Reviewerly/exHarmony huggingface dataset:
Solar irradiance prediction based on self-attention recursive model network

In recent years, with the continued development and popularity of sustainable energy sources and the increasing utilization of solar energy, accurate solar radiation prediction has become important. In this paper, we propose a new model based on deep learning, Feature-enhanced Gated Recurrent Unit, hereafter referred to as FEGRU, for solar radiation prediction. This model takes the source data with one-dimensional convolution and self-attention to feature attention and processes the data features, and then GRU performs feature extraction on solar irradiance data. Finally, the data dimensionality is transformed by a fully connected layer. The main advantage of FEGRU is that it does not require auxiliary data, but only time series data of solar irradiance can be used for good solar irradiance prediction. Our experiments with solar irradiance samples in Lyon, France, show that our model has better prediction results than the baseline model.
Immersive analytics: An overview

Abstract Immersive Analytics is concerned with the systematic examination of the benefits and challenges of using immersive environments for data analysis, and the development of corresponding designs that improve the quality and efficiency of the analysis process. While immersive technologies are now broadly available, practical solutions haven’t received broad acceptance in real-world applications outside of several core areas, and proper guidelines on the design of such solutions are still under development. Both fundamental research and applications bring together topics and questions from several fields, and open a wide range of directions regarding underlying theory, evidence from user studies, and practical solutions tailored towards the requirements of application areas. We give an overview on the concepts, topics, research questions, and challenges.
Post Philosophies and the Doing of Inquiry: Webinars and WEBing Sessions Become a Special Issue(s)

In this guest co-editors’ introduction to the special issue(s) titled “Post Philosophies and the Doing of Inquiry,” the authors share the backstory to the webinar series, information about the webinars and how to locate the recordings, and how WEBing sessions with our (former) students came from the webinar series. A list of articles and article titles for the special issue(s) are also included.
A simple relation of guessing probability in quantum key distribution

Given a communication system using quantum key distribution, the receiver can be seen as one who tries to guess the sender's information just as potential eavesdroppers do. The receiver-eavesdropper similarity thus implies a simple relation in terms of guessing probability and correctness of sifted keys, related with the distance-based, information-theoretic security. The tolerable regions of error rates determined by such a guessing-probability-based relation are shown to be close to those determined by security criteria. Thus, an alternative perspective on applying guessing probability in analyzing quantum key distribution issues is here provided. Examples of two specific protocols are illustrated. Our results contribute to evaluating an important element in communication study, and may provide useful reference for the security analysis of quantum key distribution protocols.
The Case of a Facebook Content Moderation Debacle in Greece

This chapter first discusses how Facebook’s content moderation process works, along with Facebook’s policies on Dangerous Individuals and Organizations, which were allegedly violated in the case we examine in this chapter; second, we briefly describe necessary contextual information regarding 17 November and Koufontinas’s actions, as well as what sparked the controversy presented here. Our case study consists of several instances of content removals and account restrictions of Greek journalists that were covering protests and were participating in the public debate around Koufontinas's hunger strike. All examined content moderation decisions took place on Facebook during the same period, mostly between February and April 2021. Last, we conduct a case-by-case analysis to deduce if the content had indeed violated any platform policy to extrapolate what may have happened in this case study and what socio-political stakes exist with the current content governance. Consequently, we argue that this chapter further demonstrates platforms’ preference to proactively err on the side of more content removal rather than letting politically controversial content on its services; this also further strengthens the argument that platforms are not neutral intermediaries and are increasingly taking editorial decisions.
Art as Metacognitive Tool for Critical Thinking in Engineering

The study hypothesis developed in this chapter is that the exercise of artArtart, exercise of criticismArtart criticism and aesthetic judgmentJudgmentjudgment, aesthetic encourages the emergence of such specific temperamentTemperament dispositions necessary for a substantial increase in critical thinkingThinkingthinking, critical skills in engineering studentsEngineeringengineering students. The metacognitiveEffortsefforts, meta‐cognitive toolsTooltool, metacognitive designed in this work considered that criticalityCriticality requires sensitivitySensitivity to problems and skills to redefine approaches beyond subjectionSubjection to functional fixationFixationfixation, functional, a frequent cognitive biasBiasbias, cognitive of engineering studentsEngineeringengineering students. Some of the competenciesCompetencies analyzed belong to the category of soft skillsSkillskill, soft: such as critical and creative thinkingThinkingthinking, creative or the ability to embrace contradictionsContradiction and take risks; while others belong to the category of digital literacy skillsLiteracy skill, digital: the ability to understand, interpret or evaluate information that is presented in fragmented, branched or visual-graphicVisual‐graphic formats. This chapter shows thatReflectionreflection, moments of the momentsMoment of aesthetic reflectionReflectionreflection, aesthetic and artistic experienceExperienceexperience, artistic in the engineering classroomEngineeringengineering classroom, including theaterTheater, literature, fine artsArtart, fine, modern danceDancedance, modern, and classical musicMusicmusic, classical, are highly effective because the reflection on an artistic artifactArtifactartifact, artistic goes beyond the concept of utilitarianismUtilitarianism and functionalityFunctionality. The advantage of inserting flexible artistic spotsSpotspot, artistic within the rigid scientific spaceSpacespace, scientific is that the artistic artifact, when intervened by the studentsStudent, awakens in them a state of metacognitiveEffortsefforts, meta‐cognitive self-awarenessSelf‐awareness, metacognitive that favors criticalityCriticality. Graphical Abstract
Can Blockchain Technology Be a Solution for Safety in No-Trust Environments?

