Reviewerly/exHarmony huggingface dataset:
Towards Kenyan Sign Language Hand Gesture Recognition Dataset

Datasets for hand gesture recognition are now an important aspect of machine learning. Many datasets have been created for machine learning purposes. Some of the notable datasets include Modified National Institute of Standards and Technology (MNIST) dataset, Common Objects in Context (COCO) dataset, Canadian Institute For Advanced Research (CIFAR-10) dataset, LeNet-5, AlexNet, GoogLeNet, The American Sign Language Lexicon Video Dataset and 2D Static Hand Gesture Colour Image Dataset for ASL Gestures. However, there is no dataset for Kenya Sign language (KSL). This paper proposes the creation of a KSL hand gesture recognition dataset. The dataset is intended to be in two-fold. One for static hand gestures, and one for dynamic hand gestures. With respect to dynamic hand gestures short videos of the KSL alphabet a to z and numbers 0 to 10 will be considered. Likewise, for the static gestures KSL alphabet a to z will be considered. It is anticipated that this dataset will be vital in creation of sign language hand gesture recognition systems not only for Kenya sign language but of other sign languages as well. This will be possible because of learning transfer ability when implementing sign language systems using neural network models.
A study of the development status and purchase intention factors of metaverse NFT collectibles

During the COVID-19 epidemic, museums and visitors are moving from offline to online, the age of metadata is upon us, global digitization is increasing, blockchain technology is maturing, the NFT trading market is active, and collectibles are the most popular and fastest-growing category. China is at an early stage but developing rapidly. In the study, first, the differences between NFT collectibles in China and other countries are analyzed through literature and cases, and the platform of NFT collectibles in China is analyzed; then, the dimensions of the perceived value of NFT collectibles are summarized and obtained through user interviews; finally, a theoretical framework based on multidimensional perceived value on users' purchase intention is established and validated through expert interviews. The study facilitates the analysis of Chinese NFT collectibles, thus generalizing the benefits of multidimensional perceived value, which is essential for future development.
Out of Sight but Still In Mind: Making ‘Invisible’ Cyber Threats More Salient Via Concrete Analogies

It can be easier to conceive of and anticipate physical threats than cyber threats. Cyber threats can involve unseen remote hackers, and capitalize on invisible wireless signals as vectors. As such cyber threats are often out of sight and out of mind. How can we make these abstract, 'invisible' threats more intuitive and salient? We employed concrete analogies to enable future Army Officers to better anticipate cyber threats in tactical contexts. Modern multi-domain battle involves not only physical threats like fire fights and improvised explosive devices (IEDs), but also, increasingly, cyber threats. For example, the enemy may jam, intercept or track communication signals, hack into computing systems to exfiltrate or alter information, and/or hack equipment with electronic and autonomous components (including navigation systems, drones and robots). To ensure readiness, all soldiers, (not only cyber specialists) must have some awareness of this 'threatscape'. We developed the problem anticipation task (PAT) to gauge the degree to which participants would anticipate cyber as well as non-cyber tactical threats. They read a hypothetical mission description and tried to anticipate various problems that could arise. The mission explicitly mentioned several cyber-vulnerable components (e.g., radios, navigation systems, drones, biosensors, cell phones). Prior research using a sample from the same population indicated that about 40% of subjects did not anticipate a single cyber threat (Pyke, Ness, Feltner, in press). The current research used the PAT as a pre- and post-test and included an intervening intervention. Experimental subjects read a passage about a fictitious historical mission set in the 1800s. The version of the passage presented to the experimental group included historical issues (e.g., carrier pigeon intercepted by enemy) that were intended to be analogous to modern cyber-related issues (e.g., wireless communications signal intercepted/tapped by enemy). The intervention for the comparison group involved a passage describing historical issues (e.g., horse losing a shoe) that were intended to be analogous to modern non-cyber related issues (e.g., vehicle breakdown). Note that the link to the corresponding modern situation was not made explicit to the participants, they were just exposed to a historical situation that could lend itself to being analogous to a modern cyber situation. For the experimental group (but not the control) there was a significant gain in the percent of participants who were able anticipate one or more cyber issues. Thus, concrete analogies can serve to make 'invisible' cyber threats more intuitive and easier to anticipate.
Predicting Presence using Environment-Activated Motion in Immersive Virtual Reality

