Reviewerly/exHarmony huggingface dataset:
Abstract Machine of the Programming Language for Educational Purposes SYNCHRO

The article is devoted to a number of solutions for organizing work with memory in educational languages and programming systems aimed at teaching the preparation of multi-threaded programs over shared memory. The consideration was carried out within the framework of the SYNCHRO programming language for educational purposes, which made it possible to analyze variants of such solutions without the limitations typical for traditional manufacturing tools and devices of standard programming systems. The article gives a clear formulation of an abstract machine and a diagram of a command system that allows you to define the behavior of a program as a distributed system from a number of threads interacting in terms of access to the values of variables located in shared memory. The de-vice of shared memory and mechanisms of access to it by individual processes, which are a sequence of command execution, some of which are requests to shared memory, are described. The focus is on the convenience of debugging small programs used to introduce concurrency problems in the learning process, where the pace of learners’ under-standing of the problems is more important than achieving program efficiency and performance. The solution to this problem is useful when studying programming methods, as well as studying the history of programming languages, comparing programming paradigms, the potential of the schemes and models used, assessing the level of novelty of the programming languages being created, creating a technique for measuring different characteristics of programs on models and choosing criteria for the practicality of the created programs. When choosing a system of commands for an abstract machine, the principles of functional programming were taken into account as a popular paradigm at the stage of preparing prototypes and models of multi-threaded programs. From these principles, the conclusion is drawn of the consequences that allow you to successfully select elementary instructions that support working with memory in the style of data immutability and their transactional processing. For educational tasks in programming, the ability to ensure the correctness and completeness of solutions is more important than the efficiency and productivity of the received programs. This is the path to building reliable and secure software.
A Scheme for Electronic Evidence Sharing Based on Blockchain and Proxy Re-encryption

research-article Share on A Scheme for Electronic Evidence Sharing Based on Blockchain and Proxy Re-encryption Authors: Zhikun Miao School of Computing, Qinghai Normal University, China School of Computing, Qinghai Normal University, ChinaView Profile , Chengxu Ye School of Computing, Qinghai Normal University, China School of Computing, Qinghai Normal University, ChinaView Profile , Ping Yang School of Geographical Sciences, Qinghai Normal University, China School of Geographical Sciences, Qinghai Normal University, ChinaView Profile , Rui Liu School of Computing, Qinghai Normal University, China School of Computing, Qinghai Normal University, ChinaView Profile , Bing Liu School of Computing, Qinghai Normal University, China School of Computing, Qinghai Normal University, ChinaView Profile , Ying Chen School of Computing, Qinghai Normal University, China School of Computing, Qinghai Normal University, ChinaView Profile Authors Info & Claims ICBTA 2021: 2021 4th International Conference on Blockchain Technology and ApplicationsDecember 2021 Pages 11–16https://doi.org/10.1145/3510487.3510490Online:25 February 2022Publication History 0citation59DownloadsMetricsTotal Citations0Total Downloads59Last 12 Months59Last 6 weeks16 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Gesture-Based Educational System that Integrates Simulation and Molecular Visualization to Teach Chemistry

Technology has proven effective in enhancing teaching methods. Gesture-based applications have a high potential for use in the development of teaching methods in schools. Simulation and 3D visualization, in particular, are some of the most important technologies that positively affected education. The implementation of these technologies in the education sector can determine students’ potential and enhance their interest in learning in scientific fields. This study explored the use of a chemistry education system that integrates virtual simulation and molecular visualization and uses a Leap Motion controller to teach chemistry in secondary schools. The sample comprised 113 students enrolled in four different classes in a public secondary school in Saudi Arabia. The data were analyzed using an ANOVA test to compare the results. The results indicate that the students who used the proposed educational system achieved better learning results than the students who participated in theoretical classroom learning only. Moreover, students who used the proposed educational system learned microscopic-level concepts better than those who used a real chemistry laboratory. Finally, combining simulation and molecular visualization and using gesture-based technology has a significant and positive effect on the students’ learning experience, as demonstrated by the results of this study which contributes to the development of education through the utilization of modern technology.
Algorithms May Assist Expert Pathologists in Prostate Cancer Diagnosis

Abstract Algorithms matching the performance of expert pathologists in prostate cancer diagnosis were designed.
A Survey on Techniques Used for De-Speckling of SAR Images

