Reviewerly/exHarmony huggingface dataset:
Public Participation Consortium Blockchain for Smart City Governance

Smart cities have become a trend with improved efficiency, resilience, and sustainability, providing citizens with high quality of life. With the increasing demand for a more participatory and bottom–up governance approach, citizens play an active role in the process of policy making, revolutionizing the management of smart cities. In the example of urban infrastructure maintenance, the public participation demand is more remarkable as the infrastructure condition is closely related to their daily life. Although blockchain has been widely explored to benefit data collection and processing in smart city governance, public engagement remains a challenge. In this article, we propose a novel public participation consortium blockchain system for infrastructure maintenance that is expected to encourage citizens to actively participate in the decision-making process and enable them to witness all administrative procedures in a real-time manner. To that aim, we introduced a hybrid blockchain architecture to involve a verifier group, which is randomly and dynamically selected from the public citizens, to verify the transaction. In particular, we devised a private-prior peer-prediction-based truthful verification mechanism to tackle the collusion attacks from public verifiers. Then, we specified a Stackelberg-game-based incentive mechanism for encouraging public participation. Finally, we conducted extensive simulations to reveal the properties and performances of our proposed blockchain system, which indicates its superiority over other variations.
Improved Harris Hawks Optimization Algorithm for Workflow Scheduling Challenge in Cloud–Edge Environment

Edge computingZivkovic, Miodrag Bezdan, Timea Strumberger, Ivana Bacanin, Nebojsa Venkatachalam, K. is a relatively novel technology, which is closely related to the concepts of the Internet of things and cloud computing. The main purpose of edge computing is to bring the resources as close as possible to the clients, to the very edge of the cloud. By doing so, it is possible to achieve smaller response times and lower network bandwidth utilization. Workflow scheduling in such an edge–cloud environment is considered to be an NP-hard problem, which has to be solved by a stochastic approach, especially in the scenario of multiple optimization goals. In the research presented in this paper, a modified Harris hawks optimization algorithm is proposed and adjusted to target cloud–edge workflow scheduling problem. Simulations are carried out with two main objectives—cost and makespan. The proposed experiments have used real workflow models and evaluated the proposed algorithm by comparing it to the other approaches available in the recent literature which were tested in the same simulation environment and experimental conditions. Based on the results from conducted experiments, the proposed improved Harris hawks optimization algorithm outperformed other state-of-the-art approaches by reducing cost and makespan performance metrics.
Complementary Relation Contrastive Distillation

Knowledge distillation aims to transfer representation ability from a teacher model to a student model. Previous approaches focus on either individual representation distillation or inter-sample similarity preservation. While we argue that the inter-sample relation conveys abundant information and needs to be distilled in a more effective way. In this paper, we propose a novel knowledge distillation method, namely Complementary Relation Contrastive Distillation (CRCD), to transfer the structural knowledge from the teacher to the student. Specifically, we estimate the mutual relation in an anchor-based way and distill the anchor-student relation under the supervision of its corresponding anchor-teacher relation. To make it more robust, mutual relations are modeled by two complementary elements: the feature and its gradient. Furthermore, the low bound of mutual information between the anchor-teacher relation distribution and the anchor-student relation distribution is maximized via relation contrastive loss, which can distill both the sample representation and the inter-sample relations. Experiments on different benchmarks demonstrate the effectiveness of our proposed CRCD.
Trends in human activity recognition with focus on machine learning and power requirements

The advancement and availability of technology can be employed to improve our daily lives. One example is Human Activity Recognition (HAR). HAR research has been mainly explored using imagery but is currently evolving to the use of sensors and has the ability to have a positive impact, including individual health monitoring and removing the barrier of healthcare. To reach a marketable HAR device, state-of-the-art classifications and power consumption methods such as convolutional neural network (CNN), data compression and other emerging techniques are reviewed here. The review of the current literature creates a foundation in HAR and addresses the lack of available HAR datasets, recommendation of classification and power reduction techniques, current drawbacks and their respective solutions, as well as future trends in HAR. The lack of publicly available datasets makes it difficult for new users to explore the field of HAR. This paper dedicates a section to publicly available datasets for users to access. Finally, a framework is suggested for HAR applications, which envelopes the current literature and emerging trends in HAR.
Virtual Reality in Clinical Psychology

