Reviewerly/exHarmony huggingface dataset:
Are Cryptocurrencies and Decentralized Finance Democratic?

Abstract This chapter defines the building blocks of Web3 to explore its implications for sustainable democracy. It explains the crash of the Terra ecosystem and compares it with the implosion of FTX, both to underscore the dark side of networks built on blockchain technology and to illuminate how all cryptocurrencies (crypto), as well as all investors, are not created equal. The chapter weighs the costs and benefits of crypto and decentralized finance (DeFi) at the present moment and explores emergent regulation. It argues that cryptocurrencies and DeFi will only serve democracy if governments and international organizations establish the right incentive structure for innovators and safeguards for investors in this space.
Responsible Software Engineering: Requirements and Goals

Abstract In this chapter, we provide an introduction to the discipline of requirements engineering as part of the software engineering process. We indicate how to elicit, articulate, and organize the goals of complex software systems as an explicit expression of the requirements that the proposed or existing software system is expected to achieve and maintain, including what the system should avoid performing. We advocate that system requirements goals can and should be used to explicitly capture, express, and reason about the diverse digital humanism values which are of concern in socio-technical systems. This is an essential aspect of responsible software engineering.
The Road Less Taken: Pathways to Ethical and Responsible Technologies

Abstract Technology is no longer just about technology—now it is about living. So, how do we have ethical technology that creates a better life and a better society? Technology must become truly “human-centered,” not just “human-aware” or “human-adjacent.” Diverse users and advocacy groups must become equal partners in initial co-design and in continual assessment and management of information systems with human, social, physical, and technical components. But we cannot get there without radically transforming how we think about, develop, and use technologies. In this chapter, we explore new models for digital humanism and discuss effective tools and techniques for designing, building, and maintaining sociotechnical systems that are built to be and remain continuously ethical, responsible, and human-centered.
How Blockchain Technology Can Help to Arrive at Fair Ecosystems and Platforms

Abstract Digital ecosystems and platforms are an important part of the economy. However, specifically the tech-oriented platforms are often considered as “unfair.” In this chapter, we aim to more precisely articulate this feeling of unfairness. We consider fairness in digital ecosystems and platforms as fair if a decision as a result of applying a rule should accommodate all applicable moral distinctions and reasons for all actors involved. However, fairness is not only related to the operations of a digital ecosystem or platform. Fairness of digital ecosystems and platforms requires fair governance also. We consider fair governance as a prerequisite for fair governance, because the concerns of all stakeholders can then be included in the decision process. As a second assumption, we argue that decentralized decision-making contributes to fair governance. If this assumption holds, it is worthwhile to investigate how decentralized governance can be implemented and supported by information technology. We explain how blockchain technology, with consensus reaching at its core, can support such decentralized decision-making.
A Survey on Swarm Robotics for Area Coverage Problem

The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.
The Threat of Surveillance and the Need for Privacy Protections

Abstract In recent years, and since the introduction of the General Data Protection Regulation (GDPR) in particular, we have seen an increased interest (and concern) about the amount of private information that is collected by the applications and services we use in our daily lives. The widespread collection and commodification of personal data has been mainly driven by companies collecting, mining, and selling user profiles for targeted advertisement, a practice also referred to as “surveillance capitalism.” However, as we detail in this chapter, this is not the only form of surveillance and can be necessary and even beneficial by increasing the safety of citizens—if it is aligned with the principles of digital humanisms in providing transparency, oversight, and accountability. We also detail mechanisms users can deploy to protect their own privacy, as well as mechanisms that help to develop more privacy-friendly technologies.
The Social Responsibilities of Scientists and Technologists in the Digital Age

Abstract What responsibilities do digital scientists and professionals have in serving society and the public, beyond doing “purely” “fundamental” scientific, technological, or academic inquiry alone? Now that “the digital” has become pervasive in society, even in normal people’s lives, these are pressing questions that call for answers. They go beyond individual ethical considerations or professional codes of conduct, as society-systemic aspects heavily come into play. We explore some of the complex historical and contemporary relationships between technology and society as a way to formulate useful insights for digital ethics and governance of digital technologies today.
Discount-Based Cloud Resource Management Using Cloud Broker