Because of the capacity to work at any climate circumstance and infiltration ability through the clouds and soil, SAR (Synthetic Aperture Radar) imagery has been very demanding.SAR imagery system captures images from any satellite or moving object such as aircraft etc. As per the height of a RADAR system increases the resolution of SAR images is also increased. Due to high-resolution images and multiplicative noise known as speckle, it is not easy to analyze these images. So, it has become very important to remove speckle from these images.There are great numbers of de-speckling techniques have been proposed in past. It is very difficult to identify a suitable technique for a de-speckling task. In this article, authors have proposed a literature survey of some techniques.Authors compared best techniques based on their merits and the different parameters used so far for the observation of SAR images. In recent years, the popularity of deep learning has grown tremendously. For de-speckling of SAR images, various improved CNNs and deep learning based approaches have been proposed.
Pengembangan Aplikasi Tiga-Tingkat Menggunakan Metode Scrum pada Aplikasi Presensi Karyawan Glints Academy

The rapid development of technology requires a software development management system that can be adaptive in rapidly changing circumstances. Scrum is an agile method that has the advantage of being agile and adaptive. Glints Academy holds an Industry Project Exploration as the program to prepare students for the rapid development of technology and reduce the gap between the education field and industrial field by MBKM program from the Ministry of Education and Culture. This study aims to apply the Scrum method in a heterogeneous developer team and divergent ability backgrounds to build an application with three-level architecture. The developer team is college students who come from different regions spread across Indonesia with full online implementation. Scrum is used because it is advantageous to other methods in a relatively fast-changing environment and also provides good quality control. The sprints were carried out in two sprints with two weeks of development in each sprint. The application built is an employee attendance application with a three-tier architecture: client, server, and data. The client-tier application is a front-end server built using the React.js framework while the server-tier and data-tier are built-in back-end servers with the Node.js and Express.js frameworks. JWT (JSON Web Token) authentication determines access role to functions and resources available on the back-end server. The result is a web application that fulfills the entire product backlog determined by the product owner. The results of this research are this method can used to develop features enhancement in the middle of the application development process without affecting the main feature development and this method is effectively used for different team developer backgrounds and during its online development
Use of augmented reality as a support for the visualization of urban data

The involvement of citizens and residents of the participating part of the city in the decision-making process of urban planning is becoming an increasingly topical issue. Until recently, there were no ergonomic visualization tools for this purpose, and citizens could only get acquainted with the city’s development plans through maps and technical drawings, which were not always understandable to everyone. In our article, we will try to describe our results in the field of data visualization using augmented reality, which we utilize to link the real world with added content related to the selected location. The first attempts in this area focused on blending of the historical state of the selected site with the present, or projecting the construction plans of the planned construction on the area where the building is to be built. We conducted a survey of the acceptance of this application by the general public as part of the Researchers Night event, which we accompanied with a questionnaire survey to find out how the outputs of the presented application affect citizens, whether they find it useful, simple and comfortable. The results of the survey show that the application is well received, the solution is evaluated as useful and meaningful. However, there is also a wide additional application potential. As part of the discussion of the results of the questionnaire survey, the follow-up directions of future research are indicated.
Full-round differential attack on DoT block cipher

Lightweight block cipher DoT was proposed by Patil, Bansod and Kumar in 2019. It is based on the substitution permutation network (SPN) structure. Its block and key size are 64 bits and 128 bits respectively. In this paper, we analyse the security of DoT against differential attack. We construct differential characteristics for 2 rounds and 6 rounds with probability of 2−4 and 2−12 respectively. These characteristics are used to construct the full- round differential distinguishers for DoT with probability of 2−62. The diffusion layer of DoT is a combination of byte shuffling, 8-bit permutation (P8), 32-bit word shuffling and circular shift operations. We analyse the differential attack resistance of DoT without P8 which proves that its application weakens the differential attack resistance of DoT.
Analyzing the Effects of Text Representations on the Performance of Document Clustering in Public Health Tweets

