Reviewerly/exHarmony huggingface dataset:
Design and Validation of a Novel Tool to Assess Citizens’ Netiquette and Information and Data Literacy Using Interactive Simulations

Until recently, most of the digital literacy frameworks have been based on assessment frameworks used by commercial entities. The release of the DigComp framework has allowed the development of tailored implementations for the evaluation of digital competence. However, the majority of these digital literacy frameworks are based on self-assessments, measuring only low-order cognitive skills. This paper reports on a study to develop and validate an assessment instrument, including interactive simulations to assess citizens’ digital competence. These formats are particularly important for the evaluation of complex cognitive constructs such as digital competence. Additionally, we selected two different approaches for designing the tests based on their scope, at the competence or competence area level. Their overall and dimensional validity and reliability were analysed. We summarise the issues addressed in each phase and key points to consider in new implementations. For both approaches, items present satisfactory difficulty and discrimination indicators. Validity was ensured through expert validation, and the Rasch analysis revealed good EAP/PV reliabilities. Therefore, the tests have sound psychometric properties that make them reliable and valid instruments for measuring digital competence. This paper contributes to an increasing number of tools designed to evaluate digital competence and highlights the necessity of measuring higher-order cognitive skills.
Features of Distance Learning in Higher Education Institutions in The Context of The Covid-19 Pandemic

The purpose of the research is to study the quality of distance learning. The paper identifies the main problems that arose during the transition to distance learning due to the epidemiological situation in 2020-2021 in the Russian Federation. Materials and methods. To obtain data, a survey was conducted among students of the Crimean Federal University named after V.I. Vernadsky. Because of testing the quality of distance learning, an array of 187 records was obtained. The data were processed in the IBM SPSS Statistics 23.0 program using descriptive statistics methods. The following methods were used in the work: analysis of methodological, pedagogical, psychological, scientific, technical and methodological literature on the research problem; private methods - classification, systematization, comparison, analysis and generalization of pedagogical experience, modeling of the content of education. 60.9% of women and 39.1% of men took part in the survey, among them the majority of full-time students (96.2%), less – extramural studies (2.7%) and composite study mode (1.1%). Among the levels of higher education: undergraduate students – 86.8%, graduate students – 10.4% and postgraduate students – 2.7%. Results. The study showed that half of the students of higher educational institutions have adapted to the new conditions of distance learning. Half of them have decreased learning motivation, only a quarter of the respondents are completely satisfied with the quality of the learning process, half note an increase in the workload. When evaluating the quality of software, students noted inconveniences in using the Moodle platform: problems of poor presentation of lectures, technical interruptions in the process of playing back the material, and the absence of valid hyperlinks. More than half of the students believe that they are provided with the necessary amount of material for independent study. When analyzing the degree of social activity, students noted a decrease in communication with classmates and the need for “live” communication with lecturers. More than half of the students note an increase in the level of general anxiety. In the choice of works that lecturers used more often in their classes, students note issuing of tasks for independent performance, placement of educational respondents, half faced difficulties in solving practical tasks without the help of a lecturer and did not always cope with a large amount of given information. Most of the problems in the initial stages of implementation were quickly resolved. Conclusion. Based on the study, it was found that the number of advantages of distance learning prevails over the number of disadvantages. The potential for using training in this format is at a high level, thus providing prospects for its use. However, the study also showed that further work is needed to improve feedback between lecturers and students in the face of declining non-verbal communication. The study proposes ways to eliminate the main difficulties faced by both students and lecturers during distance learning
Segmentation of Human Brain Gliomas Tumour Images using U-Net Architecture with Transfer Learning

The complexity of segmenting a brain tumour is critical in medical image processing. Treatment options and patient survival rates can only be improved if brain tumours can be prevented and treated. Segmentation of the brain is the most complex and time-consuming task to diagnose cancer utilizing a manual approach for numerous magnetic resonance images (MRI). The aim of MRI brain tumour image segmentation that to build an automated magnetic resonance imaging tumour segmentation system with separate the area of tumour and provided a clear boundary of the tumour region. U-Nets with different transfer learning models as backbones are presented in this paper, there are ResNet50, DenseNet169 and EfficientNet-B7. Brain lesion segmentation is performed using the multimodal brain tumor segmentation challenge 2020 dataset (BraTS2020). Based on MRI scans of the brain, the tumor segmentation technique is assessed using F1-score, Dice loss, and intersection over union score (IoU). The U-Net encoder used with EfficientNet-B7 outperforms all other architectures in terms of performance metrics across the board. Overall, the results of this experiment are rather excellent. The Dice-loss score was 0.009435, and the score of IoU was 0.7435, F1-score was 0.9848, accuracy was 0.9924, precision was 0.9829, recall was 0.9868, and specificity was 0.9943. The U-Net with EfficientNet-B7 architecture was shown to be crucial in the treatment of brain tumors, according to the findings of the experiments
Zenbo on Zoom: Evaluating the Human-Robot Interaction User Experience in a Video Conferencing Session