Businesses require ways to check asset use in order not to disregard Service-Level Agreements and guarantee that assets are efficiently distributed to specific departments. A method of allocating, managing, and monitoring cloud resources is provided by cloud resource management systems. They permit one to make and oversee pools of assets, allocate those assets to explicit clients or applications, and track how they are being utilized. Users are able to request and provision resources as needed through a self-service interface provided by a good cloud resource management system. When using a cloud provider, businesses that manage their own resources frequently achieve greater efficiency. A portion of the ways in which IT robotization helps organizations deal with their assets involves setting boundaries for the greatest and least number of virtual machines (VMs), setting look-ahead times for VMs to appear, and halting VMs when they are inactive and, at that point, not needed for operations. Moreover, IT organizations might profit from developing a structure of warnings to further develop perceivability and control over asset utilization. Cloud computing is a model used to enable omnipresent, helpful, on-request network admittance to a common pool of configurable processing assets that can be quickly provisioned and delivered with negligible administrative exertion and without specialist organizations. Distributed computing is a financial model for huge corporations, as it removes the requirement for beginning interest in capital or framework costs.
Heuristic-Driven Approach for Efficient Workflow Scheduling in Infrastructure as a Service Using Hybrid Optimization Algorithms

The recent trend of Infrastructure as a Service is a service that provides IT components, like computing and storage, on a pay-as-you-go basis over the web. Today, IaaS has endless applications related to the businesses using it. After conducting a contextual analysis, we note that organizations have moved most of their activities to the cloud. For the most part, this implies that they presently use Software as a Service (SaaS) applications rather than authorized on-prem applications and that they have moved their restrictive programming and frameworks from a server farm to IaaS providers. For years, cloud experts have discussed whether there is truly such an amazing concept as a confidential cloud in IaaS, that is, an on-premises cloud in the client’s server farm. IaaS has undergone an extensive transformation from conventional equipment server farms to a virtualized and cloud-based framework. By eliminating the connection between equipment and working programs and middleware, companies found that they could scale data requirements rapidly and effectively to fulfill their responsibilities. To utilize IaaS, a business can buy a particular resource from a cloud computing supplier to reorganize its computing framework so clients can concentrate on tasks like acquiring and overseeing their own computer programs. This involves incorporating things like computer servers, applications for websites, and versatile gadgets. Along these lines, an enterprise can organize its own equipment infrastructure while having the required assets to carry out a plan of action.
Wind Influence on the Spatiotemporal Forecast of Global Horizontal Irradiance

The connection between solar irradiance and wind is a topic of interest in the field of renewable energies, as wind data have proven to be effective predictors of solar energy, being indicators of cloud movement and atmospheric conditions. This study focuses the use of decision tree-based algorithms (random forest, XGBoost and light gradient boosting machine, and LightGBM) to analyse the impact of the meridional and zonal wind components as input variables. In the study, past observations of neighbours were included as predictors to include a spatiotemporal analysis. The studied models were trained on the open, well-established OIH dataset (containing data from Oahu Island, Hawaii, located in the United States of America) featuring predominantly northeasterly winds. In the post-training analysis, it was found that the inclusion of the wind components resulted in a mean improvement of approximately 1% in the forecast skill (FS) score for all models, with the XGBoost model being the best performing model (with a 27.63% FS score).
Introduction

With the exponential growth of information technology, multimedia digital technology has become an indispensable part of people&apos;s daily lives. However, multimedia data security issues are particularly prominent in the current Internet of Things (IoT) environment. Issues such as user privacy leakage, malicious data tampering, and infringement of intellectual property rights of multimedia data on platforms are not uncommon. Strengthening technical research and exploration is necessary to effectively solve the security issues of multimedia data. In this context, digital watermarking technology and encryption algorithms have become the main means of protecting multimedia data security. This study classifies the contents of multimedia and analyzes multimedia security technology based on the background information of multimedia technology. After summarizing the basic concepts of multimedia encryption technology, this study reviews the literature on multimedia watermarking and encryption technology. Furthermore, this study combines multimedia digital signature technology and multimedia authentication technology to analyze the network system structure of multimedia security. The research results demonstrate that digital watermarking technology and digital encryption algorithms can improve the application security of multimedia digital technology to a certain extent. The findings of this study hold practical reference value for optimizing the network structure of multimedia information security. By providing a comprehensive analysis of multimedia security technology, this study highlights the significance of technical research and exploration in addressing security issues surrounding multimedia data in the current IoT environment.
Students’ Attitudes About STEM Teaching Case Study From Brčko District of Bosnia and Herzegovina

