed effects of human-customer service chatbot interaction on customer satisfaction: An explanation from temporal and conversational cues

Human-customer service chatbot interaction has had mixed effects on customer satisfaction in sales-related services. To dissect these conflicting results, three 2 (temporal cues: instant versus anthropomorphic delayed response) × 2 (conversational cues: task- versus social-oriented) between-group experiments were conducted. The results revealed the complex effects of temporal cues, conversational cues, and their interactions on perceived warmth, perceived competence, and satisfaction. And, perceived warmth and competence mediated the effects of temporal and conversational cues on satisfaction. Furthermore, the product attribute type (functional versus experiential) affected the relationships between these variables. These findings provide two explanatory perspectives (temporal and conversational cues) for dissecting the mixed effects of human-customer service chatbot interaction on customer satisfaction, and provide practical insights for improving customer service chatbots and enhancing customer satisfaction.
Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation

In the field of earth observation (EO), continual learning (CL) algorithms have been proposed to deal with large datasets by decomposing them into several subsets and processing them incrementally.The majority of these algorithms assume that data are, first, coming from a single source, and second, fully labeled.Real-world EO datasets are instead characterized by a large heterogeneity (e.g., coming from aerial, satellite, or drone scenarios), and for the most part they are unlabeled, meaning they can be fully exploited only through the emerging self-supervised learning (SSL) paradigm.For these reasons, in this article, we present a new algorithm for merging SSL and CL for remote sensing applications that we call continual Barlow twins.It combines the advantages of one of the simplest self-supervision techniques, i.e., Barlow twins, with the elastic weight consolidation method to avoid catastrophic forgetting.In addition, we evaluate the proposed continual SSL approach on a highly heterogeneous EO dataset, showing the effectiveness of this strategy on a novel combination of three almost non-overlapping domains datasets (airborne Potsdam, satellite US3D, and drone unmanned aerial vehicle semantic segmentation dataset), on a crucial downstream task in EO, i.e., semantic segmentation.Encouraging results show the superiority of SSL in this setting, and the effectiveness of creating an incremental effective pretrained feature extractor, based on ResNet50, without the need of relying on the complete availability of all the data, with a valuable saving of time and resources.
Do small- and medium-sized companies intend to use the Metaverse as part of their strategy? A behavioral intention analysis

Purpose The purpose of the research is to analyze the factors that determine the intention of small- and medium-sized enterprises (SMEs) to adopt the Metaverse. For this purpose, the analysis of the effort expectancy and performance expectancy of the constructs in relation to business satisfaction is proposed. Design/methodology/approach The analysis was performed on a sample of 182 Spanish SMEs in the technology sector, using a PLS-SEM approach for development. For the confirmation of the model and its results, an analysis with PLSpredict was performed, obtaining a high predictive capacity of the model. Findings After the analysis of the model proposed in this research, it is recorded that the valuation of the effort to be made and the possible performance expected by the companies does not directly determine the intention to use immersive technology in their strategic behavior. Instead, the results obtained indicate that business satisfaction will involve obtaining information, reducing uncertainty and analyzing the competition necessary for approaching this new virtual environment. Originality/value The study represents one of the first approaches to the intention of business behavior in the development of performance strategies within Metaverse systems. So far, the literature has approached immersive systems from perspectives close to consumer behavior, but the study of strategic business behavior has been left aside due to the high degree of experimentalism of this field of study and its scientific approach. The present study aims to contribute to the knowledge of the factors involved in the intention to use the Metaverse by SMEs interested in this field.
IoT Health Devices: Exploring Security Risks in the Connected Landscape

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of attacks are possible. To understand the risks in this new landscape, it is important to understand the architecture of IoTHDs, operations, and the social dynamics that may govern their interactions. This paper aims to document and create a map regarding IoTHDs, lay the groundwork for better understanding security risks in emerging IoTHD modalities through a multi-layer approach, and suggest means for improved governance and interaction. We also discuss technological innovations expected to set the stage for novel exploits leading into the middle and latter parts of the 21st century.
A Lightweight Radar Ship Detection Framework with Hybrid Attentions

