Reviewerly/exHarmony huggingface dataset:
Towards a legal qualification of digital intimidation against environmental defenders: navigating core components and characteristics

ABSTRACTDigital technologies are becoming increasingly important for environmental defenders. Such technologies offer them possibilities to speak out and/or take action, but also increase the (digital) risks they face as a result of their (online) involvement. Recently, significant concerns have been raised about the use of various forms of online and technology-facilitated forms of harassment or ‘digital intimidation’ against environmental defenders. This raises the question of whether environmental defenders are adequately protected by the current legal framework. To assess this, it is essential to first understand the phenomenon of digital intimidation. This paper therefore aims to identify the ‘core’ components and characteristics relevant to digital intimidation to enable further legal qualification and analysis. As such, we propose a digital intimidation navigation tool that can facilitate a more sophisticated understanding of the different tactics used, as well as a fine-grained analysis of the existing legal framework.KEYWORDS: Digital intimidationEnvironmental defendersDigital intimidation navigator Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 This is also reflected in the Sustainable Development Goals. As set out in the global indicator framework for the Goals and targets of the 2030 Agenda, indicator 16.10.1 of Goal 16 requires quantification of the number of verified cases of ‘killing, kidnapping, enforced disappearance, arbitrary detention and torture’ of journalists, associated media personnel, trade unionists and human rights advocates.2 Harassment’ is defined as ‘the act of making repeated attacks on an enemy’ (see: https://www.oxfordlearnersdictionaries.com/definition/english/harassment?q=harassment) or ‘illegal behaviour towards a person that causes mental or emotional suffering, which includes repeated unwanted contacts without a reasonable purpose, insults, threats, touching, or offensive language’ (see: https://dictionary.cambridge.org/dictionary/english/harassment). Emphasis added by the authors.3 ‘Intimidation’ is defined as ‘⁣the act of frightening or threatening somebody so that they will do what you want’. See: https://www.oxfordlearnersdictionaries.com/definition/english/intimidation?q=intimidation.4 As noted by the UN Special Rapporteur on Violence against Women in the 2018 report, new technologies will continue to give rise to new and different forms of online violence.5 Other sources of violence were their sources or contacts (15%); their colleagues (14%), political party officials/workers (10%) and rival media organisation staff (8%).Additional informationFundingThis work was supported by Bijzonder Onderzoeksfonds UGent.
Continuous Experimentation and Human Factors

In today’s rapidly evolving technological landscape, the success of tools and systems relies heavily on their ability to meet the needs and expectations of users. User-centered design approaches, with a focus on human factors, have gained increasing attention as they prioritize the human element in the development process. With the increasing complexity of software-based systems, companies are adopting agile development methodologies and emphasizing continuous software experimentation. However, there is limited knowledge on how to effectively execute continuous experimentation with respect to human factors within this context. This research paper presents an exploratory qualitative study for integrating human factors in continuous experimentation, aiming to uncover distinctive characteristics of human factors and continuous software experiments, practical challenges for integrating human factors in continuous software experiments, and best practices associated with the management of continuous human factors experimentation.
Promoting Learner Autonomy in Learning English Listening Skills through Mobile-Assisted Applications

The use of mobile-assisted applications such as Listen to English Daily Practice, LearnEnglish Podcasts, and VOA Learning English: AI+ has significantly contributed to learners' improvements in English skills. This mixed methods research aimed to investigate how mobile-assisted applications promote learner autonomy in and beyond English listening classes. To serve the research purposes, the study employed a two-month experimental teaching, class observations, two sets of questionnaires, and interview responses. The findings revealed that students showed a higher level of engagement, motivation, and responsibility in their learning. Moreover, students expressed their purposeful selection of learning content and effective use of their learning strategies, resulting in better listening competence. Most of them shared their positive attitudes toward the use of mobile-assisted applications. However, the students still revealed some difficulties, such as the insufficiency of teachers' supervision and feedback, as well as the gradual decrease in students' persistence and commitment to using those applications. Therefore, some pedagogical implications were suggested to help students mitigate some barriers and drawbacks and maximize the quality of learning English listening using mobile-assisted applications.
Face detection based on a human attention guided multi-scale model

