Reviewerly/exHarmony huggingface dataset:
Democratic research: Setting up a research commons for a qualitative, comparative, longitudinal interview study during the COVID-19 pandemic

The sudden and dramatic advent of the COVID-19 pandemic led to urgent demands for timely, relevant, yet rigorous research. This paper discusses the origin, design, and execution of the SolPan research commons, a large-scale, international, comparative, qualitative research project that sought to respond to the need for knowledge among researchers and policymakers in times of crisis. The form of organization as a research commons is characterized by an underlying solidaristic attitude of its members and its intrinsic organizational features in which research data and knowledge in the study is shared and jointly owned. As such, the project is peer-governed, rooted in (idealist) social values of academia, and aims at providing tools and benefits for its members. In this paper, we discuss challenges and solutions for qualitative studies that seek to operate as research commons.
One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation

Semantic communication in the 6G era has been deemed a promising communication paradigm to break through the bottleneck of traditional communications. However, its applications for the multi-user scenario, especially the broadcasting case, remain under-explored. To effectively exploit the benefits enabled by semantic communication, in this paper, we propose a one-to-many semantic communication system. Specifically, we propose a deep neural network (DNN) enabled semantic communication system called MR\_DeepSC. By leveraging semantic features for different users, a semantic recognizer based on the pre-trained model, i.e., DistilBERT, is built to distinguish different users. Furthermore, the transfer learning is adopted to speed up the training of new receiver networks. Simulation results demonstrate that the proposed MR\_DeepSC can achieve the best performance in terms of BLEU score than the other benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) regime.
Arabic machine reading comprehension on the Holy Qur’an using CL-AraBERT

In this work, we tackle the problem of machine reading comprehension (MRC) on the Holy Qur’an to address the lack of Arabic datasets and systems for this important task. We construct QRCD as the first Qur’anic Reading Comprehension Dataset, composed of 1,337 question-passage-answer triplets for 1,093 question-passage pairs, of which 14% are multi-answer questions. We then introduce CLassical-AraBERT (CL-AraBERT for short), a new AraBERT-based pre-trained model, which is further pre-trained on about 1.0B-word Classical Arabic (CA) dataset, to complement the Modern Standard Arabic (MSA) resources used in pre-training the initial model, and make it a better fit for the task. Finally, we leverage cross-lingual transfer learning from MSA to CA, and fine-tune CL-AraBERT as a reader using two MSA-based MRC datasets followed by our QRCD dataset to constitute the first (to the best of our knowledge) MRC system on the Holy Qur’an. To evaluate our system, we introduce Partial Average Precision (pAP) as an adapted version of the traditional rank-based Average Precision measure, which integrates partial matching in the evaluation over multi-answer and single-answer MSA questions. Adopting two experimental evaluation setups (hold-out and cross validation (CV)), we empirically show that the fine-tuned CL-AraBERT reader model significantly outperforms the baseline fine-tuned AraBERT reader model by 6.12 and 3.75 points in pAP scores, in the hold-out and CV setups, respectively. To promote further research on this task and other related tasks on Qur’an and Classical Arabic text, we make both the QRCD dataset and the pre-trained CL-AraBERT model publicly available.
Efficient Hybrid Blind Watermarking in DWT-DCT-SVD with Dual Biometric Features for Images

In the modern era of virtual computers over the notional environment of computer networks, the protection of influential documents is a major concern. To bring out this motto, digital watermarking with biometric features plays a crucial part. It utilizes advanced technology of cuffing data into digital media, i.e., text, image, video, or audio files. The strategy of cuffing an image inside another image by applying biometric features namely signature and fingerprint using watermarking techniques is the key purpose of this study. To accomplish this, a combined watermarking strategy consisting of Discrete Wavelet Transform, Discrete Cosine Transform, and Singular Value Decomposition (DWT-DCT-SVD) is projected for authentication of image that is foolproof against attacks. Here, singular values of watermark1 (fingerprint) and watermark2 (signature) are obtained by applying DWT-DCT-SVD. Affixing both the singular values of watermarks, we acquire the transformed watermark. Later, the same is applied to cover image to extract the singular values. Then we add these values to the cover image and transformed watermark to obtain a final watermarked image containing both signature and fingerprint. To upgrade the reliability, sturdiness, and originality of the image, a fusion of watermarking techniques along with dual biometric features is exhibited. The experimental results conveyed that the proposed scheme achieved an average PSNR value of about 40 dB, an average SSIM value of 0.99, and an embedded watermark resilient to various attacks in the watermarked image.
A New Density Peak Clustering Algorithm Based on Cluster Fusion Strategy