The twenty-first century is the age of no trust. People do not trust each other or the governments and institutions that are supposed to serve them. Lawyers are becoming an inevitable part of even the simplest transactions, since control is a proxy for trust, and external agents, third parties are often required to provide a balance between the contractual partners. Present article endeavours to explore the usability of blockchain technology in current days' no trust environment and look into the way how the internal logic of smart, self-executing agreements based on blockchain technology can increase the sense of safety in their users by providing anonymity, transparency and trustworthiness for their users. While introducing various blockchain mechanisms, the aim of the paper is not to persuade everyone to become a member of a blockchain, but to call attention to the opportunities offered by such networks and emphasize, that neither of the introduced mechanisms are without flaws.
Automated Guided Vehicles Safety Issues and Security Aspects

One of the main advantages of AGVs is that they are safer than manual transportation of material, such as manned forklifts. AGVs don’t harm people or damage infrastructure, because they are provided with safety sensors that prevent them from running into people or things. Vehicles with the possibility of integration into production/assembly lines are largely associated with the development of flexible production systems. With their autonomy, in a way, they represent mobile workplaces, which, in accordance with the technology and speed of work in certain zones, deliver/ship work items in an appropriate way and time. The following figures show some of these solutions, which have achieved a significant level of application in some industries.
A Security Assessment of AI, Related to the Financial Institutions

This paper aims to summarise threats of new technologies, mainly the artificial intelligence solutions employed by the financial industry. Based on the professional literature, the paper provides an assessment and prioritization of threats of different systems. The authors also estimate the threat of the various threat actors. The paper assesses the risk of artificial intelligence solutions embedded into financial systems based on the abovementioned factors. The paper also concludes that no specific audit standards are applicable for AI systems. The authors suggest developing a design, implementation, and operational guideline supporting organizations that use ICT applications during their day-to-day operations. This paper is the first to highlight new research and development action plans and provide a practical summary of this field’s currently available fragmented knowledge. It suggests some potential solutions for the issue.
Fuzzy-Based Intrusion Detection Systems

Intrusion detection is a field of computer security that deals with detecting unauthorized network activities. An intrusion detection system is needed to identify all forms of malicious network traffic and computer activity that a traditional firewall cannot detect. Artificial Intelligence is an area of computing that explores how to create knowledge-based software that can successfully do tasks that humans can now do better. Expert systems, fuzzy logic, and neural networks are among the most recent developments in the field of artificial intelligence. Network Intrusion Detection Systems have been created and extensively researched in order to relieve the problem and detect malicious activity as early as possible. The fact that there are often no clear borders between normal and abnormal network traffic, that there is noisy or contains incorrect data, and that the analyzed traffic might represent both attack and normal communication, is a typical problem in this area. When compared with other techniques, fuzzy logic-based solutions may be beneficial because of their capacity to establish precise membership levels in multiple classes and perform various operations with outcomes that ensure reduced false positive and false negative categorization.
Label Distribution Learning for Generalizable Multi-Source Person Re-Identification

