Reviewerly/exHarmony huggingface dataset:
Instrumental and technological approaches to the organization of reflection in blended learning (on the example of humanities)

The paper analyzes innovative models of the educational process organization based on distance technologies with the ICT application services, their features and didactic value. Its about the main models of blended learning technology: station rotation, flipped classroom, lab rotation, individual rotation. The implementation of each model helps to achieve certain goals: differentiation and individualization of the educational process, development of students skills for individual work and self-organization, a more rational distribution of educational time in the classroom, ensuring the flexibility of the educational process, etc. The analysis of publications on this topic showed that researchers are interested in a wide range of issues related to the implementation of blended learning models at the lessons by different disciplines, such as: the usage of various tools, the analysis of models effectiveness, the usage of electronic resources, students motivation and evaluation, the role of the teacher, etc. However, little attention is paid to the organization of reflection. The paper analyzes the role of reflection in the learning process and its place at the lessons based on the models of flipped classroom and station rotation. The authors also give examples of methods and tools for organizing reflection at the lessons of humanities.
RGB-NIR Demosaicking Using a Two-Phase Primal-Dual Algorithm with a Laplacian Guided Image Filter Prior

Demosaicking is a process to interpolate raw data subsampled by the color filter array into a full-resolution image. In this work, a new RGB-NIR demosaicking algorithm based on the primal-dual method is proposed. In the algorithm, we use total variance and guided image filters as the regularization terms, so that both flat and edge regions can be properly addressed. In addition, a two-phase optimization scheme is introduced to avoid problems caused by over-correlating the RGB and NIR channels. We also design an early-termination mechanism, which can stop iterations when it foresees the result could go wrong. As demonstrated in the paper, the proposed algorithm can preserve high frequency regions well while having chroma noises, halos, and artifacts effectively removed.
Improving the Readability of Unformatted Text using Multitask Attention Networks

Unformatted text is a big obstacle to human reading and degrades the performance of many downstream language understanding tasks. To improve the readability, this paper proposes a multitask deep neural model to restore format standards including punctuation and capitalization. Unlike prior research which usually solved a single task or many tasks separately, our model employs multitask learning to simultaneously perform the restoration tasks. The model consists of a backbone network to learn language features, and attention-based predictors for the two tasks. To find the efficient encoding method for unformatted text, we analyze the model behaviour with different backbone architectures such as convolutional neural networks (CNN), unidirectional and bidirectional recurrent-based networks. The model is validated on two Vietnamese datasets and integrated into an automatic speech recognition (ASR) system. The experiments show the promising results for both restoration tasks and the applicability of our model.
Neural Network Optimization for Prediction of Student Study Period

The student's study period's in a university was significant in implementing higher education goals and study programs to improve accreditation level. The student's study period's prediction can make higher education institutions' foundation in making future policies. Several factors in implementing students during their studies, including the cumulative achievement index (GPA), affect the study period. Furthermore, the institution often does not consider the conditions or the student's study period's predictive value at its campus. A neural network (NN) is a prediction or classification method that previous researchers have widely used because it is reliable in solving prediction problems. The main problem with improving the accuracy of the NN is the tuning parameter. The neural network model has algorithms for optimization, namely, Particle Swarm Optimization (PSO) and Genetic Algorithm(GA). Based on the experiments and analyses that have been done, the accuracy has been obtained in the GA (GA-ANN) Neural network model with an accuracy score of 71.4%. The score is gained from the parameter specification number of epoch 5, mutation rate = 0.9, layer size 20, tanh activation function, adam solver, and 1000 maximum iteration.
Economic supply chain management of advanced manufacturing industry based on blockchain technology

Abstract Advanced manufacturing industry is the core of modern manufacturing industry, which is related to the overall situation of the whole national economic and social development. This study mainly discusses the advanced manufacturing economic supply chain management under the blockchain (BC) technology. The smart contract function in the BC will strengthen the execution of credit agreements between enterprises. In the application scenario of smart contract, as long as one party performs its economic activities, the system will force the other party to perform the contract directly, which improves the credibility of all parties in the chain and reduces the risk of default. To avoid false trade, commercial factoring risk management, one is to establish a cooperative relationship with banks. The second is to issue asset securitization products. Third, cooperate with internet financial platform. In 2018, under the background of slower growth in operating income, the balance of accounts receivable of enterprises fell more, and 2019 performance it began to pick up, and the substantial increase in operating income has also brought about a substantial increase in accounts receivable. Since then, operating income has maintained a rapid growth rate, but the balance of accounts receivable has not increased significantly, but has decreased slightly. After the implementation of supply chain financing, the proportion of accounts receivable of B advanced manufacturing company in operating revenue has decreased from 23.17% in 2018 to 13.45% in 2020. The implementation of supply chain financing designed in this study helps advanced manufacturing companies to control the balance of accounts receivable.
Construction of Parallel Corpus of Foreign Publicity Based on Computer-Aided Translation Software