The COVID-19 pandemic has restricted the ability of HRI researchers to undertake face-to-face HRI user studies while obeying existing social and physical distancing mandates. In this pilot study we evaluated the quality of the user experience reported by undergraduate CS/IT students after they had two online interactions with a social robot using the Zoom video conferencing system. Our results showed that there was nothing inherently detrimental to performing HRI user studies online. Indeed, based on these preliminary results, researchers who are conducting HRI user studies online can have more confidence that the online interaction modality does not negatively affect their results.
A face detection method via ensemble of four versions of YOLOs

We implemented a real-time ensemble model for face detection by combining the results of YOLO v1 to v4. We used the WIDER FACE benchmark for training YOLOv1 to v4 in the Darknet framework. Then, we ensemble their results by two methods, namely, WBF (Weighted boxes fusion) and NMW (Non-maximum weighted). The experimental analysis showed that the mAP increases in the WBF ensemble of the models for all the easy, medium, and hard images in the datasets by 7.81%, 22.91%, and 12.96%, respectively. These numbers are 6.25%, 20.83%, and 11.11% for the NMW ensemble.
Rating of Modern Color Image Cryptography: A Next-Generation Computing Perspective

Issues such as inefficient encryption architectures, nonstandard formats of image datasets, weak randomness of chaos-based Pseudorandom Number Generators (PRNGs), omitted S-boxes, and unconvincing security metrics leading to increased computational time and inadequate security level of chaos and Deoxyribonucleic Acid- (DNA-) based image encryption schemes need careful examination towards the development of more stable encryption schemes in terms of efficiency and reasonable security. A new taxonomy of image encryption based on chaotic systems, hyperchaotic systems, and DNA is propounded to assess the impact of these issues on the performance and security metrics. The primary emphasis of this research is to study various recent encryption architectures centered on a variety of confusion and diffusion methods. It is aimed at assessing the performance and security of various ciphers using a cipher rating criterion that categorizes ciphers into different classes. The parameters that are included in the rating criteria are information entropy, chi-squared goodness of fit test for histogram uniformity analysis, encryption efficiency, key space, differential attacks (Number of Pixels Change Rate and Universal Average Changing Intensity), key sensitivity analysis, encryption time, randomness tests such as NIST-R (a statistical suite for validating the randomness designed by the National Institute of Standards and Technology), correlation coefficient analysis, contrast analysis, energy analysis, homogeneity analysis, Mean Absolute Error, peak signal-to-noise ratio, and robustness to noise and occlusion attacks.
Few-Shot Learning for Radar Emitter Signal Recognition Based on Improved Prototypical Network

In recent years, deep learning has been widely used in radar emitter signal identification and has significantly increased recognition rates. However, with the emergence of new institutional radars and an increasingly complex electromagnetic environment, the collection of high-quality signals becomes difficult, leading to a result that the amount of some signal types we own are too few to converge a deep neural network. Moreover, in radar emitter signal identification, most existing networks ignore the signal recognition of unknown classes, which is of vital importance for radar emitter signal identification. To solve these two problems, an improved prototypical network (IPN) belonging to metric-based meta-learning is proposed. Firstly, a reparameterization VGG (RepVGG) net is used to replace the original structure that severely limits the model performance. Secondly, we added a feature adjustment operation to prevent some extreme or unimportant samples from affecting the prototypes. Thirdly, open-set recognition is realized by setting a threshold in the metric module.
Visual Object Tracking for Unmanned Aerial Vehicles Based on the Template-Driven Siamese Network