Immersive virtual reality (IVR) has shown promise in being an effective method for a variety of interventions across a multitude of disciplines. Indeed, IVR allows researchers to place participants in realistic three-dimensional environments with a level of control that may not have been feasible in physical environments. Additionally, IVR elicits a higher sense of presence than computer-based interventions. Evidence suggests that presence, defined as the “sense of being there”, is a key factor in the success of these interventions. The relationship between presence and hardware and software factors have been extensively investigated. Behavioral factors have comparatively received little attention. While these system factors are important to investigate, behavioral differences across users may preclude them from engaging in meaningful IVR interventions, regardless of the hardware and software solutions applied. Therefore, this study investigated how an individual’s interaction with the virtual experience may influence their sense of presence. More specifically, we wanted to probe the relationship between environment-activated motion (EAM) and presence in IVR. EVM can be described as any motion within the experience initiated by the user or the environment. This is activated by user-activated motion (i.e., a user looks around) or environment-activated motion (i.e., a virtual dog moves within view). 80 younger adults (22 male, 1 non-binary) completed a 10-minute IVR experience in Vesper Peak using the HTC Vive IVR system. The experience consisted of playing with a virtual robot dog (ex., petting and playing fetch) and teleporting to different areas of the mountain. The session was screen captured using OBS. EVM data were collected by analyzing the videos via OpenCV’s implementation of Dense Optical Flow (DOF). Presence was collected via the IGroup Presence Questionnaire (IPQ, Cronbach’s ⍺ = 0.87). Five data were removed due to corruption, resulting in a final sample size of 75. A regression was conducted with EVM as the predictor and presence as the predictand. The overall model was non-significant (R2 = 0.04, RSE = 5.87, F(1,73) = 12.61, p = 0.054). These results suggest that environment-activated motion may not play a significant role in presence during IVR experiences. Future IVR-based interventions may not have to control for environment-activated motion and can focus more on user interaction and environment design. Further research is needed to look into how environment-activated motion and user-activated motion separately may affect presence in different ways.
User-Oriented Definition of Smart Products: a "Body" Perspective

As more and more smart products appear in people's daily lives, designers begin to pay attention to smart product design. Industry and academia try to explain "what Smart Products are" in different fields. Although Smart Products are not a new term, there is no consensus on the definition of Smart Products. However, this is problematic for designers. Because the understanding of Smart Products directly affects how designers design smart products to bring users a better life. Smart products are quite different from previous products in terms of functions, interactions, and technologies. This not only affects designers but also poses challenges for users. This paper reviews the concept of Smart Products and Intelligence, constructs a user-oriented definition of smart products based upon the embodied cognition theory of cognitive science, proposes the characteristics of smart products, which provides designers with a new perspective for designing smart products.
The evolution of artificial intelligence adoption in industry

Artificial intelligence (AI) in the fourth industrial revolution is a key building block and is becoming more significant as digitization increases.AI implementation in enterprises is increasingly focused on the technological and economic aspects, disregarding the human factors. In this context, the implementation and success of AI technologies depend on employee acceptance. Low employee adoption can lead to poorer performance as well as dissatisfaction. To ensure the expected added value through AI, it is necessary for companies to increase AI acceptance. People see AI as a machine with human intelligence that surpasses employees' capabilities and acts autonomously. Moreover, workers therefore fear that AI will replace humans and that they will lose their jobs in this way. This aspect leads to a distrust of the new technology. This results in a negative attitude towards AI. Since the research field of AI acceptance and its influencing factors have not been sufficiently investigated so far, the aim of this study is to analyze the development of AI acceptance in the industrial environment.In order to achieve the goal of this study, the systematic literature review according to Tranfield et al. (2003) is chosen as the research method, as it draws on previous results and in this way the development of acceptance can be investigated. After discussing the relevance of the topic and the resulting problem, an explanation of the terms that are considered important for the understanding of this study follows. Thereupon the systematic literature research is planned, in which different search terms and databases are determined.In order to analyze the development of the individual aspects, these were then compared with the factors from existing technology acceptance models from earlier years. This provides the insight that the workers without AI experience tend to reject the AI technologies due to the fear of consequences and other factors, therefore, an increase in AI understanding through improved expertise is required. In addition, this work shows that insufficient infrastructure in enterprises slows down AI adoption, which is one of the main problems. Based on the results, a model is established for this purpose, which is compared with the technology acceptance models and the Unified Theory of Acceptance and Use of Technology model to show the similarities and differences of the factors of technology acceptance.
Social Engineering Penetration Testing within the OODCA Cycle – Approaches to Detect and Remediate Human Vulnerabilities and Risks in Information Security