The knowledge that is based on science, technology, engineering, and mathematics is the basis for the development of any country. Less developed countries lack experts in these areas. Therefore, the ENABLE-BIH project (Enhancing and Advancing Basic Learning and Education in Bosnia and Herzegovina) was introduced in Bosnia and Herzegovina, which aims to improve the situation in the education sector. This study included the Public Institution “Ninth Elementary School” in Brcko District of Bosnia and Herzegovina in which this project was implemented. The study included a total of 125 students from this school. The aim of this study is to examine the difference between attitudes about STEM from the point of view of gender differences and the age of students. After the data were collected, the statements were grouped into appropriate factors using factor analysis. The factor analysis showed that five factors stand out in this research. The results of multiple regression analysis showed that there is no difference between students ‘attitudes regarding gender differences, while there is a difference regarding students’ age. The results of this research showed that the ENABLE-BIH project delivered good results and suggests the importance of implementing similar projects in the future.
Machine/Deep learning techniques for multimedia security

Multimedia security based on Machine Learning (ML)/Deep Learning (DL) is a field of study that focuses on using ML/DL techniques to protect multimedia data such as images, videos, and audio from unauthorized access, manipulation, or theft. Developing and implementing algorithms and systems that use ML/DL techniques to detect and prevent security breaches in multimedia data is the main subject of this field. These systems use techniques like watermarking, encryption, and digital signature verification to protect multimedia data. The advantages of using ML/DL in multimedia security include improved accuracy, scalability, and automation. ML/DL algorithms can improve the accuracy of detecting security threats and help identify multimedia data vulnerabilities. Additionally, ML models can be scaled up to handle large amounts of multimedia data, making them helpful in protecting big datasets. Finally, ML/DL algorithms can automate the process of multimedia security, making it easier and more efficient to protect multimedia data. The disadvantages of using ML/DL in multimedia security include data availability, complexity, and black box models. ML and DL algorithms require large amounts of data to train the models, which can sometimes be challenging. Developing and implementing ML algorithms can also be complex, requiring specialized skills and knowledge. Finally, ML/DL models are often black box models, which means it can be difficult to understand how they make their decisions. This can be a challenge when explaining the decisions to stakeholders or auditors. Overall, multimedia security based on ML/DL is a promising area of research with many potential benefits. However, it also presents challenges that must be addressed to ensure the security and privacy of multimedia data.
Prompting Metalinguistic Awareness in Large Language Models: ChatGPT and Bias Effects on the Grammar of Italian and Italian Varieties

We explore ChatGPT’s handling of left-peripheral phenomena in Italian and Italian varieties through prompt engineering to investigate 1) forms of syntactic bias in the model, 2) the model’s metalinguistic awareness in relation to reorderings of canonical clauses (e.g., Topics) and certain grammatical categories (object clitics). A further question concerns the content of the model’s sources of training data: how are minor languages included in the model’s training? The results of our investigation show that 1) the model seems to be biased against reorderings, labelling them as archaic even though it is not the case; 2) the model seems to have difficulties with coindexed elements such as clitics and their anaphoric status, labeling them as ‘not referring to any element in the phrase’, and 3) major languages still seem to be dominant, overshadowing the positive effects of including minor languages in the model’s training.
Differential Evolution-Based Weighted Voting Stacking Ensemble Classifier for Highly Skewed Binary Data Distribution

In an era where cybercrime is getting more and more complex by day, the accuracy in classifying credit card transactions is an important issue. This chapter argues if the weighted voting stacking ensemble method that combines various classifier models can be utilized as solution to this issue. But in using such solution, selecting the appropriate weights of classifier models for the correct classification of credit card transactions is a problem, which can be viewed as a problem of weights optimization. This chapter proposes to use the differential evolution optimization method as approach to define the appropriate weight function for the weight voting stacking ensemble method of various classification methods. It is found that the number of true positive transactions generated by the stacking ensemble method is exceeded (0.7% vs 85.4%) by that of the true positives generated using the differential evolution optimization.
Quantum intelligence in medicine: Empowering thyroid disease prediction through advanced machine learning

