Reviewerly/exHarmony huggingface dataset:
Can Nonliterates Interact as Easily as Literates with a Virtual Reality System? A Usability Evaluation of VR Interaction Modalities

The aim of the study is twofold: to assess the usability of a virtuality (VR) interaction designed for nonliterate users in accordance with ISO-Standard 9241-11 and to compare the feasibility of two interaction modalities (motion controllers and real hands) considering the impact of VR sickness. To accomplish these goals, two levels were designed for a VR prototype application. The system usability scale (SUS) was used for self-reported satisfaction, while effectiveness and efficiency were measured based on observations and logged data. These measures were then analyzed using exploratory factor analysis, and the ones with high factor loading were selected. For this purpose, two studies were conducted. The first study investigated the effects of three independent variables on the interaction performance of a VR system, i.e., “User Type,” “Interaction Modality,” and “Use of New Technology.” The SUS results suggest that all the participants were satisfied with the application. The results of one-way ANOVA tests showed that there were no significant differences in the use of the VR application among the three selected user types. However, some measures, such as task completion time in level one, showed significant differences between user types, suggesting that nonliterate users had difficulty with the grab-and-move interaction. The results of the multivariate analysis using statistically significant variables from both ANOVA tests were also reported to verify the effect of modern technology on interactivity. The second study evaluated the interaction performance of nonliterate adults in a VR application using two independent variables: “Interaction Modality” and “Years of Technological Experience.” The results of the study showed a high level of satisfaction with the VR application, with an average satisfaction score of 90.75. The one sample T-tests indicated that the nonliterate users had difficulty using their hands as the interaction modality. The study also revealed that nonliterates may struggle with the poses and gestures required for hand interaction. The results suggest that until advancements in hand-tracking technology are made, controllers may be easier for nonliterate adults to use compared to using their hands. The results underline the importance of designing VR applications that are usable and accessible for nonliterate adults and can be used as guidelines for creating VR learning experiences for nonliterate adults.
A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems

Due to the increasing popularity of communication devices and vehicles, the channel environment becomes more and more complex, which makes conventional channel estimation methods further increase the pilot overhead to maintain estimation performance. However, it declines the throughput of communication networks. In this paper, we provide a novel two-stage based channel estimation method by using generative adversarial networks (GANs) to handle this problem in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the first stage aims to learn the mapping from a low-dimensional latent variable to the real channel sample. During the second stage, an iterative algorithm method is designed to find the optimal latent variable by matching the pilot channels of a real channel and generated channel. Then, the data channels are recovered based on the learned mapping relationship between the latent variable and the real channel sample. The simulation results show that our proposed method can achieve a performance gain of more than 2 dB with a pilot reduction by 75% when SNR is 10 dB, by comparing with the widely used Wiener filter interpolation method. In addition, as the low-dimensional latent variable can be obtained simultaneously, it can also be used for reducing the feedback overhead.
Cyber Attacker Profiling for Risk Analysis Based on Machine Learning

The notion of the attacker profile is often used in risk analysis tasks such as cyber attack forecasting, security incident investigations and security decision support. The attacker profile is a set of attributes characterising an attacker and their behaviour. This paper analyzes the research in the area of attacker modelling and presents the analysis results as a classification of attacker models, attributes and risk analysis techniques that are used to construct the attacker models. The authors introduce a formal two-level attacker model that consists of high-level attributes calculated using low-level attributes that are in turn calculated on the basis of the raw security data. To specify the low-level attributes, the authors performed a series of experiments with datasets of attacks. Firstly, the requirements of the datasets for the experiments were specified in order to select the appropriate datasets, and, afterwards, the applicability of the attributes formed on the basis of such nominal parameters as bash commands and event logs to calculate high-level attributes was evaluated. The results allow us to conclude that attack team profiles can be differentiated using nominal parameters such as bash history logs. At the same time, accurate attacker profiling requires the extension of the low-level attributes list.
FAD: Fine-Grained Adversarial Detection by Perturbation Intensity Classification

