Reviewerly/exHarmony huggingface dataset:
Application of Image Processing and Identification Technology for Digital Archive Information Management

It is difficult to manually build a digital management system for archive information. This paper explores the application of image processing and identification technology for digital archive information management, trying to provide convenient and swift services to archive mangers and archive information requestors, and to strengthen the standardized management of archive information. Firstly, the authors summarized the difficulties in digitalizing paper archives, and explained how to correct the tilt of digital archives. Next, the adaptive histogram equalization was improved to realize high-quality digitalization of archive images. In addition, the image processing flow was explained for the digitalization of archive images, and the horizontal projection histogram was adopted to quickly extract and detect the archive image texts being digitalized. The proposed image processing approach was proved effective through experiments.
Enhancement of scientific research function of future teachers in modern education system

The global trend of accelerating scientific and technological progress, the introduction of information technologies, and the transformation of the vocational education system require the development of professional and general cultural competencies in future specialists. The purpose of the study was to scientifically substantiate, create and experimentally test a theoretical model of the formation of a research function in future teachers of vocational training. The methods employed in this research was complementary research methods: theoretical (analysis of scientific literature on the research problem; theoretical modeling, analysis, generalization, comparison, systematization, construction of the process of forming scientific and professional competencies of future teachers); empirical (questionnaire, interview, testing, cross-sectional method); statistical (processing of research materials, primary statistical processing, correlation analysis, analysis of the results of experimental activities). The result of this study showed that the problem of studying and designing a system of pedagogical conditions for the formation of research culture is relevant in the context of the introduction of innovative technologies.&#x0D; &#x0D; Keywords: Future teachers; scientific research; modern education system.
Investigating the Potential of Integrating Augmented Reality into the 6E Instructional 3D Geometry Model in Fostering Students’ 3D Geometric Thinking Processes

Schools as a source of knowledge need to facilitate the 3D geometric thinking process involving a series of cognitive action. Therefore, this research investigates the potential of integrating augmented reality (AR) into the 6E instructional 3D geometric model (6E I3DGM). This is an exploratory research carried out with a teacher and 28 junior high school students in Indramayu District, Indonesia. The data were collected through observations and interviews and examined, selected, extracted, and coded based on the criteria determined. The data collected were further interpreted and concluded, and descriptively analyzed using content analysis. The result showed that learning that integrates AR into 6E I3DGM has the ability to raise 3D geometric thinking processes in the dimensions of representation, spatial structuring, and measurement. This is because the use of AR can characteristically help students represent, construct nets, visualize the structure of 3D shapes, and assist in measuring the surface area and volume of 3D geometry. Moreover, it also facilitates students in constructing knowledge through a series of 3D geometric thinking activities. Therefore, the use of learning that integrates AR into 6E I3DGM has the potential to be an alternative for learning 3D geometry both during and after the COVID-19 pandemic.
Multiagent Neurocognitive Models of the Processes of Understanding the Natural Language Description of the Mission of Autonomous robots

The article is devoted to the development of neurocognitive models of the processes of understanding natural language statements that describe the goals, essential conditions and the course of the mission of autonomous robots. Such models serve to provide a dialogue interface in human-robotic teams. The key problem of automatic formation of a plan for achieving goals common for all members of such a team is solved on the basis of using algorithms for synthesizing the optimal path in a single decision graph based on self-organization processes occurring in the multi-agent neurocognitive architecture we have developed. This approach makes it possible to take into account the semantics of natural language messages and the implicit information contained in them when constructing the desired plan.
Noiseless linear amplifier enables continuous variable quantum teleportation

Continuous variable quantum teleportation (CVQT) plays an important role in practical quantum communication. However, the noise and loss in the actual environment will lead to the degradation of entanglement, which has an effect on the performance of the system in terms of the transmission distance and hence hinders its implementations. In this paper, we suggest an approach to improving the performance of the CVQT system with an embedded noiseless linear amplifier (NLA). By using the NLA, one can amplify the degenerated entangled source to compensate for the attenuation of the fiber. Then we consider the influences of the available gains of the NLA on the performance of the proposed scheme under different lossy channels and entangled sources. The simulation results show that the performance is improved in fidelity and transmission distance, which may provide a feasible and effective method of putting the long-distance CVQT into practical application .
Filterisasi Air Bersih dan Penyelamatan Sumber Mata Air di Desa Mulyoasri, Kecamatan Ampelgading, Kabupaten Malang

