Reviewerly/exHarmony huggingface dataset:
Experts’ View on AR/VR in Engineering Education at Universities

Innovative teaching methods focus more on student engagement and start to become more common in the university landscape supported by digital technologies. These technologies include Augmented Reality (AR), Mixed Reality (MR) and Virtual Reality (VR). The research of this paper focuses on the use of AR/MR in education, with particular attention to engineering education at the university level. Therefore, a structured literature review is carried out to form the basis for conducting interviews with international professors from universities that are already using Extended Reality (XR) in their classroom settings. The research method used to gather data are qualitative interviews with selected experts from Europe and US followed by a structured qualitative content analysis with focus on reasons why respective XR experiences are used in class, benefits that have been identified after the experts evaluated their use cases and finally which challenges had to be overcome.
Cryptanalyzing and Improving an Image Encryption Algorithm Based on Chaotic Dual Scrambling of Pixel Position and Bit

An image encryption algorithm for the double scrambling of the pixel position and bit was cryptanalyzed. In the original image encryption algorithm, the positions of pixels were shuffled totally with the chaotic sequence. Then, the 0 and 1-bit positions of image pixels were scrambled through the use of another chaotic sequence generated by the input key. The authors claimed that the algorithm was able to resist the chosen-plaintext attack. However, through the analysis of the encryption algorithm, it was found that the equivalent key of the whole encryption algorithm was the scrambling sequence T in the global scrambling stage, the pixel bit level scrambling sequence WT and the diffusion sequence S. The generation of scrambling sequence T is related to the sum of all pixel values of the plaintext image, while the generation of WT and S is not associated with the image to be encrypted. By using a chosen-plaintext attack, these equivalent key streams can be cracked so as to realize the decoding of the original chaotic encryption algorithm. Both theoretical analysis and experimental results verify the feasibility of the chosen-plaintext attack strategy. Finally, an improved algorithm was proposed to overcome the defect, which can resist the chosen-plaintext attack and has the encryption effect of a “one time pad”.
A Motion-Aware Siamese Framework for Unmanned Aerial Vehicle Tracking

In recent years, visual tracking has been employed in all walks of life. The Siamese trackers formulate the tracking problem as a template-matching process, and most of them can meet the real-time requirements, making them more suitable for UAV tracking. Because existing trackers can only use the first frame of a video sequence as a reference, the appearance of the tracked target will change when an occlusion, fast motion, or similar target appears, resulting in tracking drift. It is difficult to recover the tracking process once the drift phenomenon occurs. Therefore, we propose a motion-aware Siamese framework to assist Siamese trackers in detecting tracking drift over time. The base tracker first outputs the original tracking results, after which the drift detection module determines whether or not tracking drift occurs. Finally, the corresponding tracking recovery strategies are implemented. More stable and reliable tracking results can be obtained using the Kalman filter’s short-term prediction ability and more effective tracking recovery strategies to avoid tracking drift. We use the Siamese region proposal network (SiamRPN), a typical representative of an anchor-based algorithm, and Siamese classification and regression (SiamCAR), a typical representative of an anchor-free algorithm, as the base trackers to test the effectiveness of the proposed method. Experiments were carried out on three public datasets: UAV123, UAV20L, and UAVDT. The modified trackers (MaSiamRPN and MaSiamCAR) both outperformed the base tracker.
The Deployment Model Abstraction Framework

For the deployment of applications, various deployment technologies, such as Kubernetes and Terraform, are available to automate the deployment of applications. However, to use these technologies, developers must acquire specialized knowledge about these deployment technologies to create, maintain, and understand deployment models, for example, configuration files created with Kubernetes. In this work, we present and demonstrate the Deployment Model Abstraction Framework (DeMAF), a tool that enables transforming technology-specific deployment models into technology-agnostic deployment models that are modeled based on the Essential Deployment Metamodel (EDMM). The resulting technology-agnostic EDMM deployment models express deployments only by using the general modeling concepts that are supported by the 13 most prominent technologies. Therefore, the target audience for this demonstration includes developers and architects, who will be shown that such transformations can be automated and that the resulting EDMM models can be understood without knowledge of the original deployment technology. We evaluate the general practical feasibility of the approach by a case study that demonstrates a scenario based on the T2-Project and the technologies Terraform, Kubernetes, and Helm.
An Analysis of Levenshtein Distance Using Dynamic Programming Method

