Reviewerly/exHarmony huggingface dataset:
Local initiative supports case isolation and contact tracing during a SARS-CoV-2 surge in summer 2020: a community case study in Antwerp, Belgium

In Antwerp, Belgium's second largest city, a COVID-19 surge in July 2020 predominantly affected neighborhoods with high ethnic diversity. Local volunteers reacted and set up an initiative to support contact tracing and self-isolation. We describe the origin, implementation, and transfer of this local initiative, based on semi-structured interviews of five key informants and document review. The initiative started in July 2020, when family physicians signaled a surge of SARS-CoV-2 infections among people of Moroccan descent. Family physicians feared that the mainstream contact tracing organized by the Flemish government through centralized call centers would not be efficient in halting this outbreak. They anticipated language barriers, mistrust, inability to investigate case clusters, and practical problems with self-isolation. It took 11 days to start up the initiative, with logistical support from the province and city of Antwerp. Family physicians referred SARS-CoV-2-infected index cases with complex needs (including language and social situation) to the initiative. Volunteer COVID coaches contacted cases, got a contextualized understanding of their living conditions, assisted with backward and forward contact tracing, offered support during self-isolation, and checked if infected contacts also needed support. Interviewed coaches were positive about the quality of the interaction: they described extensive open conversations with cases. The coaches reported back to referring family physicians and coordinators of the local initiative, who took additional action if necessary. Although interactions with affected communities were perceived as good, respondents considered that the number of referrals by family physicians was too low to have a meaningful impact on the outbreak. In September 2020, the Flemish government assigned the tasks of local contact tracing and case support to the local health system level (primary care zones). While doing so, they adopted elements of this local initiative, such as COVID coaches, tracing system, and extended questionnaires to talk with cases and contacts. This community case study illustrates how urgency can motivate people to action yet support from people with access to resources and coordination capacity is vital for effective organization and transition to long-term sustainability. From their conception, health policies should consider adaptability of new interventions to local contexts.
Overlapping group prior for image deconvolution using patch-wise gradient statistics

In image deconvolution problems, global gradient statistics serve as powerful image priors that separate high-quality images from degraded observations. However, it is unclear whether the important detailed information is sufficiently regularized in these approaches. Owing to the limitations, the restored images exhibit sharply reconstructed edges around structures, but commonly suffer from the loss of significant textures and details. In this study, we introduced patch-wise gradient statistics as an image prior strategy to restore disregarded regions in consideration of regional characteristics. The analytic relation between the prior and the restored local statistics was investigated through the Bayesian interpretation of overlapping group sparsity. The regularization function that incorporates the contextual information was derived in the maximum a posteriori framework. Subsequently, the unknown prior information and latent image were alternately estimated from the observations based on the patch-wise statistical analysis of the non-blind deconvolution process. In the experimental results, the superiority of the proposed algorithm was verified by more original textures in the restored images compared to the conventional approaches.
VulScan: A Web-Based Vulnerability Multi-Scanner for Web Application

Web application is becoming more powerful and widely used as more people are accessing it. However, web application attacks have become an ongoing problem as a result of one or more vulnerabilities that may be present. Most researchers have been able to propose solutions capable of handling specific attacks, which are not capable of identifying the vulnerabilities present in today's dynamic web applications. Hence, the proposed system named “Vulnerability Scanner (VulScan)” has the ability to identify six distinct categories of attacks that web applications may be susceptible to. The system uses an authenticated scanning approach to scan web application components such as links, forms, headers etc., and reports susceptible vulnerabilities. The detection capability of VulScan was evaluated on Damn Vulnerable Web Application (DVWA) and live web applications.
Comparison of Average Completion Time and Makespan for Various Task Scheduling Techniques

Cloud computing is becoming a trending and important technology that everyone is using globally on a large scale. It allows users to get access to resources (Storage and computational) online over the internet. With the increasing number of users, maintaining the Quality of service is challenging. The jobs are becoming more and more intensive and hence designing and implementing the correct task scheduling algorithm is very necessary to fulfill the increasing demand of users. An algorithm is needed to be designed to map the request of users to a particular Virtual Machine to utilize the resources of virtual machines efficiently. In this paper, we have observed from the results that better makespan and optimum or efficient resource usage are required for both customers and cloud service scheduling algorithms, which are considered suitable for large-scale systems. In this paper, the performance of 5 scheduling algorithms - FCFS, SJF scheduling, Round Robin scheduling, general priority algorithm, and Particle Swarm Optimization (PSO) is analyzed and compared. The performance of the various task scheduling algorithms has been carried out on the Cloudsim simulator. It has been observed from results that PSO outperforms the other compared task scheduling techniques and gives 48.3% less makespan as compared to FCFS.
Arabic Text Detection Using Rough Set Theory: Designing a Novel Approach