From a technological viewpoint, virtual reality (VR) is a set of fancy technologies: a helmet, trackers, and a 3D visualizing system. However, from a psychological viewpoint, VR is simultaneously a simulative, a cognitive, and an embodied technology. These features make VR the perfect tool for experiential assessment and learning with great clinical potential. This potential is already supported by clinical outcomes. Two recent meta-reviews assessing more than 53 systematic reviews and meta-analyses support its use in anxiety disorders, pain management, and eating and weight disorders, with long-term effects that generalize to the real world. Recent studies have also provided preliminary support for the use of VR in the assessment and treatment of psychosis, addictions, and autism.
Cybersecurity in the Internet of Medical Things

The Internet of Things has spawned a new fleet of medical devices replete with improved sensing and actuating capabilities. Preemptive mitigation of the cyber risks that arise in this hyperconnected space is needed to ensure continued patient safety. The aim of this paper is to analyse the robustness of existing policy measures in securing the Internet of Medical Things technologies. The regulatory ecosystem in the US is primarily discussed herein and includes regulatory frameworks for industry, public-private partnerships, and transparency initiatives. A qualitative review of the medical cybersecurity literature was performed with collation of federal and international legal documents, policy reports, industry frameworks, cyberbreach analyses, and scientific journal articles. Regulatory guidance documents introduced to date that address cybersecurity in the Internet of Medical Things place a key emphasis on device identification, legacy device management, enhanced physical security, and breach detection. Recent oversight trends aim to bolster federal authority around the enforcement of baseline security safeguards. Additional regulatory guidance is needed to mitigate risks in the Internet of Medical Things devices conferred by retrofitted IT infrastructures, edge-to-cloud interfaces, and off-the-shelf device components. Recent advancements in the cyber realm also raise the possibility of novel attack vectors, autonomous cyber-physical systems, and quantum computing threats. Interventions to promote awareness and security hygiene around the Internet of Medical Things devices can empower end users and facilitate smooth incident response. The rise “Smart” technologies such as voice assistants and adaptable at-home appliances moves us closer to a more personalized world that can enhance our daily lives. The field of medicine will be changed by these next generation “Internet of Things” technologies that possess the ability to interact with their users and their surrounding environment. These technologies are important because the precision with which medical devices interact with patients, healthcare workers, and other technology can have huge impact on patient care. For all of their promise, the increased interconnectivity that these devices possess also confers additional cybersecurity risks. Policy regulation and public health preparedness are critical for ensuring the benefits of these emerging technologies do not come at the expense of patient safety and privacy. In this Review, we discuss cybersecurity regulation in the Internet of Medical Things and highlight novel threats still in need of address at the policy and public-health levels.
DeepHyperion: exploring the feature space of deep learning-based systems through illumination search

Deep Learning (DL) has been successfully applied to a wide range of application domains, including safety-critical ones. Several DL testing approaches have been recently proposed in the literature but none of them aims to assess how different interpretable features of the generated inputs affect the system's behaviour.
Differential evolution with mixed mutation strategy based on deep reinforcement learning

The performance of differential evolution (DE) algorithm significantly depends on mutation strategy. However, there are six commonly used mutation strategies in DE. It is difficult to select a reasonable mutation strategy in solving the different real-life optimization problems. In general, the selection of the most appropriate mutation strategy is based on personal experience. To address this problem, a mixed mutation strategy DE algorithm based on deep Q-network (DQN), named DEDQN is proposed in this paper, in which a deep reinforcement learning approach realizes the adaptive selection of mutation strategy in the evolution process. Two steps are needed for the application of DQN to DE. First, the DQN is trained offline through collecting the data about fitness landscape and the benefit (reward) of applying each mutation strategy during multiple runs of DEDQN tackling the training functions. Second, the mutation strategy is predicted by the trained DQN at each generation according to the fitness landscape of every test function. Besides, a historical memory parameter adaptation mechanism is also utilized to improve the DEDQN. The performance of the DEDQN algorithm is evaluated by the CEC2017 benchmark function set, and five state-of-the-art DE algorithms are compared with the DEDQN in the experiments. The experimental results indicate the competitive performance of the proposed algorithm.
Explaining recommender systems fairness and accuracy through the lens of data characteristics