One of the current research areas in the synthetic aperture radar (SAR) processing fields is deep learning-based ship detection in SAR imagery. Recently, ship detection in SAR images has achieved continuous breakthroughs in detection precision. However, determining how to strike a better balance between the precision and complexity of the algorithm is very meaningful for real-time object detection in real SAR application scenarios, and has attracted extensive attention from scholars. In this paper, a lightweight object detection framework for radar ship detection named multiple hybrid attentions ship detector (MHASD) with multiple hybrid attention mechanisms is proposed. It aims to reduce the complexity without loss of detection precision. First, considering that the ship features in SAR images are not inconspicuous compared with other images, a hybrid attention residual module (HARM) is developed in the deep-level layer to obtain features rapidly and effectively via the local channel attention and the parallel self-attentions. Meanwhile, it is also capable of ensuring high detection precision of the model. Second, an attention-based feature fusion scheme (AFFS) is proposed in the model neck to further heighten the features of the object. Meanwhile, AFFS constructs and develops a fresh hybrid attention feature fusion module (HAFFM) upon the local channel and spatial attentions to guarantee the applicability of the detection model. The Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) experimental results demonstrate that MHASD can balance detection speed and precision (improving average precision by 1.2% and achieving 13.7 GFLOPS). More importantly, extensive experiments on the SAR Ship Detection Dataset (SSDD) demonstrate that the proposed method is less affected by the background such as ports and rocks.
Feature Selection Using Golden Jackal Optimization for Software Fault Prediction

A program’s bug, fault, or mistake that results in unintended results is known as a software defect or fault. Software flaws are programming errors due to mistakes in the requirements, architecture, or source code. Finding and fixing bugs as soon as they arise is a crucial goal of software development that can be achieved in various ways. So, selecting a handful of optimal subsets of features from any dataset is a prime approach. Indirectly, the classification performance can be improved through the selection of features. A novel approach to feature selection (FS) has been developed, which incorporates the Golden Jackal Optimization (GJO) algorithm, a meta-heuristic optimization technique that draws on the hunting tactics of golden jackals. Combining this algorithm with four classifiers, namely K-Nearest Neighbor, Decision Tree, Quadrative Discriminant Analysis, and Naive Bayes, will aid in selecting a subset of relevant features from software fault prediction datasets. To evaluate the accuracy of this algorithm, we will compare its performance with other feature selection methods such as FSDE (Differential Evolution), FSPSO (Particle Swarm Optimization), FSGA (Genetic Algorithm), and FSACO (Ant Colony Optimization). The result that we got from FSGJO is great for almost all the cases. For many of the results, FSGJO has given higher classification accuracy. By utilizing the Friedman and Holm tests, to determine statistical significance, the suggested strategy has been verified and found to be superior to prior methods in selecting an optimal set of attributes.
Privacy and Data Protection in ChatGPT and Other AI Chatbots: Strategies for Securing User Information

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Vision Transformers for Small Histological Datasets Learned Through Knowledge Distillation

Computational Pathology (CPATH) systems have the potential to automate diagnostic tasks. However, the artifacts on the digitized histological glass slides, known as Whole Slide Images (WSIs), may hamper the overall performance of CPATH systems. Deep Learning (DL) models such as Vision Transformers (ViTs) may detect and exclude artifacts before running the diagnostic algorithm. A simple way to develop robust and generalized ViTs is to train them on massive datasets. Unfortunately, acquiring large medical datasets is expensive and inconvenient, prompting the need for a generalized artifact detection method for WSIs. In this paper, we present a student-teacher recipe to improve the classification performance of ViT for the air bubbles detection task. ViT, trained under the student-teacher framework, boosts its performance by distilling existing knowledge from the high-capacity teacher model. Our best-performing ViT yields 0.961 and 0.911 F1-score and MCC, respectively, observing a 7% gain in MCC against stand-alone training. The proposed method presents a new perspective of leveraging knowledge distillation over transfer learning to encourage the use of customized transformers for efficient preprocessing pipelines in the CPATH systems.
Learning from cyber security incidents: A systematic review and future research agenda