Abstract Multiscale models are among the cutting-edge technologies used for face detection and recognition. An example is Deformable part-based models (DPMs), which encode a face as a multiplicity of local areas (parts) at different resolution scales and their hierarchical and spatial relationship. Although these models have proven successful and incredibly efficient in practical applications, the mutual position and spatial resolution of the parts involved are arbitrarily defined by a human specialist and the final choice of the optimal scales and parts is based on heuristics. This work seeks to understand whether a multi-scale model can take inspiration from human fixations to select specific areas and spatial scales. In more detail, it shows that a multi-scale pyramid representation can be adopted to extract interesting points, and that human attention can be used to select the points at the scales that lead to the best face detection performance. Human fixations can therefore provide a valid methodological basis on which to build a multiscale model, by selecting the spatial scales and areas of interest that are most relevant to humans.
Detecting covert disruptive behavior in online interaction by analyzing conversational features and norm violations

Disruptive behavior is a prevalent threat to constructive online engagement. Covert behaviors, like trolling, are especially challenging to detect automatically, because they utilize deceptive strategies to manipulate conversation. We illustrate a novel approach to their detection: analyzing conversational structures instead of focusing only on messages in isolation. Building on conversation analysis, we demonstrate that 1) conversational actions and their norms provide concepts for a deeper understanding of covert disruption, and that 2) machine learning, natural language processing and structural analysis of conversation can complement message-level features to create models that surpass earlier approaches to trolling detection. Our models, developed for detecting overt (aggression) as well as covert (trolling) behaviors using prior studies’ message-level features and new conversational action features, achieved high accuracies (0.90 and 0.92, respectively). The findings offer a theoretically grounded approach to computationally analyzing social media interaction, and novel methods for effectively detecting covert disruptive conversations online.
Expressive, Scalable, Mid-Air Haptics with Synthetic Jets

Non-contact, mid-air haptic devices have been utilized for a wide variety of experiences, including those in extended reality, public displays, medical, and automotive domains. In this work, we explore the use of synthetic jets as a promising and under-explored mid-air haptic feedback method. We show how synthetic jets can scale from compact, low-powered devices, all the way to large, long-range, and steerable devices. We built seven functional prototypes targeting different application domains, in order to illustrate the broad applicability of our approach. These example devices are capable of rendering complex haptic effects, varying in both time and space. We quantify the physical performance of our designs using spatial pressure and wind flow measurements, and validate their compelling effect on users with stimuli recognition and qualitative studies.
Attribute-Based Proxy Signature Scheme Supporting Flexible Threshold Predicate for UAV Networks

Unmanned aerial vehicle (UAV) is an attractive application because of its flexibility and economy. It may use a digital signature scheme to protect commands sent to UAVs. Moreover, the digital signature scheme should guarantee the real-time performance of UAVs executing commands and protect the signer’s privacy. Therefore, we proposed an attribute-based proxy signature (ABPS) scheme supporting flexible threshold predicate for UAV networks and proved its security. It has existential unforgeability under selective-predicate and chosen message attacks (EUF-sP-CMA) and can protect the signer’s privacy. We analyzed its computation costs based on experimental data and communication costs. The analysis results indicate that our ABPS scheme has less computation costs than other ABPS schemes and is at the same level as other ABPS schemes on communication costs.
Efficient and Expressive Search Scheme over Encrypted Electronic Medical Records

In recent years, there has been rapid development in computer technology, leading to an increasing number of medical systems utilizing electronic medical records (EMRs) to store their clinical data. Because EMRs are very private, healthcare institutions usually encrypt these data before transferring them to cloud servers. A technique known as searchable encryption (SE) can be used by healthcare institutions to encrypt EMR data. This technique enables searching within the encrypted data without the need for decryption. However, most existing SE schemes only support keyword or range searches, which are highly inadequate for EMR data as they contain both textual and digital content. To address this issue, we have developed a novel searchable symmetric encryption scheme called SSE-RK, which is specifically designed to support both range and keyword searches, and it is easily applicable to EMR data. We accomplish this by creating a conversion technique that turns keywords and ranges into vectors. These vectors are then used to construct index tree building and search algorithms that enable simultaneous range and keyword searches. We encrypt the index tree using a secure K-Nearest Neighbor technique, which results in an effective SSE-RK approach with a search complexity that is quicker than a linear approach. Theoretical and experimental study further demonstrates that our proposed scheme surpasses previous similar schemes in terms of efficiency. Formal security analysis demonstrates that SSE-RK protects privacy for both data and queries during the search process. Consequently, it holds significant potential for a wide range of applications in EMR data. Overall, our SSE-RK scheme, which offers improved functionality and efficiency while protecting the privacy of EMR data, generally solves the shortcomings of the current SE schemes.
Eye-Tracking System with Low-End Hardware: Development and Evaluation