Visual object tracking for unmanned aerial vehicles (UAV) is widely used in many fields such as military reconnaissance, search and rescue work, film shooting, and so on. However, the performance of existing methods is still not very satisfactory due to some complex factors including viewpoint changing, background clutters and occlusion. The Siamese trackers, which offer a convenient way of formulating the visual tracking problem as a template matching process, have achieved success in recent visual tracking datasets. Unfortunately, these template match-based trackers cannot adapt well to frequent appearance change in UAV video datasets. To deal with this problem, this paper proposes a template-driven Siamese network (TDSiam), which consists of feature extraction subnetwork, feature fusion subnetwork and bounding box estimation subnetwork. Especially, a template library branch is proposed for the feature extraction subnetwork to adapt to the changeable appearance of the target. In addition, a feature aligned (FA) module is proposed as the core of feature fusion subnetwork, which can fuse information in the form of center alignment. More importantly, a method for occlusion detection is proposed to reduce the noise caused by occlusion. Experiments were conducted on two challenging benchmarks UAV123 and UAV20L, the results verified the more competitive performance of our proposed method compared to the existing algorithms.
Numão gold deposit in the Iberian Variscan belt, Northern Portugal: Ore features and mineralization controls. A gold deposit in a W-Sn metallogenic province

The Numão gold deposit in the northern part of the Iberian Variscan belt is close to the regional first-order Vilariça-Manteigas fault system. The second and third-order structures controlled the gold mineralized bodies. These bodies consist of disseminated sulfides, arsenopyrite-quartz veins, and sulfides-scheelite-quartz veins. The scheelite occurrence predates the gold mineral assemblage and is lithologically controlled by host rocks with calc-silicate minerals. The ore mineral assemblage comprises scheelite, arsenopyrite, chalcopyrite, bismuth, and Au-Ag mineral phases, with minor pyrite, pyrrhotite, sphalerite, and galena. The hypogene gold minerals at Numão deposit are hosted by arsenopyrite, chalcopyrite, and silicate matrix in minor amounts. Chemically, gold fineness ranges from 663 to 841. The thermal evolution of the mineralization was estimated based on arsenopyrite (381° to 502 °C) and chlorite geothermometers (264° to 341 °C). The multi-element and principal component analysis information was used to better identify geochemical patterns and trends in hydrothermal alteration. The gold-metal association (Au-As-W-Bi) was found to be similar to other gold deposits in the European Variscan belt.
Effects of variability in models: a family of experiments

Abstract The ever-growing need for customization creates a need to maintain software systems in many different variants. To avoid having to maintain different copies of the same model, developers of modeling languages and tools have recently started to provide implementation techniques for such variant-rich systems, notably variability mechanisms , which support implementing the differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of which have dedicated benefits and drawbacks. Currently, language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling. In this article, we present an empirical assessment of annotative and compositional variability mechanisms for three popular types of models. We report and discuss findings from a family of three experiments with 164 participants in total, in which we studied the impact of different variability mechanisms during model comprehension tasks. We experimented with three model types commonly found in modeling languages: class diagrams, state machine diagrams, and activity diagrams. We find that, in two out of three experiments, annotative technique lead to better developer performance. Use of the compositional mechanism correlated with impaired performance. For all three considered tasks, the annotative mechanism was preferred over the compositional one in all experiments. We present actionable recommendations concerning support of flexible, tasks-specific solutions, and the transfer of established best practices from the code domain to models.
HELP-DKT: an interpretable cognitive model of how students learn programming based on deep knowledge tracing

Student cognitive models are playing an essential role in intelligent online tutoring for programming courses. These models capture students' learning interactions and store them in the form of a set of binary responses, thereby failing to utilize rich educational information in the learning process. Moreover, the recent development of these models has been focused on improving the prediction performance and tended to adopt deep neural networks in building the end-to-end prediction frameworks. Although this approach can provide an improved prediction performance, it may also cause difficulties in interpreting the student's learning status, which is crucial for providing personalized educational feedback. To address this problem, this paper provides an interpretable cognitive model named HELP-DKT, which can infer how students learn programming based on deep knowledge tracing. HELP-DKT has two major advantages. First, it implements a feature-rich input layer, where the raw codes of students are encoded to vector representations, and the error classifications as concept indicators are incorporated. Second, it can infer meaningful estimation of student abilities while reliably predicting future performance. The experiments confirm that HELP-DKT can achieve good prediction performance and present reasonable interpretability of student skills improvement. In practice, HELP-DKT can personalize the learning experience of novice learners.
An 8.3-to-18Gbps Reconfigurable SCA-Resistant/Dual-Core/Blind-Bulk AES Engine in Intel 4 CMOS