The impact of data characteristics on the performance of classical recommender systems has been recently investigated and produced fruitful results about the relationship they have with recommendation accuracy. This work provides a systematic study on the impact of broadly chosen data characteristics (DCs) of recommender systems. This is applied to the accuracy and fairness of several variations of CF recommendation models. We focus on a suite of DCs that capture properties about the structure of the user–item interaction matrix, the rating frequency, item properties, or the distribution of rating values. Experimental validation of the proposed system involved large-scale experiments by performing 23,400 recommendation simulations on three real-world datasets in the movie (ML-100K and ML-1M) and book domains (BookCrossing). The validation results show that the investigated DCs in some cases can have up to 90% of explanatory power – on several variations of classical CF algorithms –, while they can explain – in the best case – about 40% of fairness results (measured according to user gender and age sensitive attributes). Therefore, this work evidences that it is more difficult to explain variations in performance when dealing with fairness dimension than accuracy.
Head pose estimation using deep neural networks and 3D point clouds

In this paper, we propose head pose estimation using deep neural networks and 3D point cloud. Unlike existing methods that either take 2D RGB image or 2D depth image as input, we adopt 3D point cloud data generated from depth to estimate 3D head poses. To further improve robustness and accuracy of head pose estimation, we classify 3D angles of head poses into 36 classes with 5 ∘ interval and predict the probability of each angle in a class based on multi-layer perceptron (MLP). While traditional iterative methods for head model construction require high computation and memory costs, the proposed method is lightweight and computationally efficient by utilizing a sampled 3D point cloud as input combined with a graph convolutional neural network (GCNN). Experimental results on Biwi Kinect Head Pose dataset show that the proposed method achieves outstanding performance in head pose estimation and outperforms state-of-the-art ones in terms of accuracy.
Challenges and Paradoxes in Decolonising HCI: A Critical Discussion

Abstract The preponderance of Western methods, practices, standards, and classifications in the manner in which new technology-related knowledge is created and globalised has led to calls for more inclusive approaches to design. A decolonisation project is concerned with how researchers might contribute to dismantling and re-envisioning existing power relations, resisting past biases, and balancing Western heavy influences in technology design by foregrounding the authentic voices of the indigenous people in the entire design process. We examine how the establishment of local Global South HCI communities (AfriCHI and ArabHCI) has led to the enactment of decolonisation practices. Specifically, we seek to uncover how decolonisation is perceived in the AfriCHI and ArabHCI communities as well as the extent to which both communities are engaged with the idea of decolonisation without necessarily using the term. We drew from the relevant literature, our own outsider/insider lived experiences, and the communities’ responses to an online anonymised survey to highlight three problematic but interrelated practical paradoxes: a terminology, an ethical, and a micro-colonisation paradox. We argue that these paradoxes expose the dilemmas faced by local non-Western researchers as they pursue decolonisation thinking. This article offers a blended perspective on the decolonisation debate in HCI, CSCW, and the practice-based CSCW scholarly communities and invites researchers to examine their research work using a decolonisation lens.
Deep CNN Model based on VGG16 for Breast Cancer Classification

Deep learning (DL) technologies are becoming a buzzword these days, especially for breast histopathology image tasks, such as diagnosing, due to the high performance obtained in image classification. Among deep learning types, Convolutional Neural Networks (CNN) are the most common types of DL models utilized for medical image diagnosis and analysis. However, CNN suffers from high computation cost to be implemented and may require to adapt huge number of parameters. Thus, and in order to address this issue; several pre-trained models have been established with the predefined network architecture. In this study, a transfer learning model based on Visual Geometry Group with 16-layer deep model architecture (VGG16) is utilized to extract high-level features from the BreaKHis benchmark histopathological images dataset. Then, multiple machine learning models (classifiers) are used to handle different Breast Cancer (BC) histopathological image classification tasks mainly: binary and multiclass with eight-class classifications. The experimental results on the public BreakHis benchmark dataset demonstrate that the proposed models are better than the previous works on the same dataset. Besides, the results show that the proposed models are able to outperform recent classical machine learning algorithms.
A Multi-Task Learning Approach to Hate Speech Detection Leveraging Sentiment Analysis

