Reviewerly/exHarmony huggingface dataset:
The Influence of Sharing Fake News, Self-Regulation, Cyber Bullying on Social Media Fatigue During COVID-19 Work Technology Conflict as Mediator Role

The purpose of this paper is to analyze the impact of Sharing Fake news, self-regulation, Cyber Bullying on social media fatigue during COVID-19 work technology conflict as mediator role. The current study uses quantitative with cross sectional design to examine the effect of Sharing Fake news, self-regulation, Cyber Bullying on social media fatigue during COVID-19 and suing work technology conflict as mediator role. The respondents were situation from different top sites, such as twitter, Facebook and Instagram sample of 132, and population of this study is 200 users were selected for this study, sample size is calculated through ROA soft. The dissemination of unverified information has been showcased as a significant challenge during the COVID-19 pandemic. The role of social media in this process is exemplified by its increased use during COVID-19, as, for example, a recent report shows that the use of Facebook hit record levels during the pandemic. This study revealed that potential mechanisms for counteracting fake news creating Facebook pages of real news and using this advertising to disseminate accurate information this paper will enhanced the understanding the effects of SMA, cyberbullying and self-regulation on mental health of individual through the use of social cognitive perspectives. To enhance efficacy in the role of social media in this process to reduce the gap between theory and practice, social marketers should include messages that empower recipients. Campaigns should show recommended behaviors and highlight their usefulness and effectiveness. This paper has been methodological as well as theoretical limitations, first using CLT might be regarded limiting even through it has been adopted in past studies that are based on social media. The primary concern is the cognitive load theory is still essential an instructional science theory even through it has been adopted, use widely in HCI & also shown to explain not only learning but also acquiring knowledge from new theories, articles might be more useful for conceptualizing fake news sharing.
Being There Creating Home for Migrant and Refugee Women Using Participatory Design Approaches Through Virtual Reality

The growth of migrants and refugees puts pressure on the building of temporary settlements. Most are designed based on functional aspects, especially during Covid crisis. Emotional well-being connected to the notion of home is missing, impeding an inclusive community. Being There is a VR-experience of migration spaces developed through participatory-design approaches centred around needs connected to home. Thematic analysis based on 28 interviews with Latin-American migrant/refugee women and volunteers, revealed the meaning of home, based on: culture, temporary transition, togetherness, and journey. The insights were used to create the VR animations (of the journey), the ideal temporary space, the interactions (with migrant voice-overs) and Volumetric-Captured character scenarios. It revealed the importance and possibilities to translate notions of home into VR-experiences to raise empathy and awareness for the importance of designing settlements regarding sense of home and shows how VR helps architects to understand, design and communicate temporary spaces.
Mooting in Virtual Reality: An Exploratory Study Using Experiential Learning Theory

Mooting, also known as a mock trial, is a form of simulated learning that is frequently used in legal education to teach practical skills. With the increasing advancement of technologies, educators have begun to incorporate a digital element into mooting in order to enhance its educational capabilities. In other areas of education, the use of virtual reality technology to facilitate simulated learning has shown to successfully improve students’ learning. Using experiential learning theory as a foundation, this paper aims to investigate the effects of virtual reality in the context of legal education and to propose a theoretical framework to guide the successful implementation of the novel technology specifically for mooting. Semi-structured interviews were conducted at a UK university. Findings show three main groups of themes that are relevant when implementing virtual reality in mooting: effectiveness, suitability, and emotions.
Serious Game Reality and Industrial Museum: The ‘Bryant and May Match Factory’ Project in the Peoples’ History Museum (UK)

This paper capitalises on the opportunities and challenges to explore similarities and differences between consolidated new realities – augmented reality, virtual reality, and mixed reality – and hybrid technological realities to explore how the serious game reality’s technological and functional elements influence visitors’ experiences in museum and post-experience behaviours. Findings validate the influence of serious game reality functional elements on visitors’ experiences, showing how embodiment continues to remain closely correlated to traditional experience to drive post-experience behaviours. However, some theoretical questions remain open, considering the influence of 4.0 experience on museum post-experience.
How Cognitive Flexibility Affects Sense of Power in a Coffee Virtual Setting: The Moderating Role of Personality Traits

In this study, we cross two sensory experiences, one with the senses of sight and sound and the other with the senses of sight, sound, and smell. The experience takes place in a Virtual Coffee and measures the impact it has on the sense of power. We analyse the concepts of Sense of Power and Cognitive Flexibility. Personality traits is analysed as a moderator in the relation between Sense of Power and Cognitive Flexibility. A sample of 125 individuals participated in the experiment. Results show that Cognitive Flexibility significantly and positively explains Sense of Power. Personality Traits moderates the relationship between Cognitive Flexibility and Sense of Power.
“Opening and Closing Virtual Reality” The Effects of Openness on Interpretation of and Attitude Towards Virtual Reality Advertisements