When working with large amounts of textual data from social media platforms, it is essential to identify topics quickly and accurately. Different methods have been developed to collect information from online platforms, but not all the approaches have provided a good result. This paper investigates various text representation methods based on the vector space model (VSM) and word embedding models for embedding short texts into low-dimensional representations for semantic similarities. Next, we evaluate topic modeling and three document clustering algorithms, trained on various text representation models, to discover topics from the textual data collected from Twitter. We compare six feature representations obtained from term frequency–inverse document frequency (tf-idf) matrices and word embedding models, as well as a latent Dirichlet allocation topic model. Obtained results suggest that clustering techniques applied to VSM-weighted word embedding feature representations methods outperform traditional word embedding models. Further, we interpret the usefulness of the clustering results by analyzing tf-idf-based top terms. Finally, we create the word clouds for a better understanding of clusters.
Asymmetric Cryptosystem for Triple Color Images Using 6D Hyperchaotic System and Equal Modulus Decomposition in Fractional Transform Domain

A novel scheme for security of triple color images using a 6D hyperchaotic system and equal modulus decomposition (EMD) in a two-dimensional fractional discrete cosine transform domain (2D-FrDCT) is presented in this paper. To improve the reliability of the asymmetric cryptosystem, a 6D chaotic map is utilized to generate three public keys as well as three secret keys. The public keys are used to encode digital images into the 2D complex matrix. After 2D-FrDCT in the encoded complex matrix, the EMD is executed to obtain the powerful trapdoor function. Finally, the secret keys are used to scramble the private key and a XOR operation is operated to get encrypted images. The private key is used during decryption. The results of experiment and the security analysis are discussed to check the capability and validity of the presented scheme.
Engineering shortcut-based operations on a nitrogen-vacancy spin qubit and microwave photons via optimized drivings

Optimal control of quantum systems is of significance to information processing and state engineering. Here an efficient scheme is proposed for engineering shortcut-based operations on a nitrogen-vacancy spin qubit and microwave photons via simplified drivings. The spin qubit dispersively coupled to a cavity field constitutes a composite system. Within a three-state subspace, we treat an effective interaction between the composite system and two classical drivings. By the technique of invariant-based shortcuts to adiabaticity, a state swap via Rabi pulses with sine- and cosine-typed waveforms and the generation of entangled states using constant Rabi pulses can be constructed, respectively. Moreover, with the accessible rates of qubit decoherence and photon decay, robust operations could be achieved by numerically simulating noise effects. The strategy could offer simple yet versatile avenues to implement the shortcut-based operations on the composite spin–photon system experimentally.
LESN: Low-Light Image Enhancement via Siamese Network

Images have become an important medium for people to get in touch with the world and new things. Low-light images enhancement is often used in target detection, night reconnaissance, government office and other fields. Images collected in low-light environment suffer from insufficient brightness, which makes it difficult for the images to be effectively used in subsequent vision tasks. In response to this problem, many researchers have conducted in-depth research on low-light image enhancement. However, their methods mostly failed in extremely low-light scenes, and even amplified the underlying noise in the images. That is why we propose a new low-light image enhancement method that integrates deep reinforcement learning via siamese network. We conducted experiments on the public LOL dataset and achieved good experimental results.
Influence of Initialization and Modularization on the Performance of Network Morphism-Based Neural Architecture Search

Neural Architecture Search (NAS), the process of automatic network architecture design, has enabled remarkable progress over the last years on Computer Vision tasks. In this paper, we propose a novel and efficient NAS framework based on network morphism to further improve the performance of NAS algorithms. Firstly, we design four modular structures termed RBNC block, CBNR block, BNRC block and RCBN block which correspond to four initial neural network architectures and four modular network morphism methods. Each block is composed of a ReLU layer, a Batch-Norm layer and a convolutional layer. Then we introduce network morphism to correlate different modular structures for constructing network architectures. Moreover, we study the influence of different initial neural network architectures and modular network morphism methods on the performance of network morphism-based NAS algorithms through comparative experiments and ablation experiments. Finally, we find that the network morphism-based NAS algorithm that uses CBNR block for initialization and modularization is the best method to improve performance. Our proposed method achieves a test accuracy of 95.84% on CIFAR-10 with least parameters (only 2.72 M) and fewer search costs (2 GPU-days) for network architecture search.
Toward Using Cloud Computing at Universities in Developing Countries Considering the Covid-19 Crisis