Foreign publicity plays an important role in increasing the visibility of a country and companies at home and abroad. The application of big data, computer-aided translation, and machine deep learning contributes to the translation of foreign publicity materials. This has made a parallel corpus of foreign publicity a hot spot of research in recent years. This paper introduces the construction of a parallel corpus of foreign publicity in detail and discusses how to apply computer-aided translation software to the construction of foreign publicity corpus, to improve translation efficiency. The alignment of a parallel bilingual corpus of foreign publicity lays the basis for building a bilingual parallel corpus. The main task of building the foreign publicity corpus is to build an English Chinese parallel corpus with sentence-level aligned according to the characteristics of Chinese and English. Its functions include the alignment of English and Chinese sentences, screening and storing databases, and browsing, retrieving, and exporting the corpus. This research is of great significance for building a corpus that meets the specific needs of foreign publicity.
A DNN Inference Offloading Scheme for Storage Arrays

Recent advancement in deep learning technology has brought tremendous amounts of deep neural network (DNN) inference jobs into a data center. While hardware accelerators for DNN computations have made rapid progress, network capability to transfer a large amount of data needed for DNN computations still is a common bottleneck threatening service level objectives (SLO). To alleviate such a bottleneck occurred by data transfer, we propose a novel system architecture that offloads DNN inference job to a storage node. Our system includes concise API which mitigates the programming burden needed to offload computations, and software architecture to conduct general DNN inference jobs in a conventional storage system. Experimental results show that our system exhibits a 35% of shorter average latency and more than 99% reduction in network usage in common image retrieval and classification jobs over existing systems.
Cascaded Learning with Generative Adversarial Networks for Low Dose CT Denoising

CT machines can be tuned in order to reduce the radiation dose used for imaging, yet reducing the radiation dose results in noisy images which are not suitable in clinical practice. In order for low dose CT to be used effectively in practice this issue must be addressed. Generative Adversarial Networks (GAN) have been used widely in computer vision research and have proven themselves as a powerful tool for producing images with high perceptual quality. In this work we use a cascade of two neural networks, the first is a Generative Adversarial Network and the second is a Deep Convolutional Neural Network. The first network generates a denoised sample which is then fine-tuned by the second network via residue learning. We show that our cascaded method outperforms related works and more effectively reconstructs fine structural details in low contrast regions of the image.
Person Re-identification Using Deep Learning with Mask-RCNN

In today’s world, we have CCTV cameras everywhere and they have a huge value when we consider public safety in a public environment. A major problem in such systems is person re-identification, which is defined as “the problem of identifying people across images which are extracted from various surveillance cameras which don’t have any overlapping fields of view.” With the increasing need of people to identify persons in the videos, this work is also getting increased importance. It also covers many other applications such as tracking across cameras, multi-camera behavior analysis and search for criminal activities. But, this problem turns out to be quite challenging because of the huge amounts of changes in lighting conditions, point of viewing, change of pose and the background. Thus, the objective is to implement a system to identify a person again and again from multiple images that have been captured by different cameras, whose fields of view do not overlap.
Analysis of the possibility of using solar energy for the energy supply of the innovative ELTRO transport system for Myanmar

Background: The development and research of the applicability of various mathematical models for calculating the solar radiation of companies is an urgent task for the innovative transport system of Myanmar.&#x0D; Aim: Determination of the daily balance of energy generated by solar panels to perform a given operation of the transport system.&#x0D; Materials and methods: Mathematical, algorithmic and software (based on the Python language) have been developed for energy supply and creation of interactive geo-information models for the ELTRO transport system.&#x0D; Results: To estimate solar resources at a point with coordinates ( = 19.76 s. w.; = 96.07 w. d.), data from the databases "NASA", "Meteonorm" and the Myanmar GMS were used and compared with the actual data on the Myanmar GMS database in order to determine the reliability of the information presented in them.&#x0D; Conclusion: The results obtained made it possible to determine the structure of the solar energy supply system and the parameters of typical solar energy modules that provide energy supply not only to the transport system, but also to the adjacent territories.
CT-DANN