The linguistics related research and particularly, sentiment analysis using data-driven approaches, has been growing in recent years.However, the large number of users and excessive amount of information available on social media, make it difficult to detect extremism text on these platforms.The literature revealed a plethora of research studies focusing the sentiment analysis primarily, for English texts, however, very limited studies are available concerning the Arabic language which is the 4th mostly spoken language in the world.We first time in this study, propose a text detection mechanism for extremism orientations distinction in Arabic language, to improve the comprehension of subjective phrases.The study introduces a novel method based on Rough Set theory to enhance the accuracy of selected models and recognize text orientation reliably.Experimental outcomes indicate that the proposed method outperforms existing algorithms by contributing towards feature discriminations.Our method achieved 90.853%, 81.707% and 71.951% accuracies for unigram, bigram, and trigram representations, respectively.This study significantly contributes to the limited research in the field of machine learning and linguistics in Arabic language.
Confrontation and Obstacle-Avoidance of Unmanned Vehicles Based on Progressive Reinforcement Learning

The core technique of unmanned vehicle systems is the autonomous maneuvering decision, which not only determines the applications of unmanned vehicles but also is the critical technique many countries are competing to develop. Reinforcement Learning (RL) is the potential design method for autonomous maneuvering decision-making systems. Nevertheless, in the face of complex decision-making tasks, it is still challenging to master the optimal policy due to the low learning efficiency caused by the complex environment, high dimensional state, and sparse reward. Inspired by the human learning process from simple to complex, we propose a novel progressive deep RL algorithm for policy optimization in unmanned autonomous decision-making systems in this paper. The proposed algorithm divides the training of the autonomous maneuvering decision into a sequence of curricula with learning tasks from simple to complex. Finally, through the self-play stage, the iterative optimization of the policy is realized. Furthermore, the confrontation environment with two unmanned vehicles with obstacles is analyzed and modeled. Finally, the simulation leads to the one-to-one adversarial tasks demonstrate the effectiveness and applicability of the proposed design algorithm.
CryptoComparator: Supporting cryptocurrencies analysis through Visual Analytics

Cryptocurrencies are a novel phenomenon in the finance world that is gaining more attention from the general public, banks, investors, and academic research lately. A characteristic of cryptocurrencies is to be the target of investments that, due to the volatility of most of the cryptocurrencies, tends to be at high risk and behave very differently from traditional currencies. A way of reducing this risk is to look at the history of existing cryptocurrencies and compare them to spot promising trends for increased gain. This paper introduces CryptoComparator, a Visual Analytics tool designed to analyze the correlations and trends of cryptocurrencies. The system exploits an initial proposal for a double elliptic graph layout, reconfigurable with three different ordering functions, to support a fast visual search of cryptocurrencies by correlation strength. Two use cases developed with a domain expert in cryptocurrency financial activities demonstrate qualitatively the support it provides for analyzing cryptocurrencies.
Frequent temporal patterns of physiological and biological biomarkers and their evolution in sepsis

Sepsis is one of the most challenging health conditions worldwide, with relatively high incidence and mortality rates. It is shown that preventing sepsis is the key to avoid potentially irreversible organ dysfunction. However, data-driven early identification of sepsis is challenging as sepsis shares signs and symptoms with other health conditions. This paper adopts a temporal pattern mining approach to identify frequent temporal and evolving patterns of physiological and biological biomarkers in sepsis patients. We show that using these frequent patterns as features for classifying sepsis and non-sepsis patients can improve the prediction accuracy and performance up to 7%. Most of the temporal modeling approaches adopted in the sepsis literature are based on deep learning methods. Although these approaches produce high accuracy, they generally have limited model explainability and interpretability. Using the adopted methods in this study, we could identify the most important features contributing to the patients' sepsis incidence, such as fluctuations in platelet, lactate, and creatinine, or evolution of patterns including renal and metabolic organ systems, and consequently, enhance the findings' clinical interpretability.
Quantum Theory Approach to Performance Enhancement in Machine Learning

Quantum support vector machines (QSVMs) are a novel approach for solving classification problems, combining the principles of quantum computing with the support vector machine algorithm. In this study, we applied a QSVM to the Breast Cancer Wisconsin (Original) data set to classify tumors as benign or malignant. Our results showed that the QSVM outperformed classical support vector machines in terms of classification accuracy, achieving a success rate of 100% while classical SVM shows almost perfect result with its accuracy 95%. Additionally, the QSVM required significantly less training time compared to the classical SVM. These results demonstrate the potential of QSVMs for solving complex classification problems in the field of medical diagnosis.
Improving Conceptual Domain Characterization in Ontology Networks