When the density peak clustering algorithm deals with complex datasets and the problem of multiple density peaks in the same cluster, the subjectively selected cluster centers are not accurate enough, and the allocation of non-cluster centers is prone to joint and several errors. To solve the above problems, we propose a new density peak clustering algorithm based on cluster fusion strategy. First, the algorithm screens out the candidate cluster centers by setting two new thresholds to avoid the influence of noise points and outliers. Second, the remaining data points are allocated according to the density peak clustering algorithm to obtain the initial clusters. Third, considering the structural characteristics and spatial distribution of datasets, the new definitions of boundary points, inter-cluster intersection density and inter-cluster boundary density are provided. To correctly classify the clustering problems with multiple density peaks in the same cluster, a new cluster fusion strategy is proposed, which not only corrects the joint and several errors in the allocation of data points, but also correctly selects the cluster centers. Finally, to test the effectiveness of the proposed clustering algorithm, which is compared with DPC-KNN, DPC, K-means and DBSCAN on nine synthetic datasets and six real datasets. The experimental results demonstrate that the clustering performance of the proposed algorithm outperforms that of other algorithms.
A Time-Saving Path Planning Scheme for Autonomous Underwater Vehicles With Complex Underwater Conditions

Autonomous underwater vehicle (AUV) shows great potential in the Internet of Underwater Things (IoUT) system, in which the path planning algorithm plays a fundamental role. However, the complex underwater environment brings greater challenges to AUV path planning, especially the ocean current, which has a profound impact on time and energy consumption. This article focuses on the complex ocean current condition and proposes an underwater path planning method based on proximal policy optimization (UP4O). In this novel method, a deep reinforcement network is constructed to serve as a decision control to plan the moving direction of AUV. An information encoding module is developed to extract the features of the local obstacles. Furthermore, UP4O integrates the obstacle features with the current state information, including relative position, ocean current, and velocity, enabling the AUV to focus on the global direction and local obstacles at the same time. Additionally, to further adapt to the ocean current and shorten the time cost, UP4O expands the action space of AUV, realizing a fine and flexible action adjustment. The wide applicability of UP4O has been proved by numerous experiments. The proposed algorithm can always plan the time-saving and collision-free paths in complex underwater environments with various terrains and ocean current.
Discrete asymmetric zero-shot hashing with application to cross-modal retrieval

In recent years, cross-modal retrieval technology has attracted extensive attention with the massive growth of multimedia data. However, most cross-modal hashing methods mainly focus on exploring the retrieval of seen classes, while ignoring the retrieval of unseen classes. Therefore, traditional cross-modal hashing methods cannot achieve satisfactory performances in zero-shot retrieval. To mitigate this challenge, in this paper, we propose a novel zero-shot cross-modal retrieval method called discrete asymmetric zero-shot hashing(DAZSH), which fully exploits the supervised knowledge of multimodal data. Specifically, it integrates pairwise similarity, class attributes and semantic labels to guide zero-shot hashing learning. Moreover, our proposed DAZSH method combines the data features with the class attributes to obtain a semantic category representation for each category. Therefore, the relationships between seen and unseen classes can be effectively captured by learning a category representation vector for each instance. Therefore, the supervised knowledge can be transferred from the seen classes to the unseen classes. In addition, we develop an efficient discrete optimization strategy to solve the proposed model. Massive experiments on three benchmark datasets show that our proposed approach has achieved promising results in cross-modal retrieval tasks. The source code of this paper can be obtained from https://github.com/szq0816/DAZSH.
Opposition-based learning multi-verse optimizer with disruption operator for optimization problems

Multi-verse optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step is using opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.
A Systematic Review of Human–Computer Interaction (HCI) Research in Medical and Other Engineering Fields