Unsupervised domain adaptation aims at leveraging supervision from an annotated source domain for performing tasks like classification/segmentation on an unsupervised target domain. However, a large enough related dataset with clean annotations may not be always available in real scenarios, since annotations are usually obtained from crowd sourcing, and thus are noisy. Here, we consider a more realistic and challenging setting, wild unsupervised domain adaptation (WUDA), where the source domain annotations can be noisy. Standard domain adaptation approaches which directly use these noisy source labels and the unlabeled targets for the domain adaptation task perform poorly, due to severe negative transfer from the noisy source domain. In this work, we propose a novel end-to-end framework, termed CT-DANN (Co-teaching meets DANN), which seamlessly integrates a state-of-the-art approach for handling noisy labels (Co-teaching) with a standard domain adaptation framework (DANN). CT-DANN effectively utilizes all the source samples after accounting for both their noisy labels as well as transferability with respect to the target domain. Extensive experiments on three benchmark datasets with different types and levels of noise and comparison with state-of-the-art WUDA approach justify the effectiveness of the proposed framework.
Investigating the role of usable security in developers' intention toward security enhancement in <scp>service‐oriented</scp> applications

Abstract Security threats have increased in recent years. To motivate the developers to stick to the security policies, the notion of usable security is investigated. Enhancing the usability of security services leads to developing software products that are resilient against security threats. In this paper, we investigate the role of usable security in developers' intention toward fulfilling the security considerations through the development process. To study this, we conducted empirical research on 25 experts in the field of software development. We proposed a model for analyzing the research outcome using statistical inference based on an extension of the theory of planned behavior. In particular, we posit that attitude toward the development of usable services, subjective norms, and moral obligation are determinants of intention to develop usable security services. We developed 11 security services that are used in the development of service‐oriented software. We asked the experts to integrate the security services in the software and studied the integration process using a detailed questionnaire. The findings indicate a strong positive influence of all correlations, which describe developers' intentions to develop usable secure services to 52%. Our insights enhance the understanding of developers' intentions toward developing usable secure services and their fulfillment.
Towards the Implementation of FAIR Principles on an Earthquake Analysis Platform

In our work, we are presenting a FAIR approach for building an Earthquake Analysis Platform. In this context we started to perform data analysis and visualizations of existing seismic data, collected from different public sources, for various geographical regions around the world, and noted the different formats and standards which precluded a unitary treatment of the seismic zones of interest. That drove our research towards the FAIR principles, towards data standardization and data curation, developing and adapting the data formats so that we can have the data ready for our analysis programs developed in house. We thereby aimed at developing the necessary tools and processes that support the FAIRification of the seismic databases of interest (at least on our local data processing pipelines). Within this approach our computing services would become data-source-agnostic, allowing the users to perform their data analysis without being impacted by the differences in metadata indexing standards. To this end, the FAIRification process relies on building data ingestion pipelines, i.e., a collection of programs built for collecting, parsing, extracting only the necessary data and storing that relevant data in a new format which satisfies our requirements.
A New Diverse Dataset and Comparison of Algorithms for Pedestrian Detection in Top-View Fisheye Images

Pedestrian detection in images has been extensively researched, however existing detectors designed for perspective images usually fail to detect people on top-view fisheye images due to the various appearances of people. In this paper, we establish a new diverse fisheye dataset which consist of indoor and outdoor fisheye images from public and private datasets. We also adapt three types of spatial transformation to make the visual look of the pedestrians as upright as possible and four commonly used algorithms for pedestrian detection without retraining of the detector models. In addition, we analyze the pedestrian detection results with different conditions to figure out the reason of the results.
FHSR: A Successful Application of Deep Learning Technology in Signal Retrieval

With the widespread application of frequency hop-ping (FH) technology, a large number of FH signal monitoring data have been accumulated. Big data brings new opportunities and challenges to radio supervision, one of which is signal retrieval. The task of signal retrieval is to find similar signals for a given segment of signal. In this paper, we propose an idea of FH signal retrieval. Firstly, transform the FH signal into two-dimensional images, and the radio signal retrieval problem is transformed into an image retrieval problem. Then, the advanced achievements in the field of image retrieval can be used to complete signal retrieval. Based on this idea, we propose an FH signal retrieval algorithm named FHSR. In order to extract the signal information better, we also propose a data augmentation algorithm. Experiments show that our method achieves good results in retrieval accuracy and speed, which meets the actual needs.
DILG Negros Occidental Contact Tracer Information Management System