Eye-tracking systems have emerged as valuable tools in various research fields, including psychology, medicine, marketing, car safety, and advertising. However, the high costs of the necessary specialized hardware prevent the widespread adoption of these systems. Appearance-based gaze estimation techniques offer a cost-effective alternative that can rely solely on RGB cameras, albeit with reduced accuracy. Therefore, the aim of our work was to present a real-time eye-tracking system with low-end hardware that leverages appearance-based techniques while overcoming their drawbacks to make reliable gaze data accessible to more users. Our system employs fast and light machine learning algorithms from an external library called MediaPipe to identify 3D facial landmarks. Additionally, it uses a series of widely recognized computer vision techniques, like morphological transformations, to effectively track eye movements. The precision and accuracy of the developed system in recognizing saccades and fixations when the eye movements are mainly horizontal were tested through a quantitative comparison with the EyeLink 1000 Plus, a professional eye tracker. Based on the encouraging registered results, we think that it is possible to adopt the presented system as a tool to quickly retrieve reliable gaze information.
TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis

Big graphs like social network user interactions and customer rating matrices require significant computing resources to maintain. Data owners are now using public cloud resources for storage and computing elasticity. However, existing solutions do not fully address the privacy and ownership protection needs of the key involved parties: data contributors and the data owner who collects data from contributors.We propose a Trusted Execution Environment (TEE) based solution: TEE-Graph for graph spectral analysis of outsourced graphs in the cloud. TEEs are new CPU features that can enable much more efficient confidential computing solutions than traditional software-based cryptographic ones. Our approach has several unique contributions compared to existing confidential graph analysis approaches. (1) It utilizes the unique TEE properties to ensure contributors' new privacy needs, e.g., the right of revocation for shared data. (2) It implements efficient access-pattern protection with a differentially private data encoding method. And (3) it implements TEE-based special analysis algorithms: the Lanczos method and the Nystrom method for efficiently handling big graphs and protecting confidentiality from compromised cloud providers.The TEE-Graph approach is much more efficient than software crypto approaches and also immune to access-pattern-based attacks. Compared with the best-known software crypto approach for graph spectral analysis, PrivateGraph, we have seen that TEE-Graph has 103-105 times lower computation, storage, and communication costs. Furthermore, the proposed access-pattern protection method incurs only about 10%-25% of the overall computation cost.Our experimentation showed that TEE-Graph performs significantly better and has lower costs than typical software approaches. It also addresses the unique ownership and access-pattern issues that other TEE-related graph analytics approaches have not sufficiently studied. The proposed approach can be extended to other graph analytics problems with strong ownership and access-pattern protection.
Heuristically optimized weighted feature fusion with adaptive cascaded deep network: a novel breast cancer detection framework using mammogram images

This paper has aimed to develop the Breast Cancer Detection (BCD) technique along with the mammography reports of the patients. For this proposed framework, the required images are collected from publicly available datasets. Further, the collected images are given into the phase of feature extraction, where the deep features based on texture features, ResNet, morphological features and density features are extracted and fused together. In addition, the weighted fused feature selection is performed with the fused features, in which the weight is optimised with the Modified Hybrid based Leader Optimization (MHLBO) obtained from Hybrid Leader-Based Optimization(HLBO) to elevate the feature performance on classification. Then, breast cancer can be classified with the developed Adaptive Cascaded Deep Network (ACDN) by combing the classification results of, Recurrent Neural Network (RNN), Gate Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). Here, the parameter tuning has occurred in all three classifiers using the same enhanced HLBO. Throughout the experimentation, the designed model shows 97% and 96% regarding accuracy and F1-score, respectively. The tentative outcomes show the efficacy of the designed BCD method is compared with other baseline approaches.
DyLFG: A Dynamic Network Learning Framework Based on Geometry

Dynamic network representation learning has recently attracted increasing attention because real-world networks evolve over time, that is nodes and edges join or leave the networks over time. Different from static networks, the representation learning of dynamic networks should not only consider how to capture the structural information of network snapshots, but also consider how to capture the temporal dynamic information of network structure evolution from the network snapshot sequence. From the existing work on dynamic network representation, there are two main problems: (1) A significant number of methods target dynamic networks, which only allow nodes to increase over time, not decrease, which reduces the applicability of such methods to real-world networks. (2) At present, most network-embedding methods, especially dynamic network representation learning approaches, use Euclidean embedding space. However, the network itself is geometrically non-Euclidean, which leads to geometric inconsistencies between the embedded space and the underlying space of the network, which can affect the performance of the model. In order to solve the above two problems, we propose a geometry-based dynamic network learning framework, namely DyLFG. Our proposed framework targets dynamic networks, which allow nodes and edges to join or exit the network over time. In order to extract the structural information of network snapshots, we designed a new hyperbolic geometry processing layer, which is different from the previous literature. In order to deal with the temporal dynamics of the network snapshot sequence, we propose a gated recurrent unit (GRU) module based on Ricci curvature, that is the RGRU. In the proposed framework, we used a temporal attention layer and the RGRU to evolve the neural network weight matrix to capture temporal dynamics in the network snapshot sequence. The experimental results showed that our model outperformed the baseline approaches on the baseline datasets.
Blockchain: Blocking Hassles in Healthcare