Person re-identification (Re-ID) is a critical technique in the video surveillance system, which has achieved significant success in the supervised setting. However, it is difficult to directly apply the supervised model to arbitrary unseen domains due to the domain gap between the available source domains and unseen target domains. In this paper, we propose a novel label distribution learning (LDL) method to address generalizable multi-source person Re-ID task ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., there are multiple available source domains, and the testing domain is unseen during training), which aims to explore the relation of different classes and mitigate the domain-shift across different domains so as to improve the discrimination of the model and learn the domain-invariant feature, simultaneously. Specifically, during the training process, we produce the label distribution via the online manner to mine the relation information of different classes, thus it is beneficial for extracting the discriminative feature. Besides, for the label distribution of each class, we further revise it to give more and equal attention to the other domains that the class does not belong to, which can effectively reduce the domain gap across different domains and obtain the domain-invariant feature. Furthermore, we also give the theoretical analysis to demonstrate that the proposed method can effectively deal with the domain-shift issue. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed method and show that the proposed method can outperform the state-of-the-art methods. Besides, further analysis also reveals the superiority of the proposed method.
Symbol State Transition Entropy: A Novel Feature of Interpulse Radio Frequency Modulation Signal

In radar electronic warfare (EW) reconnaissance, radio frequency (RF) is an important parameter for describing the received pulse, and it is usually used in sorting and identifying radar signals. In order to achieve different detection purposes, different modulations are used in intra-pulse and interpulse radar signals. It is an important frequency modulation mode in radar signals for interpulse frequency modulation, which describes the characteristics of dynamic changes of radar pulse signals. However, it is difficult to describe these characteristics effectively by only using Pulse Description Word(PDW). In this letter, a novel feature, based on symbolization and entropy, is proposed to characterize interpulse frequency modulation characteristics of radar signals. Simulation results show that the proposed feature not only have an excellent performance of describing interpulse RF modulation radar signals, but is also insensitive to the environment with a certain rate of pulses missing and spurious pulses. Besides, in terms of time complexity, it also has a satisfactory performance.
A Novel English Translation Model in Complex Environments Using Two-Stream Convolutional Neural Networks

Although translation is an essential component of learning English, it does not receive the attention it merits in the modern English classroom. Teachers and students primarily emphasize listening, reading, and writing while neglecting the development of translation skills. The English test in China now reflects the fact that there are now very specific requirements for students' translation skills. As a result, we should emphasize developing students' translation skills when teaching them English. The following experimental data can be obtained following the study and experiment on the English translation simulation model based on the two-stream convolutional neural network: English vocabulary and grammar have passing and excellent rates of 90 and 57 percent, respectively, while reading has passing and excellent rates of 69 and 8 percent, respectively. The ability of students to translate into English has significantly improved after using the English translation simulation model based on the two-stream convolutional neural network.
Is handling unbalanced datasets for machine learning uplifts system performance?: A case of diabetic prediction

Healthcare is a sensitive sector, and addressing the class imbalance in the healthcare domain is a time-consuming task for machine learning-based systems due to the vast amount of data. This study looks into the impact of socioeconomic disparities on the healthcare data of diabetic patients to make accurate disease predictions.This study proposed a systematic approach of Closest Distance Ranking and Principal Component Analysis to deal with the unbalanced dataset. A typical machine learning technique was used to analyze the proposed approach. The data set of pregnant diabetic women is analysed for accurate detection.The results of the case are analysed using sensitivity, which demonstrates that the minority class's lack of information makes it impossible to forecast the results. On the other hand, the unbalanced dataset was treated using the proposed technique and evaluated with the machine learning algorithm which significantly increased the performance of the system.The performance of the machine learning-based system was significantly enhanced by the unbalanced dataset which was processed with the proposed technique and evaluated with the machine learning algorithm. For the first time, an unbalanced dataset was treated with a combination of Closest Distance Ranking and Principal Component Analysis.
Quantum color image scaling based on bilinear interpolation

As a part of quantum image processing, quantum image scaling is a significant technology for the development of quantum computation. At present, most of the quantum image scaling schemes are based on grayscale images, with relatively little processing for color images. This paper proposes a quantum color image scaling scheme based on bilinear interpolation, which realizes the 2 n 1 × 2 n 2 quantum color image scaling. Firstly, the improved novel quantum representation of color digital images (INCQI) is employed to represent a 2 n 1 × 2 n 2 quantum color image, and the bilinear interpolation method for calculating pixel values of the interpolated image is presented. Then the quantum color image scaling-up and scaling-down circuits are designed by utilizing a series of quantum modules, and the complexity of the circuits is analyzed. Finally, the experimental simulation results of MATLAB based on the classical computer are given. The ultimate results demonstrate that the complexities of the scaling-up and scaling-down schemes are quadratic and linear, respectively, which are much lower than the cubic function and exponential function of other bilinear interpolation schemes.
The Impact of Functional and Psychological Barriers on Algorithm Aversion – An IRT Perspective