Adversarial examples present a severe threat to deep neural networks' application in safetycritical domains such as autonomous driving. Although there are numerous defensive solutions, they all have some flaws, such as the fact that they can only defend against adversarial attacks with a limited range of adversarial intensities. Therefore, there is a need for a detection method that can distinguish the adversarial intensity in a fine-grained manner so that subsequent tasks can perform different defense processing against perturbations of various intensities. Based on thefact that adversarial attack samples of different intensities are significantly different in the highfrequency region, this paper proposes a method to amplify the high-frequency component of the image and input it into the deep neural network based on the residual block structure. To our best knowledge, the proposed method is the first to classify adversarial intensities at a fine-grained level, thus providing an attack detection component for a general AI firewall. Experimental results show that our proposed method not only has advanced performance in AutoAttack detection by perturbation intensity classification, but also can effectively apply to detect examples of unseen adversarial attack methods.
Development and Application of Knowledge Graphs for the Injection Molding Process

Injection molding, the most common method used to process plastics, is a technique with a high knowledge content; however, relevant knowledge has not been systematically organized, and as a result, there have been many bottlenecks in talent cultivation. Moreover, most of the knowledge stored in books and online articles remains in the form of unstructured data, while some even remains unwritten, resulting in many difficulties in the construction of knowledge bases. Therefore, how to extract knowledge from unstructured data and engineers’ statements is a common goal of many enterprises. This study introduced the concept of a Knowledge Graph, a triplet extraction model based on bidirectional encoder representations from transformers (BERT) which was used to extract injection molding knowledge entities from text data, as well as the relationships between such entities, which were then stored in the form of knowledge graphs after entity alignment and classification with sentence-bidirectional encoder representations from transformers. In a test, the triplet extraction model achieved an F1 score of 0.899, while the entity alignment model and the entity classification model achieved accuracies of 0.92 and 0.93, respectively. Finally, a web platform was built to integrate the functions to allow engineers to expand the knowledge graphs by inputting learning statements.
EYOLOv3: An Efficient Real-Time Detection Model for Floating Object on River

At present, the surveillance of river floating in China is labor-intensive, time-consuming, and may miss something, so a fast and accurate automatic detection method is necessary. The two-stage convolutional neural network models appear to have high detection accuracy, but it is hard to reach real-time detection, while on the other hand, the one-stage models are less time-consuming but have lower accuracy. In response to the above problems, we propose a one-stage object detection model EYOLOv3 to achieve real-time and high accuracy detection of floating objects in video streams. Firstly, we design a multi-scale feature extraction and fusion module to improve the feature extraction capability of the network. Secondly, a better clustering algorithm is used to analyze the size characteristics of floating objects to design the anchor box, enabling the network to detect objects more effectively. Then a focus loss function is proposed to make the network effectively overcome the sample imbalance problem, and finally, an improved NMS algorithm is proposed to solve the object suppressed problem. Experiments show that the proposed model is efficient in detection of river floating objects, and has better performance than the classical object detection method and the latest method, realizing real-time floating detection in video streams.
A Novel Blockchain-Based Scientific Publishing System

The scientific publishing industry is dominated by a few publishers that use centralized systems, which decrease the quality of studies and make the publication process longer. Traditional publication systems generally have high publication costs, slow and biased review processes, copyrights held by publishers, lack of rewards for contributors, lack of connection among researchers, etc. Accordingly, we propose a decentralized blockchain-based scientific publication platform to eliminate the traditional publication system deficiencies. The proposed system uses Ethereum smart contracts to accelerate the publication process and abate the biased evaluation process while reducing the publication cost. The proposed model also improves the quality of scientific studies by adding new features to the publication process. The proposed system increases the number of publishers, makes the publication process fully traceable, and makes scientific papers globally available to anyone with a small fee. In addition, the system provides journals with decentralized models and integrates scientific papers with related data or datasets. The editors, reviewers, and cited authors are also rewarded. The proposed system has been implemented using Ethereum Virtual Machine (EVM), which consists of a front-end, middleware, and back-end. When an author submits a manuscript for evaluation, the system automatically finds the most appropriate editors and reviewers for related fields. After the publication process finishes, editors, reviewers, cited authors, and other contributors are rewarded as a system token-based cryptocurrency.
Study on Cyber Common Operational Picture Framework for Cyber Situational Awareness