Abstract The medical information system is rich in datasets, but no intelligent systems can easily analyse the disease. Recently, ML (Machine Learning)‐based algorithms have acted as a handy diagnostic tool to identify whether a person is affected by thyroid or not. However, they produced classification with low accuracy and led to misclassification. Hence, the proposed system combines quantum computing with ML techniques to enhance computational power and precision. The system employs modified QPSO (Quantum Particle Swarm Optimisation) for feature selection since its searching performance is better than that of conventional PSO for selecting the optimum global position of the particle, thus selecting the relevant feature. Whereas, the QSVM (Quantum Support Vector Machine) is implemented for more accurate classification than classical SVM, as it tends to capture complex patterns in data produced due to high dimensional feature space applied by quantum kernel functions. This combination of modified QPSO and QSVM tends to increase the performance accuracy significantly. The efficiency of the proposed model is measured based on derivative parameters, such as F‐1‐score, recall, precision and accuracy, with corresponding confusion matrix and ROC. Further, the classification is compared with other traditional approaches to predict the accuracy of the proposed model with traditional methods.
Institutionalization of Hate Speech as a Daily Media Practice: The Case of “Wiadomości” TVP1

In the article, I discuss how hate speech becomes institutionalized in the media discourse thanks to the news media and how it is included in the media practice by public television in Poland. Hate speech is based on expressive, conflict-prone, and black-and-white schemes, employing language characterized by emotions, negative stigmatization, and simplifications. As a case study, I analyze the “Wiadomości” (“News”) daily program on Polish public TV. Over eight years “Wiadomości” has become a broadcast presenting ideological political positions, favoring the party in power, being the primary means for propaganda, as hate speech aimed at opposition parties and their leaders has become customary content in broadcasts. It takes various forms, including the biased selection of content in programs, the structure of information, information strips, and comments of speakers and reporters presenting content. According to the media law in Poland, a public broadcaster has to be an impartial informant while performing its mission. However, TVP became a “hate speech factory” aimed towards political opposition. Using democratic principles, the ruling party made public media publish the content following the political program of the rulers, thus institutionalizing the undemocratic practices.
Effects of Constant and Time-Varying Display Lag on DVP and Cybersickness When Making Head-Movements in Virtual Reality

When HMD users move their heads in virtual reality (VR), display lag creates differences between their virtual and physical head pose (DVP). This study examined whether objective estimates of DVP could predict experiences of cybersickness during simulations with three different types of added lag: (1) Constant lag (where the display was always delayed by 250 ms); (2) Predictable time-varying lag (where delays alternated between 0 and 250 ms every 5 s); and (3) Random time-varying lag (where delays alternated between 0 and a randomly determined value, up to 250 ms, every 1–5 s). Constant, Predictable, and Random added lag were found to generate similar levels of cybersickness—with all three conditions producing more severe sickness than the Baseline lag control. Consistent with our DVP hypothesis, the spatial magnitude and temporal dynamics of our participants’ DVP were both found to be reliable predictors of their cybersickness in all display lag conditions tested.
Performance Evaluation of Data Stream Clustering Algorithm on Parameter Specification

Parameter specification remains a difficult task in data stream clustering as density-based algorithms hyperparameters tuning to their optimal values are often difficult to determine. This paper investigates the sensitivity of parameter tuning on DenStream, a data stream clustering algorithm. The effects on different noise levels are evaluated for the DenStream and two chosen benchmark algorithms, CluStream and ClusTree algorithms, using both synthetic and real-world datasets, and several performance metrics. It was found that DenStream outperforms CluStream and ClusTree on some of those metrics.
On Algorithmic Content Moderation

Abstract This chapter provides an overview of the challenges involved in algorithmic content moderation. Content moderation is the organized practice of screening user-generated content (UGC) on Internet sites, social media, and other online outlets to determine the appropriateness of the content for a given site, locality, or jurisdiction. The most common technical approaches consist in using classifier systems that assign predefined category labels to individual posts. We briefly introduce pre- and post-moderation and provide real-world examples of algorithmic moderation systems used by an Austrian daily newspaper. We point to significant challenges of moderation such as the ambiguities of natural language and the implications for freedom of expression. We conclude with issues that algorithmic content moderation raises for societal power relations and democratic control.