The Cloud computing is one of the trendiest buzzwords utilized these days. It is the upcoming technology provisioning resources to the customers in the form of different services like software, infrastructure and platform. Services are made available via the Internet to users on demand. Cloud Computing services are aimed to furnish easy, scalable access to resources, applications, and services and are wholly handled by a cloud service provider. A Cloud Computing service can automatically scale to satisfy the demands of its customers. The service provider delivers the hardware and software needed for the service therefore there is no essential for a company to supply or deploy its own resources or assign information technology (IT) staff to control the service. Universities take benefit of available cloud services delivered by service providers and allow their own users/learners to accomplish business and academic works. In this paper, we will review what the cloud computing services will furnish in the educational field, specially at universities in developing countries where the usage of computers are more concentrated and determine the opportunities of common applications for students and teachers, and particularly considering the Covid-19 crisis. The teachers and students can use the Cloud applications anywhere at any time without going through the installation step using a device connected to Internet. This work presents a guideline for successful Cloud Computing usage.
KNN Virtual Currency Price Prediction Model Based on Price Trend Characteristics

Bitcoin presents a new monetary concept and has received a lot of interest from various parties, which in turn has created a new form of investment in virtual currencies. The prices of virtual currencies tend to change quickly and fluctuate greatly, causing many investors to rush into the market in a frenzy, ignoring the riskiness behind it. The price prediction of virtual currencies can not only provide reference for investors’ investment, but also reveal its financial laws to a certain extent, improve the risk warning mechanism, and enhance its stability and safety. In this paper, we propose an improved KNN algorithm for virtual currency price prediction. The new model restructures the data and converts the original single-point price data into the trend characteristics of the price. The experimental results show that the improved KNN model has better prediction effect compared with the traditional KNN model as well as the logistic regression model.
Retroactive Packet Sampling for Traffic Receipts

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network-transparency" system. In this system, networks emit receipts for a small sample of the packets they observe, and a monitor collects these receipts to estimate each network's loss and delay performance. Sampling is a good building block for this system, because it enables a solution that is flexible and combines low resource cost with quantifiable accuracy. The challenge is cheating resistance: when a network's performance is assessed based on the conditions experienced by a small traffic sample, the network has a strong incentive to treat the sampled packets better than the rest. We contribute a sampling algorithm that is provably robust to such prioritization attacks, enables network performance estimation with quantifiable accuracy, and requires minimal resources. We confirm our analysis using real traffic traces.
Violence Recognition Using Convolutional Neural Networks

Livestream videos are becoming the norm in this digital era. With the advancement of streaming technology, real-time sharing has been effortless for many of the global population. There are many platforms that allow anyone to create or view livestream videos. This situation, however, creates a high possibility of showing inappropriate videos as well. Violent actions explicit languages and even crimes can be streamed, intentionally or otherwise. The impact on viewers may vary, though mostly negative. Thus, this research is motivated to help censor these unwanted scenes in real-time so that the negative impact can be reduced or avoided. By implementing the convolutional neural networks (CNNs) and training on three different off-the-shelf models in a restricted environment, it was found that the VGG19 model is the best for this research to be deployed in the censoring application compared to the MobileNetV2 and InceptionResNetV2 models.
Static smartphone images supplemented by videoconferencing for breast telepathology

Both static images and videoconferencing by smartphone have been tried for telepathology. Combining the two approaches for telepathology might offset some of the limitations of both techniques.A total of 150 cases of breast pathology were retrieved and were photographed by a trained junior pathologist using a smartphone camera and binocular microscope and the images were sent as Google photos attachments with email to three pathologists. The pathologists opened the emailed attachments during separate prescheduled google meet videoconferencing sessions and rendered the diagnosis verbally to the junior pathologist. The pathologists were given the option of asking for live dynamic images of the cases during the meet which were transmitted using the rear camera of the smartphone with the video option of google meet.The overall concordance rates for the three pathologists were 96.6%, 99.3% and 98.0% (Mean 98.0%). The reporting pathologists asked for dynamic live images in 23, 20 and 18 cases respectively. Out of these 61 cases, a discrepant diagnosis was made in 7 cases (11.4%). The reporting pathologist was satisfied with the quality of static images (Mean Likert score 4.2/5). However, concerns were raised on the consistency of video quality (Mean Likert score 2.5/5). The screen share option of Google meet was found useful to discuss specific features of the case. (Mean Likert Score 4.6/5) CONCLUSION: The combined approach to telepathology has shown some promise and it is hoped that with adequate training the discordance can be reduced.