This article provides a systematic review of research related to Human–Computer Interaction techniques supporting training and learning in various domains including medicine, healthcare, and engineering. The focus is on HCI techniques involving Extended Reality (XR) technology which encompasses Virtual Reality, Augmented Reality, and Mixed Reality. HCI-based research is assuming more importance with the rapid adoption of XR tools and techniques in various training and learning contexts including education. There are many challenges in the adoption of HCI approaches, which results in a need to have a comprehensive and systematic review of such HCI methods in various domains. This article addresses this need by providing a systematic literature review of a cross-section of HCI approaches involving proposed so far. The PRISMA-guided search strategy identified 1156 articles for abstract review. Irrelevant abstracts were discarded. The whole body of each article was reviewed for the remaining articles, and those that were not linked to the scope of our specific issue were also eliminated. Following the application of inclusion/exclusion criteria, 69 publications were chosen for review. This article has been divided into the following sections: Introduction; Research methodology; Literature review; Threats of validity; Future research and Conclusion. Detailed classifications (pertaining to HCI criteria and concepts, such as affordance; training, and learning techniques) have also been included based on different parameters based on the analysis of research techniques adopted by various investigators. The article concludes with a discussion of the key challenges for this HCI area along with future research directions. A review of the research outcomes from these publications underscores the potential for greater success when such HCI-based approaches are adopted during such 3D-based training interactions. Such a higher degree of success may be due to the emphasis on the design of user-friendly (and user-centric) training environments, interactions, and processes that positively impact the cognitive abilities of users and their respective learning/training experiences. We discovered data validating XR-HCI as an ascending method that brings a new paradigm by enhancing skills and safety while reducing costs and learning time through replies to three exploratory study questions. We believe that the findings of this study will aid academics in developing new research avenues that will assist XR-HCI applications to mature and become more widely adopted.
A deep and systematic review of the intrusion detection systems in the fog environment

Abstract Fog computing has arisen to complement cloud computing, offering a cost‐effective architecture to power the Internet of things. Fog computing is a network computing and service paradigm that expands cloud computing and services to the network's edge to solve the cloud's fundamental shortcomings, like a lack of location awareness, mobility, and latency. Besides, fog devices may have security issues because fog nodes are near end‐users and have limited computational power. As the linked devices in the Internet of things grow exponentially, so does the potential for cyber‐attack. Intrusion detection systems have been developed as a feasible tool for detecting and preventing harmful activity in this vein. This work used a systematic literature review to examine intrusion detection system approaches in a fog environment. By categorizing intrusion detection systems in the literature, we produce a review. An intrusion detection system's characteristic is early identification of an attack and the decrease of false alarms as a result. Some of the issues that these systems' designers will face are also discussed. Recall rate, detection rates, detection delay, and detection precision were the parameters that received the most attention. This report will serve as a roadmap for future network intrusion detection studies in fog computing.
Motivation readiness of future software engineer’s professional self-improvement and prospects of its formation in college cloud environment

Innovative technologies have an impact on the countries socioeconomic development, the structure of labor market and educational services transformation. Rapid IT industry development constantly requires qualified programmers capable of professional self-improvement throughout life, the driving force of which is the individual motivation which activates the individual self-development process, optimizes thinking and develops special professional qualities, moral and ethical values. The main article purpose is to analyze the state of the form of motivational readiness for future programmer’s professional self-improvement, to identify problems of its formation in colleges and to determine the ways of its increase as one of the main factors of quality improvement. To achieve it, a complex of theoretical and empirical methods was used, with help of which a number of problems were revealed which slow down the process of improving the quality of future programmers professional training. To eliminate them, a system of phased motivation for future specialists professional self-improvement has been developed on the basis of general secondary education, which can be integrated into the teaching of both general education and professionally-oriented disciplines; ways of improving the quality of the educational process through the creation of a cloud of oriented environment, the introduction of innovative teaching technologies, special training of teachers in the system of professional development.
TBraTS: Trusted Brain Tumor Segmentation