Healthcare institutions around the world are increasingly reliant on digital databases for the storage of medical data. The unprecedented growth of healthcare data’s scale and velocity has made it of paramount concern for the modern age. Digital databases are vulnerable and adversely affect both the patient and the healthcare industry as a whole. The risk of cyber threats can breach data and disrupt its integrity. Maintaining both data integrity and patient privacy is critical to healthcare organizations. Regulatory frameworks such as GDPR in the EU and HIPAA in the US are both bodies for compliance rules for maintaining healthcare data privacy. Unfortunately, the tendency of healthcare institutions to use proprietary systems creates isolated silos of data that become difficult to secure using traditional methods. A blockchain-based method provides a novel way of securing electronic healthcare records using a decentralized peer-to-peer based network on top of these isolated silos. Each block contains information and links to the other, forming a collective chain. This chain enables it to regulate on its own to store and share information instead of relying on a centralized system. Blockchain has many potential use cases in healthcare applications and can help in patient monitoring, storage, securing data, health information exchange, and clinical trial management, among others. The principle of decentralization and cryptography, at its core, will help transform the Healthcare system by improving the accessibility and security of patient information for the modern age. Through a systematic review of literature on Blockchain and healthcare data, this paper aims to explore the current application methods, challenges faced, open questions, data standards, and compliance issues that are core to implementing a Blockchain-based solution in the Healthcare industry. Further, the present study seeks to explore the concerns and scope of the blockchain experts operating in the healthcare industry.
Blockchain Distributed Ledger Technologies for Biomedical and Healthcare Applications

The distributed ledger technology in the healthcare sector has been reported to be very important in generating patient information for research, medication adherence, management of several bedside information about patients, pharmaceutical supply chain, and quality of care. Its application has been documented in the biomedical domain and blockchain technology such as custom, IOTA, NEM, Gcoin, JUICE, TenderMint, Multichain, Hyperledger Fabric, Ethereum, Bitcoin, as well as in data integrity, data auditing, data provenance, data versioning, access control and non-repudiation. Therefore, this chapter will give an overview based on the applications of Blockchain distributed ledger technologies for biomedical and healthcare systems.
Blockchain Technology as a Tool for Prediction and Prevention of the Spread of COVID-19

Blockchain is predicted to greatly transform the conventional methods of transacting between users, covering almost all sectors of the economy. While the expectations of blockchain technology are high, the actual impacts and benefits are still unclear, causing delays and skepticism in its adoption. As the coronavirus disease (COVID-19) continues to affect the world, businesses and governments scramble for answers in attempting to limit the impact of the pandemic. In order to assess the ability of blockchain technology in its efforts to minimize the impact of the pandemic and clearly define the challenges and prospects of blockchain, a structured literature review of peer-reviewed articles on block chain’s implementation and adoption in supply chain management, education, logistics and finance sectors was conducted. It is recommended that block chain’s implementation and adoption is not limited to the finance sector but can be applied in any sector, where it provides a decentralized network in which information is accessible and personal privacy and security are guaranteed. Therefore, the wider acceptance and implementation of blockchain in selected sectors of the economy, especially in finance and supply chain management, has proven that blockchain can be utilized by businesses, governments and health professionals in the fight against the virus by transforming the challenges into opportunities combined with the prominent essential enablers would fast track its wider adoption.
ULKB Logic: A HOL-Based Framework for Reasoning over Knowledge Graphs

ULKB Logic is an open-source framework written in Python for reasoning over knowledge graphs. It provides an interactive theorem prover-like environment equipped with a higher-order language similar to the one used by HOL Light. The main goal of ULKB Logic is to ease the construction of applications that combine state-of-the-art computational logic tools with the knowledge available in knowledge graphs, such as Wikidata. To this end, the framework provides APIs for fetching statements from SPARQL endpoints and operating over the constructed theories using automated theorem provers and SMT solvers (such as the E prover and Z3). In this paper, we describe the design and implementation of ULKB Logic, its interfaces for querying knowledge graphs and calling external provers, and plans for further development.