Reviewerly/exHarmony huggingface dataset:
Data Property, Data Governance and Common European Data Spaces

The Data Act proposal of February 2022 constitutes a central element of a broader and ambitious initiative of the European Commission (EC) to regulate the data economy through the erection of a new general regulatory framework for data and digital markets. The resulting framework may be represented as a model of governance between a pure market-driven model and a fully regulated approach, thereby combining elements that traditionally belong to private law (e.g., property rights, contracts) and public law (e.g., regulatory authorities, limitation of contractual freedom). This article discusses the role of (intellectual) property rights as well as of other forms of rights allocation in data legislation with particular attention to the Data Act proposal. We argue that the proposed Data Act has the potential to play a key role in the way in which data, especially privately held data, may be accessed, used, and shared. Nevertheless, it is only by looking at the whole body of data (and data related) legislation that the broader plan for a data economy can be grasped in its entirety. Additionally, the Data Act proposal may also arguably reveal the elements for a transition from a property-based to a governance-based paradigm in the EU data strategy. Whereas elements of data governance abound, the stickiness of property rights and rhetoric seem however hard to overcome. The resulting regulatory framework, at least for now, is therefore an interesting but not always perfectly coordinated mix of both. Finally, this article suggests that the Data Act Proposal may have missed the chance to properly address the issue of data holdersâ€™ power and related information asymmetries, as well as the need for coordination mechanisms.
Comparison of the validity, perceived usefulness, and usability of I-MeDeSA and TEMAS, two tools to evaluate alert system usability

Two tools are currently available in the literature to evaluate the usability of medication alert systems, the instrument for evaluating human factors principles in medication-related decision support alerts (I-MeDeSA) and the tool for evaluating medication alerting systems (TEMAS). This study aimed to compare their convergent validity, perceived usability, usefulness, strengths, and weaknesses, as well as users' preferences. To evaluate convergent validity, two experts mapped TEMAS' items against I-MeDeSA's items with respect to the usability dimensions they target. To assess perceived usability, usefulness, strengths, and weaknesses of both tools, staff with expertise in their medication alerting system were asked to use French versions of the TEMAS and I-MeDeSA. After the use of each tool, participants were asked to complete the System Usability Scale (SUS) and answer questions about the understandability and usefulness of each tool. Finally, participants were asked to name their preferred tool. Numeric scores were statistically compared. Free-text responses were analyzed using an inductive approach. Forty-five participants from 10 hospitals took part in the study. In terms of convergent validity, I-MeDeSA focuses more on the usability of the graphical user interface while TEMAS considers a wider range of usability principles. Both tools have a fair level of perceived usability (I-MeDeSA' SUS score = 61.85 and TEMAS' SUS score = 62.87), but results highlight that revisions are necessary to both tools to improve their usability. Participants found TEMAS more useful than I-MeDeSA (t = -3.63, p =.005) and had a clear preference for TEMAS to identify problems in formative evaluation (39 of 45; 0.867, p <.001) and to compare the usability of alert systems during the procurement process (36 of 45; 0.8, p <.001). The TEMAS is perceived as more useful and is preferred by participants. The I-MeDeSA seems more relevant for quick evaluations that focus on the graphical user interface. The TEMAS seems to be more suitable for in-depth usability evaluations of alert systems. Even if both tools are perceived to be equally usable, they suffer from wording, instructional, and organizational problems that hinder their use. The results of this study will be used to improve the design of I-MeDeSA and TEMAS.
Event-Aware Video Deraining via Multi-Patch Progressive Learning

In this paper, we address the problem of video-based rain streak removal by developing an event-aware multi-patch progressive neural network. Rain streaks in video exhibit correlations in both temporal and spatial dimensions. Existing methods have difficulties in modeling the characteristics. Based on the observation, we propose to develop a module encoding events from neuromorphic cameras to facilitate deraining. Events are captured asynchronously at pixel-level only when intensity changes by a margin exceeding a certain threshold. Due to this property, events contain considerable information about moving objects including rain streaks passing though the camera across adjacent frames. Thus we suggest that utilizing it properly facilitates deraining performance non-trivially. In addition, we develop a multi-patch progressive neural network. The multi-patch manner enables various receptive fields by partitioning patches and the progressive learning in different patch levels makes the model emphasize each patch level to a different extent. Extensive experiments show that our method guided by events outperforms the state-of-the-art methods by a large margin in synthetic and real-world datasets.
On computing probabilistic abductive explanations