Power and electromagnetic (EM) side-channel attacks (SCA) exploit data-dependent power consumption from cryptographic engines to extract embedded secret keys. While series-connected voltage regulators [1], [2] and arithmetic countermeasures like heterogenous Galois-field arithmetic [3] provide acceptable levels of side-channel leakage suppression, they cannot defend against determined adversaries. Random additive masking [4] on the other hand, provides a provably-secure solution [5] that disrupts first-order correlations between measured power/EM signatures and secret keys, while incurring <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\times$</tex> overhead in area and power consumption. In this paper, we demonstrate a reconfigurable AES accelerator fabricated in Intel 4 CMOS process with minimum-time-to-disclosure (MTD) <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt; 1\text{B power}/\text{EM}$</tex> traces in on-demand SCA-resistant mode, while providing a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.2\times$</tex> boost in encryption performance during a dual-core mode of operation (Fig. 34.4.1). When coupled with side-channel attack detection techniques [6], [7], this approach allows the user to operate at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt; 2\times$</tex> AES throughput during the safe mode of operation in trusted environments, with the ability to quickly trade-off throughput for a higher level of SCA-resistance when the onset of an attack is detected. In the blind-bulk mode of operation, the accelerator randomly switches at a user-specified rate between SCA-resistant and dual-core modes while encrypting bulk data, providing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.14-\text{to}-1.6\times$</tex> boost in encryption throughput with measured MTD <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt; 50\mathrm{M}$</tex> traces.
Use of Virtual Site Visits in Construction Management Distance Education

While distance and online education had some initial momentum with many universities fully or partially embracing distance education prior to 2019, the COVID-19 pandemic forced the switch to distance education as a precautionary move. Site visits and hands-on experiences can provide valuable experiences to students during their education; however, in a distance education system, conducting a physical site visit and providing a hands-on experience is not an easy task. This paper reports on the use of virtual site visits and experiencing virtual hands-on training using immersive videos. This method has been adopted and tested in two courses that were previously face to face, which were then shifted to an online format due to the COVID-19 pandemic. The use of immersive videos gave students who would not have the ability to experience a physical site visit the opportunity to experience the construction site environment, and receive educational direction during a pre-recorded, hands-on, immersive video project. In the following, students' provided feedback on their experience with this technology and its comparison to using other available tools in distance education is presented, and the implications for future research and the potential implementation of this technology is discussed.
A Survey on Multi-document Summarization and Domain-Oriented Approaches

Before the advent of the World Wide Web, lack of information was a problem. But with the advent of the web today, we are faced with an explosive amount of information in every area of search. This extra information is troublesome and prevents a quick and correct decision. This is the problem of information overload. Multi-document summarization is an important solution for this problem by producing a brief summary containing the most important information from a set of documents in a short time. This summary should preserve the main concepts of the documents. When the input documents are related to a specific domain, for example, medicine or law, summarization faces more challenges. Domain-oriented summarization methods use special characteristics related to that domain to generate summaries. This paper introduces the purpose of multi-document summarization systems and discusses domain-oriented approaches. Various methods have been proposed by researchers for multi-document summarization. This survey reviews the categorizations that authors have made on multi-document summarization methods. We also categorize the multi-document summarization methods into six categories: machine learning, clustering, graph, Latent Dirichlet Allocation (LDA), optimization, and deep learning. We review the different methods presented in each of these groups. We also compare the advantages and disadvantages of these groups. We have discussed the standard datasets used in this field, evaluation measures, challenges and recommendations.
Investigating the uses of mobile phone evidence in China criminal proceedings

Data from mobile phones are regularly used in the investigation of crime and court proceedings. Previously published research has primarily addressed technical issues or provided operational manuals for using forensic science evidence, rather than analysing human factors and the implementation of forensic tools in investigation settings. Moreover, previous research has focused almost entirely on western countries, and there is a dearth of research into the uses of forensic evidence in China. In this study, a review was carried out of court sentencing documents referring to mobile phone evidence in China over the period 2013-2018. Automated content analysis was used to identify the specific evidence types utilised and the sentencing outcome for each case. Results show that mobile phone evidence was used in 3.3% of criminal proceedings. Among various data types mentioned in criminal proceedings, call records sustained as the most frequently used type of data. After which, instant messaging tools (e.g. WeChat) are an increasing proportion of all mobile phone evidence, from 1% in 2015 to 25% in 2018. For cases that utilised mobile phone data, the analysis of instant messaging and online transaction tools is routine, with little variation in the use of each application (WeChat, Alipay, QQ) for investigations of different types of crime. However, in the majority of criminal cases, mobile phone data function as subsidiary evidence and posed limited impacts on verdict reached. The current findings indicate that a large amount of mobile phone evidence was transformed into other evidence formats or filtered out directly before court proceedings.