The community of Conceptual Modeling (CM) perception that Semantic Interoperability cannot be achieved without the support of an ontology-driven approach has become increasingly consensual. Moreover, the more complex and extensive the domain of the application of conceptual models, the harder it is to achieve semantic consensus. Therefore, it has emerged the perception that ontologies built to describe complex domains should not be overly large or be used in isolation. Ontology Networks arose to cover this issue. The community had to deal with issues such as different ontologies of the network using the same concept with different meanings or the same term used to designate distinct concepts. We developed a framework for classifying ontologies that provides a stable and homogeneous environment to facilitate the ontological analysis process by dealing simultaneously with ontological and domain perspectives. This article presents our proposal where conceptualization is used to identify the relationships among the evaluated ontologies. Our goal is to facilitate semantic consensus, providing guidelines and best practices supported by a stable, homogeneous, and repeatable environment.
Internet of Cloud (IoC): The Need of Raising Privacy and Security Awareness

In our fast-paced society, due to the interconnection of millions of devices on the Internet, a great amount of complex data is shared, received, and managed by facilitating communication among users and devices globally. As a result, users from different educational backgrounds and professions are exposed to cyber threats and risks. Current megatrends such as Industry 4.0, Internet of Things (IoT), Cloud Computing, Metaverse, and 6G technology introduce a new era of user experience through strong connectivity, and costless service with high adaptability, however, it raises major data privacy and security concerns. These trends can ameliorate people's lives; however, they are characterized as "attractive vulnerable targets" for cyber-attacks with high intent to harm and disrupt people's daily lives. This paper focuses on two main pillars: the privacy and security fundamental challenges of the synergy between the Internet of Things (IoT) and the Cloud which creates the Internet of Cloud (IoC) and the need of raising awareness to prevent violation of user's data on the IoC. The scope of this paper is to indicate the importance of privacy and security concerns on IoC and to explain why privacy awareness training should be a priority.
Optimum LQR Controller for Inverted Pendulum Using Whale Optimization Algorithm

In this work, a Linear-Quadratic Regulator (LQR)-based control scheme is designed for a highly nonlinear and unstable inverted pendulum system. The system is linearised about its vertical position based on certain assumptions. Initially, weight matrices of the LQR controller are selected based on a trial and error method. These matrices are then optimised using a multi-objective genetic algorithm (GA) and whale optimization algorithm (WOA). The robustness of designed controllers is tested by reference tracking and parametric uncertainty analysis. The results reveal that optimisation of LQR by WOA provides superior performance compared to GA.
Joint Shared-and-Specific Information for Deep Multi-View Clustering

Multi-view data describes an image sample with different modalities of features, thus provides a more comprehensive description of data. Its three basic characteristics, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., consensus, complementary and redundancy, determine its performances in computer vision tasks. In this paper, we effectively exploit the above three characteristics to propose a deep learning scheme with joint shared-and-specific information (JSSI) for multi-view clustering. Aiming at facilitating the consensus, JSSI extracts shared information of multi-view data via an adversarial similarity constraint, which is realized by classification and discrimination interactions. Aiming at reducing the redundancy, JSSI separate out view-specific features and prevent them from interfering with the shared features via a difference constraint. Aiming at ensuring the complementary, JSSI aligns the shared features and then concatenates them with the specific features. We examine the effectiveness of JSSI with multi-view clustering on real-world datasets, such as faces and indoor scenes. Extensive experiments and comparisons show that JSSI outperforms other state-of-the-art methods in most of these datasets.
A Multi-Modal Hypergraph Neural Network via Parametric Filtering and Feature Sampling

In the real world, relationships between objects are often complex, involving multiple variables and modes. Hypergraph neural networks possess the capability to capture and represent such intricate relationships by deriving and inheriting their graph-based counterparts. Nevertheless, both graph and hypergraph neural networks suffer from the problem of over-smoothing when multiple graph convolution layers are stacked. To address this issue, this article introduces the Multi-modal Hypergraph Neural Network with Parametric Filtering and Feature Sampling (MHNet) to encode complex hypergraph features and mitigate over-smoothing. The proposed approach uses hypergraph structures to model high-order and multi-modal data correlations, a polynomial hypergraph filter to dynamically extract multi-scale node features through parametric polynomial fitting, and a feature sampling strategy to learn from sparse and labeled samples while avoiding overfitting. Experimental results on four hypergraph datasets and two multi-modal visual datasets demonstrate that the proposed MHNet outperforms state-of-the-art algorithms.
BMDENet: Bi-Directional Modality Difference Elimination Network for Few-Shot RGB-T Semantic Segmentation