The application of artificial intelligence (AI) in decision-making is regarded as the most impactful disruption in an organization's digitalization. However, the benefits of the algorithmic decision can be leveraged only if the managers of an organization adopt this technology. Research found that despite the superior performance of algorithms, people discount algorithmic decisions either deliberately or unintentionally, a phenomenon known as algorithm aversion. In this regard, the current study seeks to investigate whether managers' innovation resistance, measured by different barriers, has any impact on algorithm aversion. Analyzing the survey data of 167 bank/financial managers, we found that while value barriers, tradition barriers, and image barriers are significantly associated with algorithm aversion, such relationships are absent in the case of usage barriers and risk barriers. The findings of this study have several theoretical and practical implications.
A Study on the Education Design for Literacy-Based Integrated Classics Reading－Based on the Case of the Class &amp;amp;#x0003c;Masterpiece Reading&amp;amp;#x0003e;

The purpose of this study is to present the education design measures required for effective classics reading for the improvement of literacy. Due to the 4th Industrial Revolution, including the emergence of artificial intelligence, big data, and IoT, we are facing a hyper-connected era in which everything is interlinked. With the increased importance of communication and collaboration with others, the importance of literacy to freely express one’s own opinions after reading, understanding, and judging certain writings is also becoming more important. Recently, however, the declining literacy level of Korean students is increasing and is now becoming a serious educational and social problem.&lt;/br&gt;Thus, this study explored the measures needed for improving the literacy level of students through the college subject of classics reading. First, this study presented the phased educational activities inclusively composed of ‘reading-speaking-listeningwriting’ activities based on the actual case of the class &lt;Masterpiece Reading&gt; with Lao-tzu’s &lt;Tao Te Ching&gt; . In particular, contrary to preceding research focusing on group discussion, this thesis presented the education design mixed with individual speaking/listening activities, as well as reading/writing activities.&lt;/br&gt;Furthermore, by conducting a survey this study compared the subjective perception of students who took the same course in the face-to-face and non-face-to-face methods. From the results of the analysis, overall the students showed positive responses to the integrated class activities regardless of class method. However, the results showed that it would still be necessary to complement the professor-led lecture and debate bulletin board. Also, the students thought that the activities in this class were helpful for the improvement of competencies like ‘communication, self-directedness, and problem-solving’.
Predictive Cyber Defense Remediation against Advanced Persistent Threat in Cyber-Physical Systems

Advanced Persistent Threat (APT) has dramatically changed the landscape of cybersecurity. APT is carried out by stealthy, continuous, sophisticated, and well-funded attack processes for long-term malicious gain thwarting most current defense mechanisms. There is a need for a defense strategy that continuously combats APT over a long time-span in imper-fect/incomplete information on attacker's actions. We propose the stochastic evolutionary game model to simulate the dynamic adversary to address this need in this work. We add the player's rationality parameter c to the Logit Quantal Response Dynamics (LQ RD) model to quantify the cognitive differences of real-world players. We propose an optimal decision-making plan by calculating the stable evolutionary equilibrium that balances a trade-off between defense cost and benefit. Cases studies conducted on Energy Delivery Systems (EDS) indicate that the proposed method can help the defender predict possible attack action, select the related optimal cyber defense remediation over time, and gain the maximum defense payoff.
Cyberspace, Black-Hat Hacking and Economic Sustainability of Corporate Organizations in Cross-River State, Nigeria

Globally, e-commerce offers unlimited opportunities for corporate organizations to flourish as its customers reside in all parts of the universe. However, given the fluid nature of the cyberspace, black-hat hackers have enormously taken undue advantages of such mobility to perpetrate numerous financial frauds against individuals and corporate organizations. The impact of their attacks on the economic sustainability of corporate organizations is of obvious policy relevance, but little is empirically known about it. This study examined the role cyberspace plays in aiding the criminal activities of black-hat hackers and the impact of such malicious attacks on the economic sustainability of corporate organizations in Cross River State, Nigeria. An exploratory cross-sectional survey research design that allows the triangulation of quantitative and qualitative methods was adopted. Questionnaires were distributed to 1,074 respondents purposively selected from 18 financial institutions, 4 telecommunication network providers, and 2 manufacturing companies, while an in-depth interview was conducted on 13 participants across the selected corporate organizations. The presentation of data was done using frequency distribution tables, charts and relevant narratives. Chi-square was used to test the hypothesis, and content analyzing was adopted to analyze the qualitative data. The study found that corporate organizations with previous hacking experience are more likely to perceive black-hat hacking as affecting the productivity of their organization. The study, therefore, recommends that corporate organizations should strengthen their firewalls; educate their staff on the dangers of visiting unsafe websites, and downloading unverified attachments or clicking on links in unfamiliar emails.