Water is a basic need of human life for drinking, cooking, washing, agricultural activities, plantations, animal husbandry, and various other daily activities. The purpose of implementing the Field Work Lecture Activities-Community Empowerment (KKN-PPM) of Thematic of the University of Islam Malang is to empower the village community in their efforts to utilize clean water and save of springs. KKN-PPM of Thematic activities are carried out for 1 month (1 February‒1 March 2020). One of the problems faced by the community is the difficulty of getting clean water. During the rainy season, the water is dark brown in color, caused by mud material blocking the springs, and made worse by the water becomes cloudy due to a water filtration system that is not yet available and does not have a water reservoir. Based on these problems, the students of KKN-PPM of Thematic carried out an empowerment program with the theme clean water filtration and efforts to save springs. Community empowerment activities related to clean water are carried out through four stages, namely: 1) Location survey and public consultation; 2) Household-scale clean water filtering, and socialization of the use of clean water filtering to the community; 3) Counseling and actions to protect springs; and 4) Evaluation of activities through pre-test and post-test to the community. The results obtained from activities the KKN-PPM of thematic are that the community in the Mulyoasri village positively welcomes the program by understanding how to make filtering equipment and assessing that it is efficient and effective enough to produce clean water. The community also gains understanding and is committed to jointly protecting springs.
Power Aware Energy Efficient Virtual Machine Migration (PAEEVMM) in Cloud Computing

In Cloud computing, Energy Efficiency is a latest research area. These days the dependency of industry on cloud is increasing day by day that leads to requirement of large number of datacenters as well as size of virtual machines. It needs migration of virtual machine along with energy saver. This paper has proposed a method for migrating the virtual machine based on virtual machine temperature’s threshold value named as Power Aware Energy Efficient Virtual Machine Migration (PAEEVMM) Method. This method migrates the high loaded virtual machine to the less loaded virtual machine based on temperature. The simulation is performed on CloudSim Plus and results are compared with other traditional first fit algorithm. The experiment shows that the proposed algorithm works better in terms of power usage and CPU utilization.
The impact of design on improved learning in virtual worlds: an experimental study

Virtual worlds are the most advanced form of virtual environments, which offer one of the best platforms for serving various domains. They are, especially, well suited for education, to cope with the physical restrictions imposed due to COVID-19 outbreak, as they offer classroom experience to their users through immersion. They are online interactive spaces which are collaborative, persistent, coherent, and social in nature. Users immersed in these spaces are represented in the form of digital characters called, avatars. Virtual worlds offer advanced navigation methods such as flying and teleporting to facilitate quick learning. This paper analyses the use of a partial but carefully reconstructed cultural heritage site, developed in OpenSimulator framework, for learning both in terms of discourse and quantitative analysis. Discourse analysis compares the developed virtual world presence with traditional content provisioning methods in terms of a large set of well-known characteristics. Quantitative analysis, on the other hand, is based on data collected from users after conducting simple learning experiments. It revealed that the properties such as realism, friendliness, advanced navigation, and being detailed and social in nature greatly attracted user attention in learning. The learning was fast compared with traditional methods, however, it was a little hard for naive users to start exploring the content. Pre and post learning responses of users revealed that their knowledge level was significantly increased. Based on valuable suggestions, it is planned in future, to add intelligence to traditional agents, so they may help in an increased learning experience of users, based on the knowledge gained in earlier sessions.
Double Branch Attention Block for Discriminative Representation of Siamese Trackers

Siamese trackers have achieved a good balance between accuracy and efficiency in generic object tracking. However, background distractors cause side effects to the discriminative representation of the target. To suppress the sensitivity of trackers to background distractors, we propose a Double Branch Attention (DBA) block and a Siamese tracker equipped with the DBA block named DBA-Siam. First, the DBA block concatenates channels of multiple layers from two branches of the Siamese framework to obtain rich feature representation. Second, the channel attention is applied to the two concatenated feature blocks to enhance the robust features selectively, thus enhancing the ability to distinguish the target from the complex background. Finally, the DBA block collects the contextual relevance between the Siamese branches and adaptively encodes it into the feature weight of the detection branch for information compensation. Ablation experiments show that the proposed block can enhance the discriminative representation of the target and significantly improve the tracking performance. Results on two popular benchmarks show that DBA-Siam performs favorably against its counterparts. Compared with the advanced algorithm CSTNet, DBA-Siam improves the EAO by 18.9% on VOT2016.
An Improved Saliency Detection Algorithm Based on Edge Boxes and Bayesian Model