Few-shot semantic segmentation (FSS) aims to segment the target prospects of query images using a few labeled support samples. Compared with the fully-supervised methods, FSS has a greater ability to generalize to unseen classes and reduce the pressure to label large pixel-level datasets. To cope with the complex outdoor lighting environment, we introduce the thermal infrared images (T) to the FSS task. However, the existing RGB-T FSS methods all ignore the differences between various modalities for direct fusion, which may hinder cross-modal information interaction. Also considering the effect of successive downsampling on the results, we propose a bi-directional modality difference elimination network (BMDENet) to boost the segmentation performance. Concretely, the bi-directional modality difference elimination module (BMDEM) reduces the heterogeneity between RGB and thermal images in the prototype space. The residual attention fusion module (RAFM) mines the bimodal features to fully fuse the cross-modal information. In addition, the mainstay and subsidiary enhancement module (MSEM) enhances the fusion features for the existing problem of the advanced model. Extensive experiments on Tokyo Multi-Spectral- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4^{\mathrm{ i}}$ </tex-math></inline-formula> dataset prove that BMDENet achieves the state-of-the-art on both 1- and 5-shot settings.
How data governance principles influence participation in biodiversity science

ABSTRACTABSTRACTBiodiversity science is in a pivotal period when diverse groups of actors – including researchers, businesses, national governments, and Indigenous Peoples – are negotiating wide-ranging norms for governing and managing biodiversity data in digital repositories. The management of these repositories, often called biodiversity data portals, can serve either to redress or to perpetuate the colonial history of biodiversity science and current inequities. Both researchers and Indigenous Peoples are implementing new strategies to influence whom biodiversity data portals recognise as salient participants in data management and use. Two notable efforts are the FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority, Responsibility, Ethics) Data Principles. Actors use these principles to influence the governance of biodiversity data portals. 'Fit-for-use' data is a social status provided by groups of actors who approve whether the data meets specific purposes. Advocates for the FAIR and CARE Principles use them in a similar way to institutionalise the authority of different groups of actors. However, the FAIR Principles prioritise the ability of machine agents to understand the meanings of data, while the CARE Principles prioritise Indigenous Peoples and their data sovereignty. Together, FAIR and CARE illustrate a broader emerging strategy for institutionalising international norms for digital repositories about who they should recognise as having a formal role in determinations of the fitness-for-use of data.KEYWORDS: FAIR PrinciplesCARE PrinciplesIndigenous datadata sovereigntycitizen scienceknowledge infrastructure Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by United States National Science Foundation: [grant no SES 2122818].Notes on contributorsBeckett SternerBeckett Sterner is an Assistant Professor in the School of Life Sciences at Arizona State University. His research investigates what knowledge is needed for people to work together while differing in fundamental ways, including in beliefs, aims, and values. He examines this driving question using knowledge infrastructures for global biodiversity loss as a case study, including data commons, modelling methods, and international governance. He has an interdisciplinary background in history, philosophy, and social studies of science as well as statistics and biology.Steve ElliottSteve Elliott is a Research Affiliate at Arizona State University's Centre for Biology and Society, for which he studies the economics, social organisation, and reasoning strategies of research. Some of his recent work examines science diaspora networks and proof-of-concept research.
Multi-Feature and Multi-Modal Mispronunciation Detection and Diagnosis Method Based on the Squeezeformer Encoder

In recent years, with the development of deep learning, research on end-to-end mispronunciation detection and diagnosis(MDD) methods has been further promoted. At present, research on end-to-end mispronunciation detection and diagnosis is gradually emerging. Most end-to-end mispronunciation detection and diagnosis methods are based on the CNN-RNN-CTC network structure. To improve the performance of end-to-end mispronunciation detection and diagnosis systems, this paper proposes an end-to-end multi-feature and multi-modal mispronunciation detection and diagnosis method based on the Squeezeformer encoder. The model uses Squeezeformer as an audio encoder, a Bi-LSTM network as a phoneme encoder, and Transformer as a decoder. The model fuses phoneme information before speech encoding and decoding, respectively, and uses a secondary decoding mechanism during the decoding process. This study further incorporated phoneme information in the encoding process so that the model could learn the intrinsic characteristics of the speaker’s pronunciation content. The decoding process uses a secondary decoding mechanism to send the sequence decoded by the model to the decoder for decoding again, which solves the problem of no a priori knowledge at the decoder end in the first decoding stage, thus improving the performance of mispronunciation detection and diagnosis. In this study, experiments were conducted on the PSC-Reading Mandarin mispronunciation detection and diagnosis dataset. Compared with the baseline model, the F1 index improved from 0.4060 to 0.7943, and the diagnostic accuracy improved from 83.93% to 88.45%.