An edit distance (or Levenshtein distance) amongst dual verses refers to the slightest amount of replacements, additions and omissions of signs essential to turn one name addicted to the additional is referred to as the edit distance (or Levenshtein distance) amongst dual verses. The challenge of calculating the edit distance of a consistent verbal, that is the set of verses recognised by a fixed mechanism, is addressed in this research. The Levenshtein distance is a straightforward metric for calculating the distance amongst dual words using a string approximation. After witnessing its efficiency, this approach was refined by combining certain comparable letters and minimising the biased modification between associates of the similar set. The findings displayed a considerable enhancement over the old Levenshtein distance method.
Breast Cancer Prediction Using Machine Learning Classifiers

Breast cancer is one cancer that is becoming more prevalent every day. It's becoming worse due to a lack of detection. Lowering the death rate may be possible with quick detection. Based on the Wisconsin Breast Cancer dataset, this study suggests a machine learning-based strategy for identifying breast cancer. There were five distinct machine learning algorithms tested. Logistic Regression has given 94.73% accuracy, Decision Tree has 92.98% accuracy, Random Forest has 98.24% accuracy, and Support Vector Machine (SVM) has 96.49% accuracy. Random Forest has given the highest accuracy which is 98.24 %.
The limit joint distributions of statistics of four tests of the NIST package

Abstract For sequences of independent random variables having a Bernoulli distribution with parameter p the limit joint distribution of statistics of four tests of the NIST statistical package (« Monobit Test », « Frequency Test within a Block », « Runs Test » and a generalization of « Non-overlapping Template Matching Test ») is obtained. Conditions of asymptotic uncorrelatedness and/or asymptotic independence of these statistics are given.
The current landscape of author guidelines in chemistry through the lens of research data sharing

Abstract As the primary method of communicating research results, journals garner an enormous impact on community behavior. Publishing the underlying research data alongside journal articles is widely considered good scientific practice. Ideally, journals and their publishers place these recommendations or requirements in their author guidelines and data policies. Several efforts are working to improve the infrastructure, processes, and uptake of research data sharing, including the NFDI4Chem consortium, working groups within the RDA, and IUPAC, including the WorldFAIR Chemistry project. In this article, we present the results of a large-scale analysis of author guidelines from several publishers and journals active in chemistry research, showing how well the publishing landscape supports different criteria and where there is room for improvement. While the requirement for deposition of X-ray diffraction data is commonplace, guidelines rarely mention machine-readable chemical structures and metadata/minimum information standards. Further evaluation criteria included recommendations on persistent identifiers, data availability statements, data deposition into repositories as well as of open analytical data formats. Our survey shows that publishers and journals are starting to include aspects of research data in their guidelines. We as authors should accept and embrace the guidelines with increasing requirements for data availability, data interoperability, and re-usability to improve chemistry research.
Named entity recognition for Chinese based on global pointer and adversarial training

Named entity recognition aims to identify entities from unstructured text and is an important subtask for natural language processing and building knowledge graphs. Most of the existing entity recognition methods use conditional random fields as label decoders or use pointer networks for entity recognition. However, when the number of tags is large, the computational cost of method based on conditional random fields is high and the problem of nested entities cannot be solved. The pointer network uses two modules to identify the first and the last of the entities separately, and a single module can only focus on the information of the first or the last of the entities, but cannot pay attention to the global information of the entities. In addition, the neural network model has the problem of local instability. To solve mentioned problems, a named entity recognition model based on global pointer and adversarial training is proposed. To obtain global entity information, global pointer is used to decode entity information, and rotary relative position information is considered in the model designing to improve the model's perception of position; to solve the model's local instability problem, adversarial training is used to improve the robustness and generalization of the model. The experimental results show that the F1 score of the model are improved on several public datasets of OntoNotes5, MSRA, Resume, and Weibo compared with the existing mainstream models.
Deep evidential learning in diffusion convolutional recurrent neural network

&lt;abstract&gt;&lt;p&gt;Graph neural networks (GNNs) is applied successfully in many graph tasks, but there still exists a limitation that many of GNNs model do not consider uncertainty quantification of its output predictions. For uncertainty quantification, there are mainly two types of methods which are frequentist and Bayesian. But both methods need to sampling to gradually approximate the real distribution, in contrast, evidential deep learning formulates learning as an evidence acquisition process, which could get uncertainty quantification by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution without sampling. So evidential deep learning (EDL) has its own advantage in measuring uncertainty. We apply it with diffusion convolutional recurrent neural network (DCRNN), and do the experiment in spatiotemporal forecasting task in a real-world traffic dataset. And we choose mean interval scores (MIS), a good metric for uncertainty quantification. We summarized the advantages of each method.&lt;/p&gt;&lt;/abstract&gt;
WEB 2.0 POTENTIAL TO SUPPORT SOFT SKILLS DEVELOPMENT