In more than 95% of all successfully conducted cyberattacks, the human factor is exploited as a vulnerability point. The following principle applies. Whenever a hacker uses external attack vectors and thus does not directly use the Internet as a medium, employees become the target of the attack. As a result, the current technical and intelligent defense mechanisms can only contribute to a limited extent to the increase the resilience of IT systems, as these technological approaches do not fully account for the behavioral, cognitive, and heterogeneous motivations that lead to human error in the security causal chain of information security using social engineering (SE) methods. In this paper, we present a strategic and iterative analysis tool to detect SE threats through systemic monitoring, to train and successfully defend against them. For this purpose, we use the so-called Course of Actions to practically check the security-compliant behavior of employees and to initialize the feedback processes for reactivating the human firewall based on the knowledge gained. This approach is already being applied to various types of organizations and critical infrastructure and can be seamlessly integrated into existing training and auditing programs.
How to set up design skills in IT companies

In recent years, businesses utilizing data and artificial intelligence are becoming more prevalent in society, and the design skills required in IT companies are changing. The purpose of this study is to propose a method for setting up design skills that takes into account changes in the design skills required by IT companies. After organizing the skills to be referred to when setting up design skills, this research conducted a workshop to validate how to set up design skills and discussed the evaluations from the workshop participants.
A Scoping Review on Certified Immersive Virtual Training Applications

The fourth industrial revolution has given rise to virtual applications for training to meet the rapidly changing work environment. However, there are scanty reviews on the subject in the context of virtual training and certification. The current paper thus presents a scoping review of the literature regarding virtual applications for certification in training for specific competence building. The review essentially focuses on the required processes and outcomes of previous immersive virtual environments for certifications, and the effects of the training on competence measurement reliability. Additionally, the study investigates from the selected publications the learning outcome, competencies gained and the related training acquired. Besides, current weaknesses and strengths of VR applications are presented with suggestions for further improvements. Reviewed articles were obtained by extracting the salient information from publications indexed in four scientific digital libraries utilizing exclusion and inclusion methods. Our research design constituted five steps beginning with the research questions, literature extraction, relevant publication section, data extraction, evaluation and future research agenda. Selected publications also focused on fully immersive virtual reality utilizing HTC VIVE and Oculus Rift. Several advantages of the virtual certification training were discovered including enthusiasm, learning outcome, cost reduction, measurability and effects of the certifications. The majority of the publications focused extensively on the healthcare industry, especially medical/surgical. However, industrial virtual certifications such as hot work safety training, forklift safety operation, crane safety operation, and general work safety were discovered to be lacking. Despite these gaps, current interest and commitments are driving future alternatives for virtual training and certification for improving industrial training and competencies in other areas where utilization is conspicuously limited.
Polynomial Neural Layers for Numerical Modeling of Dynamical Processes

The paper aims to present a description of the tm-flow library for the “flowly” construction and training of polynomial neural networks (PNN) for time-evolving process prediction. The introduced polynomial models have a strong relationship to dynamic systems that can be described by a system of nonlinear differential equations. The paper provides implementation details and an explanation of training strategies, along with a few illustrative numerical examples. The source code is available at https://github.com/PNN-Lab/tmflow/ .
A Decentralized Mechanism to Decouple Vendor-Specific Access Management from IoT Devices Using Blockchain Technology, Smart Contract, and Wallets

This paper provides a mechanism for distributed access management and authorization transfer for communicating between devices belonging to different wallets and the same wallet. This paper also discusses segregating the IoT network into different groups or levels for fine-grain control. This approach allows Access control and intersystem network communication between authenticated devices and authenticated devices residing in the same system, using the devices layer, smart contract, and wallets. The Device layer is used to transmit data from one device to another, regardless of the wallet they belong to. The smart contract acts as the distributed command and control for access management. The wallet stands as the owner of the IoT in the system. The wallets are generally owned by individuals or end consumers of the IoT product. The approach can be split into three phases: initialization, device authentication, and device-to-device communication.
Preliminary Results of Group Detection Technique Based on User to Vector Encoding

This paper presents a novel approach for detecting groups of users based on observations of co-occurrences of user behavior. A Deep Neural Network is trained to encode users into a vector representation using an innovative adaptation of the word embedding architecture used in Natural Language Processing, which has been recently applied and modified for various domains, including graph data, recommender systems, and DNA gene sequence embedding. Preliminary experiments show promising results for the proposed adaptation to group detection based on the user-to-vector encoding derived from behavior observations in a variety of scenarios.
Prevention of Cyber-Attacks and Privacy Breaches in Healthcare Sector