Despite recent improvements in the accuracy of brain tumor segmentation, the results still exhibit low levels of confidence and robustness. Uncertainty estimation is one effective way to change this situation, as it provides a measure of confidence in the segmentation results. In this paper, we propose a trusted brain tumor segmentation network which can generate robust segmentation results and reliable uncertainty estimations without excessive computational burden and modification of the backbone network. In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution. Meanwhile, the trusted segmentation framework learns the function that gathers reliable evidence from the feature leading to the final segmentation results. Overall, our unified trusted segmentation framework endows the model with reliability and robustness to out-of-distribution samples. To evaluate the effectiveness of our model in robustness and reliability, qualitative and quantitative experiments are conducted on the BraTS 2019 dataset.
Estimating the global technical potential of building-integrated solar energy production using a high-resolution geospatial model

The building sector is responsible for about one third of the global final energy consumption and CO2 emission, thus it is desired to limit and replace building-related fossil energy sources to meet climate goals. In this context, the utilization of building integrated solar technology has proven to be a reliable and increasingly affordable alternative, however, there is still an immense potential remained unexplored. This study thus uses, a high-resolution, geospatial energy supply model to estimate the useable building rooftop areas across 11 regions of the World, and calculates the corresponding global and regional potential of energy production of state-of-the-art rooftop PV/T collectors over a 39-year period. Our results demonstrate that solar PV/T energy production on residential and commercial/public rooftops has enormous global potential (47.5 PWh), with the possibility of doubling by 2060. The current magnitude of potential implies that about 60% of the suitable building rooftops could be installed with PV/T collectors to offset most of the local energy demand. Regarding the future trends we found that beyond the extended building stock in large economies (e.g., China, USA and EU), the newly-built commercial buildings of developing regions (e.g., Latin America and South Asia) are modeled to have key role in realizing the estimated potential over the next decades. Our study also focuses on the geographical, temporal and building-level characteristics of energy production and concludes that rooftops in the Middle East, South and Pacific Asia have the most favorable geographical exposure for capturing solar (dominantly thermal) energy by PV/T collectors. It was found to be especially valid for months during the warm season. In regions dominated by temperate climate, the energy generation is characterized by a second maximum before the warm season, due to the peak of electricity production. At the time of the production peaks and in general annually, irrespective to regions, PV/T collectors installed on single-family roofs and retails were estimated to have the greatest potential to supply green energy for the entire building and thus likely to balance the in-situ energy consumption.
Solving multimodal optimization problems using adaptive differential evolution with archive

Evolutionary algorithms are widely used to solve multimodal optimization problems. The two main challenges faced while solving MMOPs are locating multiple optimal solutions and improving the accuracy of these solutions. In this paper, we have proposed an adaptive algorithm based on differential evolution using the distributed framework in mutation strategy and an elite archive mechanism termed Adaptive Differential Evolution with Archive to deal with these challenges. The following techniques have been proposed and integrated to locate multiple diverse optimal solutions with refined accuracy. Firstly, each individual in the population is treated as a possible exemplar and is expected to reach an optimal value by exploring the nearby search space. The search space is controlled by using an adjustable range mechanism. An adaptive mutation strategy is then used to ensure that all the good solutions or individuals of the population move to better positions. Next, an elite archive is constructed for stagnated individuals to avoid getting stuck in local optimas. The experimental results on the 20 multimodal functions from IEEE Congress on Evolutionary Computation 2013 illustrate that the performance of the proposed algorithm is better than the existing multimodal optimization algorithms in terms of finding more number of accurate solutions.
Micro-expression spotting based on optical flow features

Expression is the changes of facial organs due to facial muscle movement and an important way of human emotional interaction. Neurophysiological studies show that micro-expressions (MEs) are not controlled by subjective consciousness and reflect people's real emotions. That is why MEs have significant value in public security applications. This paper proposes an automatic ME spotting method of high accuracy and interpretability. Firstly, we design the nose tip location-based image alignment method to remove global displacement caused by head shaking. Secondly, according to the action unit definition in the face coding system (FACS), we select fourteen regions of interest (ROI) to capture subtle facial movements. The dense optical flow is introduced to estimate local movements and time-domain variations of the ROIs. Thirdly, we design a peak detection method on the time-domain variation curves to locate the movement intervals precisely. Lastly, we propose an overlapping index to measure the consistency of changes in different organs. Evaluation on the CAS(ME)2 and SAMM Long Video database shows that our ME spotting method may achieve better accuracy with a relatively lower computation cost and can be applied to similar facial image processing applications.