Cyber security incidents are now prevalent in many organisations. Arguably, those who can learn from security incidents and address the underlying causes will reduce the prevalence of similar ones in the future. This research provides a new examination of how organisations learn from incidents by systematically reviewing academic research on organisational learning from cyber security incidents and identifying further research needed in this area. To do this, it considers three research questions: what research has been conducted on learning from cyber security incidents, what learning practices in organisations have been found by research and what improvements have been recommended, and what further research is needed as organisations learn from such incidents. Using the PRISMA method, a total of 3,986 articles were extracted and, from these, a relevant set of 30 were selected for analysis to map the body of research, and to identify future research avenues. Despite learning lessons being recommended by both researchers and industry standards, our findings suggest that this advice is not being fully adopted by organisations. Importantly, these studies have found inadequate participation in learning activities, with superficial causal investigations, scarce effort on ensuring lessons are implemented and no evaluation of whether the actions taken actually reduce future security incidents. More research is needed to understand the right level and which learning practices to invest in for the greatest impact. For practitioners, this review discusses the essential elements of an effective process to learn from incidents. This review provides academics with a novel synthesis of the research undertaken on this topic, enabling them to incorporate the significant findings into their work and potentially explore the research agenda suggested.
A Systematic Review of Research Examining Teachers’ Competence-Related Beliefs About ICT Use: Frameworks and Related Measures

Existing measures of teachers' ICT-related competences and competence beliefs vary broadly across studies. This systematic review provides a summary of competence dimensions and assigned competences across different theoretical conceptualizations of teacher competence regarding ICT use, both basic and professional pedagogical competence. Guided by this summary, existing measures of teacher competence-related beliefs about ICT use are reviewed, described, and compared. A total of 45 studies published between 2016 and early 2021 are included in this rapid systematic review (databases: ERIC, ScienceDirect, tandfonline). Across all included studies, teachers' competence-related beliefs about ICT use were assessed in 49 different ways. The majority of measures were designed to assess competence-related beliefs about professional pedagogical ICT use (n = 46) rather than competence-related beliefs about basic ICT use (n = 25). In addition to the large variability of assessed competence dimensions and assigned competences across the reviewed instruments, the labels used, the theoretical background, and the number of items used in the measures also vary. Thus, our systematic review points to a high diversity of measures of teachers' competence-related beliefs about ICT use.
Deep learning algorithms for very short term solar irradiance forecasting: A survey

Integrating solar energy with existing grid systems is difficult due to its variability, which is impacted by factors such as the predicted horizon, meteorological conditions, and geographic position. Accurate global horizontal irradiance (GHI) estimates can help to address this issue and allow for early and effective participation in the energy planning and management market. The existing models either use time series data or sky images in various network topologies to perform solar radiance forecasts. This study compares three categories of solar irradiance forecasting models such as time series-based, image-based and hybrid models. Here several state-of-the-art methods are compared against the proposed models, namely Convolutional Long Short-Term Memory Fusion Network (CNN-L) and Multiple Image Convolutional Long Short-Term Memory Fusion Network (MICNN-L). Both models use both infrared sky images as well as past values of GHI for prediction. These methods extract spatial features using convolutional neural networks and temporal features using long short-term memory networks. The extracted features are finally concatenated and passed through a fully connected layer to obtain a prediction. Further analysis also included using a feature extraction method such as optical flow (OF) on the image data before passing it to the hybrid model MICNN-L (OF). The results observed in this comparative analysis denote that MICNN-L improves the efficacy of the forecasts in cloudy conditions compared to the rest of the state-of-the-art approaches.
Blockchain inspired secure and reliable data exchange architecture for cyber-physical healthcare system 4.0