With the increase of Virtual Reality (VR), the importance of advertising within VR has become more relevant. However, it is unclear whether advertising strategies used in traditional media also work in VR. This study is focused on openness in advertisements, an advertisement strategy that refers to the degree of guidance towards the intended message. More open means less guidance and is often used to increase attention and attitude. However, open ads have not been researched in VR before. In this study, VR-advertisements were created and tested that differ in openness. An experiment with 87 participants revealed new ways of creating openness based on positioning and timing of anchoring cues. Moreover, the results revealed a negative effect of openness on interpretation, consistent with findings in studies using traditional media. Unexpectedly, openness in VR-advertisements did not influence (ad and brand) attitude, possibly due to the newness-effect causing high appreciation for ads in VR.
An Exploration of Poisoning Attacks on Data-Based Decision Making

Many real-world problems involve building a predictive model about an adversary and then determining a decision accordingly, including two-stage (predict then optimize) and decision focused (joint predict and optimize) approaches. The involvement of a predictive model learned from adversary’s behavior data poses a critical threat that an adversary can influence the learning process, which will ultimately deteriorate the end-goal decision quality. In this paper, we study the problem of poisoning attacks in this data-based decision making setting. That is, the adversary can alter the training data by injecting some perturbation into the data to a certain limit that can substantially change the final decision outcome in the end towards the adversary goal. To our knowledge, this is the first work that studies poisoning attacks in such data-based decision making scenarios. In particular, we provide the following main contributions. We introduce a new meta-gradient based poisoning attack for various types of predict and optimize frameworks. We compare to a technique shown effective in computer vision. We find that the complexity of the problem makes attacking decision focused model difficult. We show that an attack crafted against a two-stage model is effectively transferable to a decision-focused model.
Reward Delay Attacks on Deep Reinforcement Learning

Most reinforcement learning algorithms implicitly assume strong synchrony. We present novel attacks targeting Q-learning that exploit a vulnerability entailed by this assumption by delaying the reward signal for a limited time period. We consider two types of attack goals: targeted attacks, which aim to cause a target policy to be learned, and untargeted attacks, which simply aim to induce a policy with a low reward. We evaluate the efficacy of the proposed attacks through a series of experiments. Our first observation is that reward-delay attacks are extremely effective when the goal is simply to minimize reward. Indeed, we find that even naive baseline reward-delay attacks are also highly successful in minimizing the reward. Targeted attacks, on the other hand, are more challenging, although we nevertheless demonstrate that the proposed approaches remain highly effective at achieving the attacker’s targets. In addition, we introduce a second threat model that captures a minimal mitigation that ensures that rewards cannot be used out of sequence. We find that this mitigation remains insufficient to ensure robustness to attacks that delay, but preserve the order, of rewards.
Voting Games to Model Protocol Stability and Security of Proof-of-Work Cryptocurrencies

We model the protocol stability and the security of proof-of-work cryptocurrencies using voting games. The first game, which we call the Rule Game, pertains to the scenario where the cryptocurrency miners engage in a voting procedure to accept or reject a proposal for change of the cryptocurrency protocol. The second game, which we call the Attack Game, refers to the scenario where a group of miners can form a coalition to launch a $$51\%$$ attack on the system and consequently change a portion of the history of the underlying blockchain, thus defeating its promise of immutability. For the Attack Game, we define progressively granular notions of security all of which are based on the key concept of minimal winning coalitions from voting game theory. For both the Rule Game and the Attack Game, we show practical applicability of tools from voting game theory using a snapshot of real world data for Bitcoin. In particular, this highlights the fragile nature of the security of Bitcoin with respect to $$51\%$$ attacks.
Casino Rationale: Countering Attacker Deception in Zero-Sum Stackelberg Security Games of Bounded Rationality

In this work, we consider a zero-sum game between an adaptive defender and a potentially deceptive attacker who is able to vary their degree of rationality as a deceptive ruse. Under this setup, we provide a complete characterization of the deception space of the attacker and uncover optimal strategies for adaptive defender against a deceptive attacker. In addition, we consider the setup in which both the attacker and defender are allowed to evolve their strategies over time. In this setting, one of our main results is to demonstrate that allowing the attacker to vary their degree of rationality can significantly impact the game in favor of the attacker.
Recent Advances in Infrared Face Analysis and Recognition with Deep Learning