Visual saliency detection aims to extract salient objects from the original image, making it less complicated to process the image. This paper combines an edge box algorithm with Bayesian theory to detect salient objects. The proposed saliency detection algorithm transforms the process of traditional detection method, and prioritizes the positioning of significant objects. Firstly, the Harris corners of the original image were calculated, and clustered by the improved clustering algorithm, yielding the number of salient objects in the image. Then, all possible positions of salient objects in the image were framed by the edge box algorithm, and the boxes were sorted in descending order of the score. According to the number N of clusters of the image corners, the N top-ranking boxes were selected to determine the salient regions. In this way, the position and number of salient objects were clarified. Based on the selected salient regions, the final saliency map was calculated by improved geodesic distance and Bayesian model. Experimental results show that our approach performed better than 11 existing algorithms in both simple and relatively complex scenes. In terms of objective performance, the accuracy and recall of our algorithm on MSRA10k, ECSSD, DUT-OMRON and SED2 datasets were higher than that of the other algorithms.
Learning a Discriminative Feature Attention Network for pancreas CT segmentation

Abstract Accurate pancreas segmentation is critical for the diagnosis and management of diseases of the pancreas. It is challenging to precisely delineate pancreas due to the highly variations in volume, shape and location. In recent years, coarse-to-fine methods have been widely used to alleviate class imbalance issue and improve pancreas segmentation accuracy. However, cascaded methods could be computationally intensive and the refined results are significantly dependent on the performance of its coarse segmentation results. To balance the segmentation accuracy and computational efficiency, we propose a Discriminative Feature Attention Network for pancreas segmentation, to effectively highlight pancreas features and improve segmentation accuracy without explicit pancreas location. The final segmentation is obtained by applying a simple yet effective post-processing step. Two experiments on both public NIH pancreas CT dataset and abdominal BTCV multi-organ dataset are individually conducted to show the effectiveness of our method for 2D pancreas segmentation. We obtained average Dice Similarity Coefficient (DSC) of 82.82±6.09%, average Jaccard Index (JI) of 71.13± 8.30% and average Symmetric Average Surface Distance (ASD) of 1.69 ± 0.83 mm on the NIH dataset. Compared to the existing deep learning-based pancreas segmentation methods, our experimental results achieve the best average DSC and JI value.
Making Video Quality Assessment Models Sensitive to Frame Rate Distortions

We consider the problem of capturing distortions arising from changes in frame rate as part of Video Quality Assessment (VQA). Variable frame rate (VFR) videos have become much more common, and streamed videos commonly range from 30 frames per second (fps) up to 120 fps. VFR-VQA offers unique challenges in terms of distortion types as well as in making non-uniform comparisons of reference and distorted videos having different frame rates. The majority of current VQA models require compared videos to be of the same frame rate, but are unable to adequately account for frame rate artifacts. The recently proposed Generalized Entropic Difference (GREED) VQA model succeeds at this task, using natural video statistics models of entropic differences of temporal band-pass coefficients, delivering superior performance on predicting video quality changes arising from frame rate distortions. Here we propose a simple fusion framework, whereby temporal features from GREED are combined with existing VQA models, towards improving model sensitivity towards frame rate distortions. We find through extensive experiments that this feature fusion significantly boosts model performance on both HFR/VFR datasets as well as fixed frame rate (FFR) VQA databases. Our results suggest that employing efficient temporal representations can result much more robust and accurate VQA models when frame rate variations can occur.
Intelligent Recognition of Physical Education Teachers’ Behaviors Using Kinect Sensors and Machine Learning

In this research, Kinect sensors were used to obtain body posture data of physical education (PE) teachers during simulated classes and in combination with classical algorithms of machine learning, to achieve the intelligent recognition of the classroom teaching behaviors of PE teachers.Kinect 1.0 was used to test 10 PE teachers without students during simulated classes, and the characteristics of body postures corresponding to different teaching behaviors during the classes of PE teachers were obtained through time sampling.The accuracy of the light gradient boosting machine (LightGBM) recognition model combined with the Kinect sensor was 0.998, which was significantly higher than those of other algorithms.The combination of Kinect sensors and machine learning enabled the intelligent classification of, for example, password teaching, language explanation, action demonstration, and guiding behavior during a simulated class of PE teachers.The recognition models trained by LightGBM were the most effective.
Threshold-free Pattern Mining Meets Multi-Objective Optimization: Application to Association Rules