The remarkable development of the Internet has made our lives very convenient, such as through the ability to instantaneously transmit individual pictures. As a result, cyber-attacks are also being developed and increasing, and the computer/mobile devices we use can become infected with viruses in an instant. Rapid cyber situational awareness is essential to prepare for such cyber-attacks. Accelerating cyber situational awareness requires Cyber Common Operational Pictures, which integrate and contextualize numerous data streams and data points. Therefore, we propose a Cyber Common Operational Pictures framework and criteria for rapid cyber situation awareness. First, the system reaction speed based on the user’s request and the standard for easily recognizing the object shown on the screen are presented. Second, standards and frameworks for five types of visualization screens that can directly recognize and respond to cyber-attacks are presented. Third, we show how a system was constructed based on the proposed framework, as well as the results of an experiment on the response time of each visualization screen. As a result of the experiment, the response speed of the 5 visualization screens was about 0.11 s on average for inquiry (simple) and 1.07 s on average for inquiry (complex). This is consistent with the typical response times of the studies investigated in this paper. If CyCOP is developed in compliance with the framework items (UI, object symbol, object size, response speed) presented in this paper, rapid situational awareness is possible. This research can be used in cyber-attack and defense training in the military field. In the private sector, it can be used in cyber and network control.
Effective and Privacy-Preserving Estimation of the Density Distribution of LBS Users under Geo-Indistinguishability

With the widespread use of mobile devices, location-based services (LBSs), which provide useful services adjusted to users’ locations, have become indispensable to our daily lives. However, along with several benefits, LBSs also create problems for users because to use LBSs, users are required to disclose their sensitive location information to the service providers. Hence, several studies have focused on protecting the location privacy of individual users when using LBSs. Geo-indistinguishability (Geo-I), which is based on the well-known differential privacy, has recently emerged as a de-facto privacy definition for the protection of location data in LBSs. However, LBS providers require aggregate statistics, such as user density distribution, for the purpose of improving their service quality, and deriving them accurately from the location dataset received from users is difficult owing to the data perturbation of Geo-I. Thus, in this study, we investigated two different approaches, the expectation-maximization (EM) algorithm and the deep learning based approaches, with the aim of precisely computing the density distribution of LBS users while preserving the privacy of location datasets. The evaluation results show that the deep learning approach significantly outperforms other alternatives at all privacy protection levels. Furthermore, when a low level of privacy protection is sufficient, the approach based on the EM algorithm shows performance results similar to those of the deep learning solution. Thus, it can be used instead of a deep learning approach, particularly when training datasets are not available.
Lightweight Pedestrian Detection Based on Feature Multiplexed Residual Network

As an important part of autonomous driving intelligence perception, pedestrian detection has high requirements for parameter size, real-time, and model performance. Firstly, a novel multiplexed connection residual block is proposed to construct the lightweight network for improving the ability to extract pedestrian features. Secondly, the lightweight scalable attention module is investigated to expand the local perceptual field of the model based on dilated convolution that can maintain the most important feature channels. Finally, we verify the proposed model on the Caltech pedestrian dataset and BDD 100 K datasets. The results show that the proposed method is superior to existing lightweight pedestrian detection methods in terms of model size and detection performance.
A lower approximation based integrated decision analysis framework for a blockchain-based supply chain

Organizations and their supply chains generate vast amounts of structured and unstructured data, popularly known as Big Data. This paper proposes a framework in the prescriptive analytics category of big data analytics (BDA) and is especially relevant for organizations on a permissioned blockchain network. The framework integrates the concepts of Artificial intelligence (Case-based reasoning, Interval-valued rough fuzzy set theory, and Value closeness relation algorithm) and blockchain to help decision-makers make accountable and conformable decisions. The framework comprises five stages - the first three stages generate the decisions, while the last two stages enable validation and communication of decisions. Its application is demonstrated on an organizational dataset with 20 supply chain KPIs. The numerical example shows how the organization's business strategy can translate to framework parameters resulting in varying outputs. The proposed framework is compared with the rough set approach through sensitivity analysis, highlighting the differences. The concept of L-Graphs is proposed to facilitate the visual display of prescribing decisions, which can help decision-makers make optimal decision choices. The framework has several unique features: (i) it is one of the first decision-making frameworks proposed for a blockchain-based supply chain that can be a part of the BDA toolbox of an organization; (ii) it uses concepts of artificial intelligence; (iii) the optimal decisions are proposed based on analysis of multicriteria; (iv) it uses multiple approaches to validate the decision rules on blockchain.
Improved detection of transient events in wide area sky survey using convolutional neural networks