Besides the many advances made in the facial detection and recognition fields, face recognition applied to visual images (VIS-FR) has received increasing interest in recent years, especially in the field of communication, identity authentication, public safety and to address the risk of terrorism and crime. These systems however encounter important problems in the presence of variations in pose, expression, age, occlusion, disguise, and lighting as these factors significantly reduce the recognition accuracy. To prevent problems in the visible spectrum, several researchers have recommended the use of infrared images. This paper provides an updated overview of deep infrared (IR) approaches in face recognition (FR) and analysis. First, we present the most widely used databases, both public and private, and the various metrics and loss functions that have been proposed and used in deep infrared techniques. We then review deep face analysis and recognition/identification methods proposed in recent years. In this review, we show that infrared techniques have given interesting results for face recognition, solving some of the problems encountered with visible spectrum techniques. We finally identify some weaknesses of current infrared FR approaches as well as many future research directions to address the IR FR limitations.
Anomaly Detection of DC Nut Runner Processes in Engine Assembly

In many manufacturing systems, anomaly detection is critical to identifying process errors and ensuring product quality. This paper proposes three semi-supervised solutions to detect anomalies in Direct Current (DC) Nut Runner engine assembly processes. The nut runner process is a challenging anomaly detection problem due to the manual nature of the process inducing high variability and ambiguity of the anomalous class. These characteristics lead to a scenario where anomalies are not outliers, and the normal operating conditions are difficult to define. To address these challenges, a Gaussian Mixture Model (GMM) was trained using a semi-supervised approach. Three dimensionality reduction methods were compared in pre-processing: PCA, t-SNE, and UMAP. These approaches are demonstrated to outperform the current approaches used by a major automotive company on two real-world datasets. Furthermore, a novel approach to labelling real-world data is proposed, including the concept of an ‘Anomaly No Concern’ class, in addition to the traditional labels of ‘Anomaly’ and ‘Normal’. Introducing this new term helped address knowledge gaps between data scientists and domain experts, as well as providing new insights during model development and testing. This represents a major advancement in identifying anomalies in manual production processes that use handheld tools.
Using Deception in Markov Game to Understand Adversarial Behaviors Through a Capture-The-Flag Environment

Identifying the actual adversarial threat against a system vulnerability has been a long-standing challenge for cybersecurity research. To determine an optimal strategy for the defender, game-theoretic based decision models have been widely used to simulate the real-world attacker-defender scenarios while taking the defender’s constraints into consideration. In this work, we focus on understanding human attacker behaviors in order to optimize the defender’s strategy. To achieve this goal, we model attacker-defender engagements as Markov Games and search for their Bayesian Stackelberg Equilibrium. We validate our modeling approach and report our empirical findings using a Capture-The-Flag (CTF) setup, and we conduct user studies on adversaries with varying skill-levels. Our studies show that application-level deceptions are an optimal mitigation strategy against targeted attacks—outperforming classic cyber-defensive maneuvers, such as patching or blocking network requests. We use this result to further hypothesize over the attacker’s behaviors when trapped in an embedded honeypot environment and present a detailed analysis of the same.
Causal Discovery of Stochastic Dynamical Systems: A Markov Chain Approach

Our proposed method for exploring the causal discovery of stochastic dynamic systems is designed to overcome the limitations of existing methods in detecting hidden and common drivers. The method is based on a simple principle and is presented in a nonparametric structural vector autoregressive modeling framework.
ARMOR: Differential Model Distribution for Adversarially Robust Federated Learning

In this work, we formalize the concept of differential model robustness (DMR), a new property for ensuring model security in federated learning (FL) systems. For most conventional FL frameworks, all clients receive the same global model. If there exists a Byzantine client who maliciously generates adversarial samples against the global model, the attack will be immediately transferred to all other benign clients. To address the attack transferability concern and improve the DMR of FL systems, we propose the notion of differential model distribution (DMD) where the server distributes different models to different clients. As a concrete instantiation of DMD, we propose the ARMOR framework utilizing differential adversarial training to prevent a corrupted client from launching white-box adversarial attack against other clients, for the local model received by the corrupted client is different from that of benign clients. Through extensive experiments, we demonstrate that ARMOR can significantly reduce both the attack success rate (ASR) and average adversarial transfer rate (AATR) across different FL settings. For instance, for a 35-client FL system, the ASR and AATR can be reduced by as much as 85% and 80% over the MNIST dataset.
Symbol Preaching in the Digital Age: From Symbol Recognition to Symbol Interpretation in Facebook Ads