Constraint-based pattern mining is at the core of numerous data mining tasks. Unfortunately, thresholds which are involved in these constraints cannot be easily chosen. This paper investigates a Multi-objective Optimization approach where several (often conflicting) functions need to be optimized at the same time. We introduce a new model for efficiently mining Pareto optimal patterns with constraint programming. Our model exploits condensed pattern representations to reduce the mining effort. To this end, we design a new global constraint for ensuring the closeness of patterns over a set of measures. We show how our approach can be applied to derive high-quality non redundant association rules without the use of thresholds whose added-value is studied on both UCI datasets and case study related to the analysis of genes expression data integrating multiple external genes annotations.
Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review

The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on the various images (e.g., radiological, pathological, and camera images) and non-image data (e.g., clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multi-modal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multi-modal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (1) overview of current multi-modal learning workflows, (2) summarization of multi-modal fusion methods, (3) discussion of the performance, (4) applications in disease diagnosis and prognosis, and (5) challenges and future directions.
Revisiting RCAN: Improved Training for Image Super-Resolution

Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight. However, most SR models were optimized with dated training strategies. In this work, we revisit the popular RCAN model and examine the effect of different training options in SR. Surprisingly (or perhaps as expected), we show that RCAN can outperform or match nearly all the CNN-based SR architectures published after RCAN on standard benchmarks with a proper training strategy and minimal architecture change. Besides, although RCAN is a very large SR architecture with more than four hundred convolutional layers, we draw a notable conclusion that underfitting is still the main problem restricting the model capability instead of overfitting. We observe supportive evidence that increasing training iterations clearly improves the model performance while applying regularization techniques generally degrades the predictions. We denote our simply revised RCAN as RCAN-it and recommend practitioners to use it as baselines for future research. Code is publicly available at https://github.com/zudi-lin/rcan-it.
Big Data Analysis of Water Saving Standard Based on Bibliometrics

At present, water-saving standard is an important technical means to implement national water-saving actions and the strictest water resources management system. In order to analyze the development process and research fields of water-saving standards in China, this paper makes quantitative statistics and qualitative analysis on seven aspects of water-saving standards, such as annual publication, drafting unit, research fields, and progress. The results show that the annual release of water-saving standards generally showed a trend of first rising and then declining, and the release reached its peak in 2017. The contribution of China National Institute of Standardization to the development of water-saving standards is dominant. From the point of view of water department, the water-saving standards in the industrial field are comprehensive. Our finding are helpful to better understand the development process of water-saving standards and provide reference for further improving the water-saving standard system and building a water-saving society.
Effects of Covid-19 on the Online Library and Information Science Degree at the University of León (Spain)

The Degree in Library Science and Documentation of the University of León started during the 1990–1991 academic year. This first curriculum suffered from a strong presence of subjects from the humanities, with a low fraction of experimental work in many subjects because of its inclusion within the Faculty of Philosophy and Letters. The first years attracted large numbers of students. However, the subsequent decrease in the number of students pursuing the degree drove us to initiate a change toward blended and online modalities. The Librarianship and Documentation Area of the University of León (ULe) already has a long history in blended teaching, starting with the Degree in Librarianship and Documentation, which has used the platform [email protected] since the 2006–2007 academic year; the Degree of Information and Documentation has used the blended modality since the 2010–2011 academic year, and it has been offered in the distance learning modality since the 2014–2015 academic year. These new technologies have been fully introduced into university education and have facilitated the teaching–learning process through the use of interactive programs via the Internet. The main objective of this column is to determine and study the kind of multimedia tools used by professors of the Bachelor of Information and Documentation at the University of León during the teaching–learning process and in virtual teaching before the COVID-19 pandemic, as well as to report which new tools were introduced by the university for this reason. We study the student–teacher and student–student interactions to help promote communication and participatory learning through chats, forums, email, hangouts, Skype, Meet, videoconferences, etc. To carry out this study, two types of methodology were applied: satisfaction surveys implemented by the Quality Office of the ULe to determine the degree of satisfaction of students and teachers, and a study of annual reports.