The aim of data science is to catch up with the data-intensive life style as well as the demand for decision support, which becomes common in various domains such as medical, education and other smart solutions. As such, high quality of data analysis is greatly desired for accurate and effective downstreaming exploitations. This is also true for the domain of astronomical survey like GOTO (Gravitational-wave Optical Transient Observer), where large amount of raw data has been collected daily. This is one of recognised projects that search for transient events with the new breed of optical survey telescopes that can detect the sky faster and deeper. This is accomplished by comparing the night-specific data with the reference such that new bright sources are obtained for further study. However, the huge size of data makes it difficult to sift by naked eyes, thus requiring an automated system. Yet, many conventional machine-learning models have been sub-optimal for this task, as true positives can hardly be recognised due to the nature of imbalance data. This motivates the exploration of convolutional neural networks or CNN for this binary classification problem. Based on existing technologies, the paper reports the original application of basic CNN model to a representative data, which has been designed and generated within the GOTO project. In addition to the improvement over those previous works, this empirical study also includes details of parameter analysis, which will be useful for practice and further investigation.
The Integration of Traditional Broadcasters with Artificial Intelligence in Television News Programmes

Artificial Intelligence (AI) has been a powerful force in technological development over the last decade, creating a new wave of technology worldwide, led by China and the United States, with solid but different development momentum. Therefore, understanding Chinese social media’s current attitudes and tendencies towards AI is crucial to understanding the future direction of AI development. Artificial intelligence technology is applied to various fields, and media is one of them. The advent of the 5G era and the emergence of AI anchors have hugely impacted the media sector. In this paper, we analyse the advantages of human presenters and the measures that news presenters can take in the face of challenges in the AI era so that practitioners are aware of the impact of AI technology on the media industry and the importance of improving their capabilities.
SKDCGN: Source-free Knowledge Distillation of Counterfactual Generative Networks Using cGANs

With the usage of appropriate inductive biases, Counterfactual Generative Networks (CGNs) can generate novel images from random combinations of shape, texture, and background manifolds. These images can be utilized to train an invariant classifier, avoiding the wide spread problem of deep architectures learning spurious correlations rather than meaningful ones. As a consequence, out-of-domain robustness is improved. However, the CGN architecture comprises multiple over parameterized networks, namely BigGAN and U2-Net. Training these networks requires appropriate background knowledge and extensive computation. Since one does not always have access to the precise training details, nor do they always possess the necessary knowledge of counterfactuals, our work addresses the following question: Can we use the knowledge embedded in pre-trained CGNs to train a lower-capacity model, assuming black-box access (i.e., only access to the pretrained CGN model) to the components of the architecture? In this direction, we propose a novel work named SKDCGN that attempts knowledge transfer using Knowledge Distillation (KD). In our proposed architecture, each independent mechanism (shape, texture, background) is represented by a student 'TinyGAN' that learns from the pretrained teacher 'BigGAN'. We demonstrate the efficacy of the proposed method using state-of-the-art datasets such as ImageNet, and MNIST by using KD and appropriate loss functions. Moreover, as an additional contribution, our paper conducts a thorough study on the composition mechanism of the CGNs, to gain a better understanding of how each mechanism influences the classification accuracy of an invariant classifier. Code available at: https://github.com/ambekarsameer96/SKDCGN .
A Stit Logic of Intentionality

We extend epistemic stit theory with a modality $$I_\alpha \varphi $$ , meant to express that at some moment agent $$\alpha $$ had a present-directed intention toward the realization of $$\varphi $$ . The semantics is based on the extension of stit frames with special topologies associated to agents. The open sets of the associated topology are interpreted as present-directed intentions, that support whether an agent had an intention of realizing a specific state of affairs when it chose one of its available actions and executed it. As an important application, we use $$I_\alpha \varphi $$ to formalize intentional action and intentional responsibility. We present an axiom system for our logic of intentionality, and prove that it is sound and complete.
A Robust Decoupled Control of Electric Vehicle Using Type-2 Fuzzy Logic Controller

For environmental reasons, Electric Vehicle (EV) is arousing renewed interest because it has the advantage of being non-polluting. As known Electric Vehicle (EV) is a complicated system due to non-linearity’s and unmeasured disturbance, which make it a challenge that faces the controller’s designer to improve the performance of the EVs. Intelligent controllers are frequently used in many areas and provide good results. These controllers have special calculation features for solving specific problems. Basis on this, we propose in this paper Type-2 Fuzzy Logic Control (T2FLC) for speed EV regulation. The main features of the suggested controller were compared with those of the classical type-1 FLC by simulation tests. Besides, a real model of the EV’s presented and evaluated under Artemis driving cycle. The obtained simulations results have proven that the T2FLC has an attractive result in terms of fast response and rapidity of rejection the perturbations, which confirm the robustness and high performances assured by the T2FLC.