The emergence of Web 2.0 has had an obvious impact at both individual and social level. The ubiquity of technology has enabled new ways of communication and collaboration that have also impacted the education. The frequent use of technological devices by students allow them to quickly adapt, by taking over with ease these new technologies. In the attempt to simplify learning, students sometimes use various Web 2.0 tools, expecting to encounter similar classroom experiences. Although the importance of possessing soft skills is recognized in successfully facing the future challenges, some studies highlight the existence of common soft skill gaps. The most common approaches to using Web 2.0 as a tool in developing soft skills include using Web 2.0 in a formal learning context. This study aims to identifying the potential of Web 2.0 tools to develop soft skills, when used by students "in a personal way". Therefore, this study shows the results of the research on the impact of using Web 2.0 tools in the development of soft skills. The research was carried out by applying a questionnaire on a study group of 43 students of technical faculties. The results of the conducted study confirmed the hypothesis according to which the soft skills were improved when Web.2.0 tools were used "in a personal way". Among the most developed skills include: communication, problem solving and self-regulated learning skills. The results obtained may represent some useful guidelines in identifying possible solutions to increase the potential of Web 2.0 in developing these skills, as supporting learning in formal contexts.
EDUCATIONAL COMPUTER GAMES IN PROGRAMMING TEACHING AND LEARNING

Computer games are at present very popular for all ages of users, computers and other digital devices that allow them to play. Creation and implementation of didactic computer games and the desire to create their own games is enough motivation for both high school, college and university students learn programming. The article describes the problem and project teaching of programming at the college and university, where the theme of the project is the implementation of didactic computer game of their choice. The project can be realized as an individual or group project. Programming environment or programming language to implement the computer game is freely selectable. Students can work in any higher programming language with a rich graphics library, or they can use a programming environment that supports computer games creation. Depends on the skill of the programmers/developers/team members how to share the work. Can be individually addressed by the individual functional parts of the game, or work can be divided into separate layers. Each team member is engaged with him in what is good, for what who has better conditions to be successful and achieve a good result. E.g. one focuses on graphic design of game, next to the algorithms that express check compliance with the rules of the game, next member of team developing winning strategies, other looks at how the game stopped and resumed, how increases the difficulty level of the game as power play value and how the achieved good performance. Solving the project students learn to work in a team and practically use the knowledge from the field of software engineering.
Complementarity and drivers of thermal and physical soil organic carbon fractions at the scale of mainland France

Assessing soil organic carbon biogeochemical stability is critical for estimating future changes in soil carbon stocks. Several methods for the assessment of soil organic carbon (SOC) biogeochemical stability have been proposed but very few can be implemented on large sample sets. Indeed, to date, only simple physical fractionation protocols (e.g. Lavallee et al., 2020) and Rock-Eval&amp;#174; thermal analysis techniques (Delahaie et al., 2022, SOIL discussion) have been implemented on data sets larger than a few hundred samples. Simple fractionation techniques allow separating a particulate organic carbon fraction (POC; considered labile) and an organic fraction associated with minerals (MaOC; considered more stable). Regarding thermal analyses, Rock-Eval&amp;#174; results associated to the PARTYsoc machine-learning model (C&amp;#233;cillon et al., 2021) provide a measure of the active (mean residence time of ca. 30 years) and centennially stable SOC fractions.In this study, we present the results of physical fractionations performed on ca. 1000 samples and thermal analyses performed on ca. 2000 samples from French mainland topsoils (RMQS program). We compare the amount and the drivers of each fraction. Our results show that most of the MaOC fraction is not stable at a centennial timescale. However, we show using a Random Forest model that the MaOC content and the centennially stable SOC content are similarly influenced by a common set of drivers: clay, pH and climatic conditions (mean annual temperature and mean annual precipitation). Finally, we discuss the complementarity of these two types of relatively high-throughput fractionation protocols.&amp;#160;ReferencesC&amp;#233;cillon, L., Baudin, F., Chenu, C., Christensen, B. T., Franko, U., Houot, S., Kanari, E., K&amp;#228;tterer, T., Merbach, I., van Oort, F., Poeplau, C., Quezada, J. C., Savignac, F., Souc&amp;#233;marianadin, L. N., &amp; Barr&amp;#233;, P. (2021). Partitioning soil organic carbon into its centennially stable and active fractions with machine-learning models based on Rock-Eval&amp;#174; thermal analysis (PARTY SOC v2. 0 and PARTY SOC v2. 0 EU).&amp;#160;Geoscientific Model Development,&amp;#160;14(6), 3879-3898. Delahaie, A. A., Barr&amp;#233;, P., Baudin, F., Arrouays, D., Bispo, A., Boulonne, L., Chenu, C., Jolivet, C., Martin, M. P., Rati&amp;#233;, C., Saby, N. P. A., Savignac, F., &amp; C&amp;#233;cillon, L. (2022). Elemental stoichiometry and Rock-Eval&amp;#174; thermal stability of organic matter in French topsoils.&amp;#160;EGUsphere, 1-31. Lavallee, J. M., Soong, J. L., &amp; Cotrufo, M. F. (2020). Conceptualizing soil organic matter into particulate and mineral&amp;#8208;associated forms to address global change in the 21st century.&amp;#160;Global Change Biology,&amp;#160;26(1), 261-273.
Semi-supervised Textual Entailment on Indonesian Wikipedia Data