Periodically, analysts in the cybersecurity sector, collect and share relevant data about recent cybercrime trends. These insights cover many aspects of the current cyber-threat scenario, including actors involved, motivations of the attacks, attack vectors, consequences, targets, and so on. Many organizations leverage such data to prepare their risk management plans and cyber defense strategies. This paper illustrates a case study involving two sample structures, a university and its hospital that want to compare the data available in last cyber security reports with the one collected from their actual operating environment, with the aim of being aware of how the mentioned reports could be helpful in reducing cybersecurity risks.
Continuous Authentication Methods for Zero-Trust Cybersecurity Architecture

Zero-trust architecture (ZTA) is a new approach to distributed systems cybersecurity which both provides better data protection and reflects the structure of modern information systems more accurately. Unlike the traditional perimeter-style approach, ZTA views each part of the system as a potential threat, and continuously authenticates it to detect possible threats. This article presents a comparative analysis of the most common continuous authentication methods, and evaluates their viability for use in ZTA systems. Comparison criteria are introduced, most prominent authentication methods found in literature are listed, and their thorough evaluation is carried out. We conclude that, first, most continuous authentication approaches are not universal and can only be used with certain specific input methods; second, that face recognition, while a theoretically universal approach, has several issues of its own according to our criteria; third, that hybrid methods that combine several approaches are a potentially viable way of authentication in ZTA context; and fourth, that machine learning is essential to all of the present approaches.
Real-Time Anomaly Detection Business Process for Industrial Equipment Using Internet of Things and Unsupervised Machine Learning Algorithms

Today, it is evident that the Internet of Things, real-time data processing, and artificial intelligence technologies are essential in industrial settings to enable early warning autonomous anomaly detection systems. Such systems can detect anomaly situations that could cause failures shortly after they occur, allowing necessary maintenance to be performed promptly. In this research, a software platform has been designed and developed to collect data from sensors placed on industrial equipment to monitor their condition using the required IoT infrastructure. The real-time data collected from this platform is analyzed using real-time data processing techniques. Here, a business process is introduced for instant anomaly detection using real-time clustering analysis methods. To validate the proposed business process architecture, a prototype software has been developed, and its ability to detect anomaly situations has been evaluated. The results show that the proposed business process architecture is effective in real-time anomaly detection and can successfully detect anomalies that can lead to industrial equipment failures.
A New Exercise Environment for the Experimental Treatment of Visual Snow

The Visual Snow Syndrome is a neurological condition that causes flickering dots to appear across a person’s entire field of vision. Those who suffer from this syndrome report seeing an unending stream of flickering dots throughout their visual field. Although patients often experience concurrent migraines, Visual Snow Syndrome appears to be a distinct phenomenon from prolonged migraine aura. The cause of this syndrome is not yet fully understood, but it has been linked to various eye and brain dysfunctions. The aim of this work is to make improvements to the environment in which exercises affected by this condition are developed. Currently, the Visual Snow Initiative provides an online platform where it is possible to carry out a 30-day exercise cycle during which patients are shown videos with noise that simulates Visual Snow and only affects certain areas of the visual field. This video noise moves and shifts along the screen occupying different areas of the screen. The video stream is sent from the servers to the users and requires a modern and efficient internet connection. A single video file occupies about 2GB of disk space. Modern codecs that deal with compressing video and encoding it into browser-supported formats have great difficulty encoding a stream where the pixel matrix of the video is in constant motion. The purpose of this work is to reconstruct the noise that is displayed within the video files for the exercises by means of JavaScript algorithms in such a way as to reduce the download required from users to a few kilobytes and to generate client-side the video that will then be used for the exercise.
Natural Language Processing Techniques for Hate Speech Evaluation for Brazilian Portuguese

The numerous harmful publications generated from large amounts of data expelled daily on social media make it necessary to adopt automated technologies for online content moderation. Sentence classification and sentiment analysis are Natural Language Processing (NLP) techniques used to detect hate speech on social media platforms such as Facebook and Instagram. However, some difficulties reduce the effectiveness of these tools in the Portuguese language. Previous research has shown how NLP models have high accuracy when trained with datasets centered on mastering the Brazilian Portuguese language. In this work, we propose the creation of a large-scale linguistic corpus for Brazilian Portuguese composed of publications collected from the social network Twitter. The experiments were performed by tuning a pretrained transformer model.