The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches based on saliency maps. One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness. Unfortunately, intrinsic interpretability can display unwieldy explanation redundancy. Formal explainability represents the alternative to these non-rigorous approaches, with one example being PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. Recently, it has been observed that the (absolute) rigor of PI-explanations can be traded off for a smaller explanation size, by computing the so-called relevant sets. Given some positive δ, a set S of features is δ-relevant if, when the features in S are fixed, the probability of getting the target class exceeds δ. However, even for very simple classifiers, the complexity of computing relevant sets of features is prohibitive, with the decision problem being NPPP-complete for circuit-based classifiers. In contrast with earlier negative results, this paper investigates practical approaches for computing relevant sets for a number of widely used classifiers that include Decision Trees (DTs), Naive Bayes Classifiers (NBCs), and several families of classifiers obtained from propositional languages. Moreover, the paper shows that, in practice, and for these families of classifiers, relevant sets are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained for the families of classifiers considered.
CPR Emergency Assistance Through Mixed Reality Communication

We design and evaluate a mixed reality real-time communication system for remote assistance during CPR emergencies. Our system allows an expert to guide a first responder, remotely, on how to give first aid. RGBD cameras capture a volumetric view of the local scene including the patient, the first responder, and the environment. The volumetric capture is augmented onto the remote expert’s view to spatially guide the first responder using visual and verbal instructions. We evaluate the mixed reality communication system in a research study in which participants face a simulated emergency. The first responder moves the patient to the recovery position and performs chest compressions as well as mouth-to-mask ventilation. Our study compares mixed reality against videoconferencing-based assistance using CPR performance measures, cognitive workload surveys, and semi-structured interviews. We find that more visual communication including gestures and objects is used by the remote expert when assisting in mixed reality compared to videoconferencing. Moreover, the performance and the workload for the first responder during simulation does not differ significantly between the two technologies.
Feature-aware conditional GAN for category text generation

Category text generation receives considerable attentions since it is beneficial for various natural language processing tasks. Recently, the generative adversarial network (GAN) has attained promising performance in text generation, attributed to its adversarial training process. However, there are several issues in text GANs, including discreteness, training instability, mode collapse, lack of diversity and controllability etc. To address these issues, this paper proposes a novel GAN framework, the feature-aware conditional GAN (FA-GAN), for controllable category text generation. In FA-GAN, the generator has a sequence-to-sequence structure for improving sentence diversity, which consists of three encoders including a special feature-aware encoder and a category-aware encoder, and one relational-memory-core-based decoder with the Gumbel SoftMax activation function. The discriminator has an additional category classification head. To generate sentences with specified categories, the multi-class classification loss is supplemented in the adversarial training. Comprehensive experiments have been conducted, and the results show that FA-GAN consistently outperforms 10 state-of-the-art text generation approaches on 6 text classification datasets. The case study demonstrates that the synthetic sentences generated by FA-GAN can match the required categories and are aware of the features of conditioned sentences, with good readability, fluency, and text authenticity.
Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements

Purpose This study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the Lexicon-LSTM model. Design/methodology/approach Eight research themes were selected for experiment, with a large-scale ( N = 11,625) dataset of research papers from the China National Knowledge Infrastructure (CNKI) database constructed. And it is complemented with multiple corpora. Knowledge elements were extracted through a Lexicon-LSTM model. A subject knowledge graph is constructed to support the searching and classification of knowledge elements. An interdisciplinary-weighted average citation index space was constructed for measuring the interdisciplinary characteristics and contributions based on knowledge elements. Findings The empirical research shows that the Lexicon-LSTM model has superiority in the accuracy of extracting knowledge elements. In the field of LIS, the interdisciplinary diversity indicator showed an upward trend from 2011 to 2021, while the disciplinary balance and difference indicators showed a downward trend. The knowledge elements of theory and methodology could be used to detect and measure the interdisciplinary characteristics and contributions. Originality/value The extraction of knowledge elements facilitates the discovery of semantic information embedded in academic papers. The knowledge elements were proved feasible for measuring the interdisciplinary characteristics and exploring the changes in the time sequence, which helps for overview the state of the arts and future development trend of the interdisciplinary of research theme in LIS.
Multimodal Hate Speech Detection from Bengali Memes and Texts