Recognizing Textual Entailment (RTE) is a research in Natural Language Processing that aims to identify whether there is an entailment relation between two texts. Textual Entailment has been studied in a variety of languages, but it is rare for the Indonesian language. The purpose of the work presented in this paper is to conduct the RTE experiment on Indonesian language dataset. Since manual data creation is costly and time consuming, we choose semi-supervised machine learning approach. We apply co-training algorithm to enlarge small amounts of annotated data, called seeds. With this method, the human effort only needed to annotate the seeds. The data resource used is all from Wikipedia and the entailment pairs are extracted from its revision history. Evaluation of 1,857 sentence pairs labelled with entailment information using our approach achieve accuracy 76%.
Dissimilar collective decay and directional emission from two quantum emitters

We study a system of two distant quantum emitters coupled via a one-dimensional waveguide where the electromagnetic field has a direction-dependent velocity. As a consequence, the onset of collective emission is nonsimultaneous, and, for appropriate parameters, radiation could be enhanced for one of the emitters while inhibited for the other. Interference effects enable the system to radiate in a preferential direction depending on the atomic state and the field propagation phases. We characterize such directional emission as a function of various parameters, delineating the conditions for optimal directionality.
Enhanced dark-state sideband cooling in trapped atoms via photon-mediated dipole-dipole interactions

Resolved sideband cooling provides a crucial step in subrecoil cooling the trapped atoms toward their motional ground state, which is essential in atom-based quantum technologies. Here we present an enhanced dark-state sideband cooling in trapped atoms utilizing photon-mediated dipole-dipole interactions among them. By placing the atoms at the magic interparticle distances, we manifest an outperformed cooling behavior in the target atom, which surpasses the limit that a single atom permits. We further investigate various atomic configurations in a multiatom setup with a laser detuning and different light polarization angles, where multiple magic spacings can be identified and a moderate improvement in cooling performance is predicted as the number of atoms increases. Our results provide insights to subrecoil cooling of atoms with collective and light-induced long-range dipole-dipole interactions, and pave the way toward implementing genuine quantum operations in multiple quantum registers.
Artificial Intelligence Functionalities During the COVID-19 Pandemic

Abstract Background: The coronavirus disease 2019 (COVID-19) pandemic has led us to use virtual solutions and emerging technologies such as artificial intelligence (AI). Recent studies have clearly demonstrated the role of AI in health care and medical practice; however, a comprehensive review can identify potential yet not fulfilled functionalities of such technologies in pandemics. Therefore, this scoping review study aims at assessing AI functionalities in the COVID-19 pandemic in 2022. Methods: A systematic search was carried out in PubMed, Cochran Library, Scopus, Science Direct, ProQuest, and Web of Science from 2019 to May 9, 2022. Researchers selected the articles according to the search keywords. Finally, the articles mentioning the functionalities of AI in the COVID-19 pandemic were evaluated. Two investigators performed this process. Results: Initial search resulted in 9123 articles. After reviewing the title, abstract, and full text of these articles, and applying the inclusion and exclusion criteria, 4 articles were selectd for the final analysis. All 4 were cross-sectional studies. Two studies (50%) were performed in the United States, 1 (25%) in Israel, and 1 (25%) in Saudi Arabia. They covered the functionalities of AI in the prediction, detection, and diagnosis of COVID-19. Conclusions: To the extent of the researchers’ knowledge, this study is the first scoping review that assesses the AI functionalities in the COVID-19 pandemic. Health-care organizations need decision support technologies and evidence-based apparatuses that can perceive, think, and reason not dissimilar to human beings. Potential functionalities of such technologies can be used to predict mortality, detect, screen, and trace current and former patients, analyze health data, prioritize high-risk patients, and better allocate hospital resources in pandemics, and generally in health-care settings.