A cyber-physical system is considered to be a collection of strongly coupled communication systems and devices that poses numerous security trials in various industrial applications including healthcare. The security and privacy of patient data is still a big concern because healthcare data is sensitive and valuable, and it is most targeted over the internet. Moreover, from the industrial perspective, the cyber-physical system plays a crucial role in the exchange of data remotely using sensor nodes in distributed environments. In the healthcare industry, Blockchain technology offers a promising solution to resolve most securities-related issues due to its decentralized, immutability, and transparency properties. In this paper, a blockchain-inspired secure and reliable data exchange architecture is proposed in the cyber-physical healthcare industry 4.0. The proposed system uses the BigchainDB, Tendermint, Inter-Planetary-File-System (IPFS), MongoDB, and AES encryption algorithms to improve Healthcare 4.0. Furthermore, blockchain-enabled secure healthcare architecture for accessing and managing the records between Doctors and Patients is introduced. The development of a blockchain-based Electronic Healthcare Record (EHR) exchange system is purely patient-centric, which means the entire control of data is in the owner's hand which is backed by blockchain for security and privacy. Our experimental results reveal that the proposed architecture is robust to handle more security attacks and can recover the data if 2/3 of nodes are failed. The proposed model is patient-centric, and control of data is in the patient's hand to enhance security and privacy, even system administrators can't access data without user permission.
Low-light aware framework for human activity recognition via optimized dual stream parallel network

Human Activity Recognition (HAR) plays a crucial role in communication and the Internet of Things (IoT), by enabling vision sensors to understand and respond to human behavior more intelligently and efficiently. Existing deep learning models are complex to deal with the low illumination, diverse viewpoints, and cluttered backgrounds, which require substantial computing resources and are not appropriate for edge devices. Furthermore, without an effective video analysis technique it processes entire frames, resulting inadequate performance. To address these key challenges, a cloud-assisted IoT computing framework is proposed for HAR in uncertain low-lighting environments, which is mainly composed of two tiers: edge and cloud computing. Initially, a lightweight Convolutional Neural Network (CNN) model is developed which is responsible to enhance the low-light frames, followed by the human detection algorithm to process the selective frames, thus enabling efficient resource utilization. Next, these refined frames are then transmitted to the cloud for accurate HAR, where dual stream CNN and transformer fusion network extract both short-and long-range spatiotemporal discriminative features followed by proposed Optimized Parallel Sequential Temporal Network (OPSTN) with squeeze and excitation attention to efficiently learn HAR in complex scenarios. Finally, extensive experiments are conducted over three challenging HAR datasets to deeply examine the proposed framework from various perspectives such as complex activity recognition, lowlighting, etc., where the results are outperformed compared with the state-of-art methods.
Ransomware through the lens of state crime: Conceptualizing ransomware groups as cyber proxies, pirates, and privateers

Cybercrime and other cybersecurity harms are gaining increasing political and public attention across many countries. One of the most serious and fastest growing categories of such harms relates to ransomware attacks. Many of the groups responsible for ransomware attacks have come under political pressure in recent years as they have become more aggressive in their methods and targeting. On a geopolitical level, an area attracting increasing interest is the complex relationships between ransomware groups and states, in particular, Russia. This paper introduces the concept of state crime to ransomware groups. Starting with the concept of proxies before turning to the historical examples of privateering and piracy, we focus on the notion of “cyber privateers” to analyze two select ransomware groups—DarkSide and REvil—that are believed to be affiliated with the Russian state. We argue that approaching these ransomware groups as cyber privateers engaged in state crime has the potential to enhance our understanding of how these groups operate. We further posit that a state crime perspective also assists in identifying how ransomware may be countered, highlighting the need for policy responses that are effective even when ransomware groups may be tacitly protected by a state.