COVID-19 pandemic reshapes the use of technology and innovation in an organization. Organizations are adopting and utilizing technologies in their day-to-day operations. Information Systems offer a wide range of solutions in terms of data management and interconnecting people with the use of a system. Its use was proven to enhance and improve the productivity of an organization in this pandemic. These promising results led the researcher to develop the Department of the Interior and Local Government (DILG) Negros Occidental Contact Tracer Information Management System (CTIMS). The CTIMS is a web application that enables DILG Negros Occidental Personnel to manage and facilitate all DILG Hired-Contact Tracers (CT) in the Province of Negros Occidental. The automated system made the data collection easy and error-free, streamlined reporting, and eliminated the time-consuming process of consolidating the Accomplishment Reports (ARs) from the Field and Provincial Offices. The document tracker feature replaced the manual entry in the logbook. It also improved the received and released document reporting. It improved the processes of the CT Task team compared to the manual operations and is expected to increase the productivity, effectivity, and efficiency of DILG Negros Occidental.
Factors Influencing Consumer Intention in Indonesia to Utilize E-Marketplace of Agricultural Products

Food security is the government&#x0027;s top priority during this pandemic, because the government is trying to ensure that people can still meet their daily food needs even though the government issues policies to limit community activities. The existence of an e-marketplace is an attractive option for the community to meet their food needs without having to do outside activities. E-marketplaces can also be a solution to break the long distribution chain for agricultural products. This study aims to break the long distribution chain of agricultural products through consumer perceptions, trust, and consumer experiences in shopping for agricultural products through e-marketplaces. The sample used in this study are consumers who have purchased agricultural products through e-marketplaces. The data was obtained through a questionnaire using the google form. The results showed that the perceived ease of use, perceived benefits, trust, and experience simultaneously affect the intention to use the e-marketplace of agricultural products. This research study can provide information for the agricultural sector regarding the factors that can affect consumers to use e-marketplaces in obtaining agricultural products.
The analysis of student kinesthetic learning activity on the materials of Compton and photoelectric effects

Abstract This study aims to analyze the kinesthetic learning activities of students on Compton and photoelectric effects. This further involved a qualitative descriptive study, where students of class XII Mathematical and Natural Science 2, State of Madrasah Aliyah in Palu Region (Central of Sulawesi, Indonesia), were the sample population examined, in the odd semester of 2018/2019 academic session. Data was obtained through observation of learning activities, concept understanding tests, interviews and learning style identification questionnaires. The selection of respondents was based on the level of understanding of students’ concept categories. The results of the study showed that students perform kinesthetic learning activities very well, as seen from their endeavors in following the lessons and the results of theoretical understanding assessments, which were observed to be in the high category. Furthermore, the interview evaluated that the students are happy with the kinesthetic learning method carried out as it promoted their understanding and recollection of the content of the material and questionnaires indicate that students have visual, auditory and kinesthetic learning styles. From the performance of Compton and the photoelectric effect, it was shown that students playing as electrodes and electrons (kinesthetic learning style) possess high understanding of the concept in the kinesthetic learning model.
Research on Hotel Comment Emotion Analysis Based on BiLSTM and GRU

Aiming at the problem that LSTM can not deal with the long-distance two-way semantic dependence, complex model calculation and long training time, this paper proposes a C-BiLG deep learning algorithm based on two-way LSTM and GRU for emotion analysis task. Two-way LSTM has fewer parameters and faster model training, which can effectively extract the deep-seated information of the text. The combination of BiLSTM and GRU can solve the problem of semantic dependence and gradient disappearance. According to the control experiment, the experimental results show that the algorithm can improve the accuracy and optimize the classification effect in the emotional analysis of hotel comment data set.
Weather station for monitoring the microclimate in the room on arduino platform

In this project was shown approaches to the implementation of a weather station for monitoring the microclimate in the room on Arduino platform. Considered a platform for project development and software environment. The virtual scheme of the weather station is modeled. The main functions of the components are described and their connection to the microcontroller is shown. The process of firmware of the microcontroller is considered. The algorithm of system operation is described. A functional electrical diagram were also constructed. Described the weather station settings and instructions for use are provided. The device was also tested. Comparisons with analogues are given.