The thesis of this paper is that in the digital age we are moving away from words and concepts characteristic of the print age, towards the use of images and symbols. I distinguish between objective symbols as in mathematics, and cultural symbols as in poetry and religion. Students must learn to move from recognizing the objective rules of language to internalizing the norms of culture, according to the analogy of learning. Ricoeur’s theory of interpretation explains the passage from recognition to interpretation in the cultural sciences. This passage is not only cognitive but also implies the discovery of an experiential dimension, as in poetry and worship. This theory is applied to the findings from religious ads on Facebook. By creating new audiences by trial and error, the number of viewers increased from 1 K to up to 100 K. The analysis revealed that viewers showed little interest in informational and moralistic ads, but favored symbolic presentations of the Passion, the Resurrection, the Transfiguration, the Eucharist, the origin of evil, etc. The conclusion offers guidelines: the need to advertise, to adapt to audiences, to get feedback, and to preach through symbols rather than concepts.
Fan-Slicer: A Pycuda Package for Fast Reslicing of Ultrasound Shaped Planes

Fan-Slicer (<a href="https://github.com/UCL/fan-slicer" target="_blank">https://github.com/UCL/fan-slicer</a>) is a Python package that enables the fast sampling (slicing) of 2D ultrasound-shaped images from a 3D volume. To increase sampling speed, CUDA kernel functions are used in conjunction with the Pycuda package. The main features include functions to generate images from both 3D surface models and 3D volumes. Additionally, the package also allows for the sampling of images from curvilinear (fan shaped planes) and linear (rectangle shaped planes) ultrasound transducers. Potential uses of Fan-slicer include the generation of large datasets of 2D images from 3D volumes and the simulation of intra-operative data among others.
An Overview of Blockchain-Based IoT Architectures and Designs

In recent years there has been an increased interest in the internet of things and blockchain in both industry and academia. The blockchain is quickly becoming an important technology that will fundamentally alter the method in which people exchange information. As a result, the emergence of blockchain technology promises to alleviate many of the issues related to the current centralised IoT data model. However, blockchain and IoT are both emerging technologies, the design issues and architectural styles of blockchain-based IoT systems are still not well explored. It is possible to develop safe IoT systems by utilising various architecture designs and different blockchain systems. However, there are insufficient studies to aid in making architecture design and configuration choices for blockchain-based systems. As a result, we could make improper design decisions and setups that pose additional issues because of this information gap. In trying to rectify this matter, we give a recent literature review analysis on the design of blockchain-based IoT architectures. Specifically, we identify four major components together with their design considerations and obstacles that must be taken into account when developing a blockchain-based architecture for IoT. Then we presented and discussed four typical Blockchain-based IoT architecture styles. Additionally, we discussed the challenges of implementing blockchain-powered IoT services and looked at the challenges associated with each scenario.
Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification

Email classification is essential to the trouble of email and pattern recognition. Nowadays, a number of unsolicited messages are circulated over the internet. While plenty of machine learning techniques are a success in detecting textual primarily based on totally unsolicited mail, this isn’t the case for messages spams, which can without difficulty avoid those textual-unsolicited mail detection systems. This paper proposes the introduction of MOBGOA constructed on the binary version of the GOA algorithm, for multi-objective selection of features purposes and the wrapper approach of selection of features and purpose of the EGOAMLP algorithm utilized this algorithm as the wrapper classifier. Spam assassin dataset was used to validate the performance of the MOB-EGOAMLP. The result uncovered that the new technique outflanks the wide selection of different methods in previously published works.
Blockchain Technology in Malaysian Estate Distribution: A Systematic Review

In Malaysia, the process of claiming an estate is quite complicated. It is managed by multiple agencies and a dual legal system, which is costly and time-consuming. Blockchain technology is therefore being considered a good database for consolidating all documents and agencies involved in the estate distribution procedure into a single platform. Even though blockchain technology is becoming increasingly popular, research on how it may be used in the distribution of estate is still lacking. In response to this, we conducted a systematic evaluation on the application of blockchain technology to be implemented in estate distribution in Malaysia. In this paper, we conduct a systematic review to identify, extract, and analyze all relevant publications relating to inheritance within 12 years, between 2010 and 2022 in online databases. This analysis is limited to the papers based on the relevancy of the paper on the subject being studied in respect of blockchain in estate distribution. Of those, 17 were assessed for eligibility, and the remaining three were found useful for this study. A systematic review was conducted to examine the factors causing arrears in the claiming process, blockchain characteristics that may be implemented in estate distribution, applicability of blockchain decision and future implementation. Previous research has investigated and analyzed the use of blockchain in inheritance, but much of it focuses on the wills of the deceased. There are still gap in study for Muslim estate distribution with the procedure included faraid distribution approach. This paper highlights the roles and features of blockchain technology and will assist academics in understanding blockchain technology, especially inheritance-related. Finally, the corresponding solutions and ideas are summarized, and the future development of blockchain technology in estate distribution is analyzed and judged.