The rise of social media platforms has significantly changed the way our world communicates, and part of those changes includes a rise in inappropriate behaviors, such as the use of aggressive and hateful language online.Detecting such content is crucial to filtering or blocking inappropriate content on the Web.However, due to the huge amount of data posted every day, automatic methods are essential for identifying this type of content.Seeking to address this issue, the Natural Language Processing community is increasingly involved in testing a wide range of techniques for hate speech detection.While achieving promising results, these techniques consider hate speech detection as the sole optimization objective, without involving other related tasks such as polarity and emotion classification that are strongly linked to offensive behavior.In this paper, we propose the first Multi-task approach that leverages the shared affective knowledge to detect hate speech in Spanish tweets, using a well-known Transformer-based model.Our results show that the combination of both polarity and emotion knowledge helps to detect hate speech more accurately across datasets.
Interplay between upsampling and regularization for provider fairness in recommender systems

Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in recommended lists. Prior work showed that certain minority groups of providers, characterized by a common sensitive attribute (e.g., gender or race), are being disproportionately affected by indirect and unintentional discrimination. Our study in this paper handles a situation where ($i$) the same provider is associated with multiple items of a list suggested to a user, ($ii$) an item is created by more than one provider jointly, and ($iii$) predicted user-item relevance scores are biasedly estimated for items of provider groups. Under this scenario, we assess disparities in relevance, visibility, and exposure, by simulating diverse representations of the minority group in the catalog and the interactions. Based on emerged unfair outcomes, we devise a treatment that combines observation upsampling and loss regularization, while learning user-item relevance scores. Experiments on real-world data demonstrate that our treatment leads to lower disparate relevance. The resulting recommended lists show fairer visibility and exposure, higher minority item coverage, and negligible loss in recommendation utility.
The Use Of Blockchain As A Resource For Combating Corruption In Global Shipping: An Interpretive Case Study

Corruption is one of the most troubling societal challenges facing businesses today. Businesses have been combating corruption in fragmented ways, sometimes by creating anti-corruption policies applicable to certain stakeholders and, at other times, by harnessing digital technologies. Recently, the power of blockchain, with its capacity to provide full transactional disclosure and thereby reduce uncertainty, insecurity, and ambiguity in transactions, has been touted as being a game changer in the fight against corruption. Based on a study of the global shipping industry, we find that blockchain mitigates both process and document-related corruption. Based on these findings, we develop an understanding of how corruption may be combated using both social and digital/informational resources, including blockchain technology. Our model, drawing on past work on corruption, shows the complex interplay between identity, institutional actors, technical and other resources, and practices, and we develop conditions that could be effective in fighting corruption by using technologies such as blockchain.
COVID-19, cryptocurrencies bubbles and digital market efficiency: sensitivity and similarity analysis

This paper compares the degree of cryptocurrency market efficiency during the pre- and post COVID-19 pandemic with the bubble and non-bubble periods of cryptocurrency markets. Furthermore, it examines and clusters eighteen cryptocurrencies by exploring their market efficiency similarity. Comparing the cryptocurrency bubble periods with the COVID-19 pandemic, the results indicate that this pandemic has the highest impact on cryptocurrency market efficiency. Interestingly, using the dynamic time warping clustering approach, we found evidence on the presence of three clusters that essentially represent mining coins, non-mining coins and token categorizations .
Chain of Values: Examining the Economic Impacts of Blockchain on the Value-Added Tax System

Blockchain technology can benefit inter-organizational activities by improving data integrity, increasing transaction transparency, and decreasing transaction costs. In this paper, we discuss the strategic and economic value of this technology by applying it to the value-added tax (VAT) reporting system, focusing on blockchain’s characteristic traceability. By effectively increasing financial transparency, the application of blockchain to the VAT system can prevent VAT-related fraud (e.g., underreported VAT) that can arise due to the information asymmetry that exists at different stages of the supply chain. We develop a game theoretical model that involves a retailer and two vendors in order to study the players’ strategic decisions regarding blockchain adoption and to examine the effects on social welfare. We also show how the decision to adopt blockchain depends on considerations such as adoption costs, the vendors’ VAT reporting behavior, the retailer’s profit margins, and inter-vendor competition. Furthermore, we find that under certain conditions, policymakers can increase social welfare by providing subsidies to encourage blockchain adoption.88 Sun Yin, H.H.; Langenheldt, K.; Harlev, M.; Mukkamala, R.R.; and Vatrapu, R. Regulating cryptocurrencies: A supervised machine learning approach to de-anonymizing the bitcoin blockchain. Journal of Management Information Systems, 36, 1 (2019), 37–73.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]