Numerous machine learning (ML) and deep learning (DL)-based approaches have been proposed to utilize textual data from social media for anti-social behavior analysis like cyberbullying, fake news detection, and identification of hate speech mainly for highly-resourced languages such as English. However, despite of having a lot of diversity and millions of native speakers, some languages like Bengali are under-resourced, which is due to lack of computational resources for natural language processing (NLP). Similar to other languages, Bengali social media contents also include images along with texts (e.g., multimodal memes are posted by embedding short texts into images on Facebook). Therefore, only the textual data is not enough to judge them since images might give extra context to make a proper judgement. This paper is about hate speech detection from multimodal Bengali memes and texts. We prepared the only multimodal hate speech dataset for-a-kind of problem for Bengali, which we use to train state-of-the-art neural architectures (e.g., Bi-LSTM/Conv-LSTM with word embeddings, ConvNets + pre-trained language models, e.g., monolingual Bangla BERT, multilingual BERT-cased/uncased, and XLM-RoBERTa) to jointly analyze textual and visual information for hate speech detection. Conv-LSTM and XLM-RoBERTa models performed best for texts, yielding F1 scores of 0.78 and 0.82, respectively. As of memes, ResNet-152 and DenseNet-161 models yield F1 scores of 0.78 and 0.79, respectively. As of multimodal fusion, XLM-RoBERTa + DenseNet-161 performed the best, yielding an F1 score of 0.83. Our study suggest that text modality is most useful for hate speech detection, while memes are moderately useful.
Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer

Continued advances in precision medicine rely on the widespread sharing of data that relate human genetic variation to disease. However, data sharing is severely limited by legal, regulatory, and ethical restrictions that safeguard patient privacy. Federated analysis addresses this problem by transferring the code to the data—providing the technical and legal capability to analyze the data within their secure home environment rather than transferring the data to another institution for analysis. This allows researchers to gain new insights from data that cannot be moved, while respecting patient privacy and the data stewards’ legal obligations. Because federated analysis is a technical solution to the legal challenges inherent in data sharing, the technology and policy implications must be evaluated together. Here, we summarize the technical approaches to federated analysis and provide a legal analysis of their policy implications.
Assessing Economic Complementarity in Wind–Solar Hybrid Power Plants Connected to the Brazilian Grid

The share of electricity generation from Variable Renewable Energy Sources (VRES) has increased over the last 20 years. Despite promoting the decarbonization of the energy mix, these sources bring negative characteristics to the energy mix, such as power ramps, load mismatch, unpredictability, and fluctuation. One of the ways to mitigate these characteristics is the hybridization of power plants. This paper evaluates the benefits of hybridizing a plant using an AI-based methodology for optimizing the wind–solar ratio based on the Brazilian regulatory system. For this study, the hybrid plant was modeled using data collected over a period of 10 months. The measurements were obtained using two wind profilers (LIDAR and SODAR) and a sun tracker (Solys 2) as part of the EOSOLAR R&amp;D project conducted in the state of Maranhão, Brazil. After the power plant modeling, a Genetic Algorithm (GA) was used to determine the optimal wind–solar ratio, considering costs with transmission systems. The algorithm achieved a monthly profit increase of more than 39% with an energy curtailment inferior to 1%, which indicates economic complementarity. Later, the same methodology was also applied to verify the wind–solar ratio’s sensitivity to solar energy pricing. The results show that a price increase of 15% would change the power plant’s optimal configuration.
Blockchain Integration for IoT-Enabled V2X Communications: A Comprehensive Survey, Security Issues and Challenges

In V2X (vehicle-to-everything) communication, there is a two-way communication among the vehicle(s) and other Internet of Things (IoT)-enabled smart devices around it that may change how we need to drive. Due to the advancement of Information and Communications Technology (ICT) and the rapid development of IoT in transportation, traditional applications are converted to intelligent applications. In V2X communications, the collected information from the IoT smart devices and other sources passes through low-latency, high-bandwidth, high-reliability links. With the future adoption of the 5th generation mobile network (5G) and beyond networks, V2X continues to produce a huge volume of data. However, collecting and storing data securely in blockchain-based storage are extremely needed for immutability and transparency. In this survey article, the convergence of IoT, V2X and blockchain technologies, and various security challenges and their countermeasures are discussed. Next, we discuss various V2X applications and their respective services. Moreover, IoT-V2X architecture and its enabling technologies are discussed in this article. In addition, we also provide a comprehensive analysis of various security mechanisms. Finally, we provide some important challenges and issues of Blockchain for Intelligent Transportation System (BITS).
Blockchain model for environment/infrastructure monitoring in cloud-enabled high-altitude platform systems

The recently accentuated features of augmenting conventional wireless networks with high altitude platform systems (HAPS) have fueled a plethora of applications, which promise to offer new services to ground users, as well to enhance the efficiency and pervasion of existing applications. Cloud-enabled HAPS, which aims to create HAPS-based datacenters that offer cloud services to users, has particularly emerged as a promising key enabler to provide large-scale equitable services from the sky. Although offering cloud services from the HAPS proves to be efficient, its practical deployment at the stratosphere level still faces many challenges such as high energy requirements, physical maintenance, and is particularly prone to security considerations. Safeguarding the cloud-enabled HAPS against various cyberattacks is a necessity to guarantee its safe operation. This paper proposes a blockchain model to secure cloud-enabled HAPS networks that contain a large number of HAPS stations from recurring cyberattacks within the context of the environment and infrastructure monitoring (EIM) application. To this end, the paper first presents a detailed blockchain framework, and describes the ways of integrating the developed framework into the various system components. We then discuss the details of the system implementation, including the storing and consuming of cloud transactions, the generation of new blocks, and the blockchain consensus protocol that is tailored to the EIM requirements. Finally, we present numerical simulations that illustrate the performance of the system in terms of throughput, latency, and resilience to attacks.
Q2ATransformer: Improving Medical VQA via an Answer Querying Decoder

Medical Visual Question Answering (VQA) systems play a supporting role to understand clinic-relevant information carried by medical images. The questions to a medical image include two categories: close-end (such as Yes/No question) and open-end. To obtain answers, the majority of the existing medical VQA methods rely on classification approaches, while a few works attempt to use generation approaches or a mixture of the two to process the two kinds of questions separately (classification for the close-end and generation for the open-end). The classification approaches are relatively simple but perform poorly on long open-end questions, while the generation approaches face the challenge of generating many non-existent answers, resulting in low accuracy rates. To bridge this gap, in this paper, we propose a new Transformer based framework for medical VQA (named as Q2ATransformer), which integrates the advantages of both the classification and the generation approaches and provides a unified treatment for the close-end and open-end questions. Specifically, we introduce an additional Transformer decoder with a set of learnable candidate answer embeddings to query the existence of each answer class to a given image-question pair. Through the Transformer attention, the candidate answer embeddings interact with the fused features of the image-question pair to make the decision. In this way, despite being a classification-based approach, our method provides a mechanism to interact with the answer information for prediction like the generation-based approaches. On the other hand, by classification, we mitigate the task difficulty by reducing the search space of answers. Our method achieves new state-of-the-art performance on two medical VQA benchmarks. Especially, for the open-end questions, we achieve 79.19% on VQA-RAD and 54.85% on PathVQA, with 16.09% and 41.45% absolute improvements, respectively.
A novel hybrid generative adversarial network for CT and MRI super-resolution reconstruction

Abstract Objective . Computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in medical imaging modalities, and provide valuable information for clinical diagnosis and treatment. However, due to hardware limitations and radiation safety concerns, the acquired images are often limited in resolution. Super-resolution reconstruction (SR) techniques have been developed to enhance the resolution of CT and MRI slices, which can potentially improve diagnostic accuracy. To capture more useful feature information and reconstruct higher quality super-resolution images, we proposed a novel hybrid framework SR model based on generative adversarial networks. Approach . The proposed SR model combines frequency domain and perceptual loss functions, which can work in both frequency domain and image domain (spatial domain). The proposed SR model consists of 4 parts: (i) the discrete Fourier transform (DFT) operation transforms the image from the image domain to frequency domain; (ii) a complex residual U-net performs SR in the frequency domain; (iii) the inverse discrete Fourier transform (iDFT) operation based on data fusion transforms the image from the frequency domain to image domain; (iv) an enhanced residual U-net network is used for SR of image domain. Main results . Experimental results on bladder MRI slices, abdomen CT slices, and brain MRI slices show that the proposed SR model outperforms state-of-the-art SR methods in terms of visual quality and objective quality metric such as the structural similarity (SSIM) and the peak signal-to-noise ratio (PSNR), which proves that the proposed model has better generalization and robustness. (Bladder dataset: upscaling factor of 2: SSIM = 0.913, PSNR = 31.203; upscaling factor of 4: SSIM = 0.821, PSNR = 28.604. Abdomen dataset: upscaling factor of 2: SSIM = 0.929, PSNR = 32.594; upscaling factor of 4: SSIM = 0.834, PSNR = 27.050. Brain dataset: SSIM = 0.861, PSNR = 26.945). Significance . Our proposed SR model is capable of SR for CT and MRI slices. The SR results provide a reliable and effective foundation for clinical diagnosis and treatment.