Reviewerly/exHarmony huggingface dataset:
Audiobooks: Reading Practices and Educational Technologies

Digital technologies have made it possible to create and replicate educational content in various forms: text, video, and audio. The post-PC generation actively uses audio content. There is a tendency to demand audiobooks as a format of educational resources. The purpose of the article is to identify and describe educational technologies and reading practices related to audiobooks. The novelty of the research is the analysis of student practices of audio reading. Methods: The first stage includes a desk study of audio reading as an educational technology, its capabilities and limitations, the representation of audiobooks in universities’ electronic libraries and the use of audiobooks in the educational process, educational technologies and practices of working with audiobooks. The second stage includes an empirical study of students ‘practices of accessing and using electronic libraries in the process of educational (1), leisure (2) activities, readers’ preferences for the reading format (traditional, audio reading) (3). Research results: With the development of digital technologies, electronic libraries have become a popular educational resource. The e-libraries extend the representation of the audio books. The possibilities and limitations of audio reading remain debatable. Audio reading as a resourcesaving technology (carefulness to vision, time optimization) has a number of advantages and at the same time requires the development of listening skills and critical perception of audio text.
PROBLEMATIKA PENGGUNAAN MEDIA PEMBELAJARAN DI SEKOLAH DASAR

This research was carried out based on the problem that teachers worked at SDN Maghileko did not often use learning media during the instructional processes.  By looking at the case, so this study aims to describe the factors that causes why teachers were difficult using the media. Arrying out this project used descriptive analytical study with a qualitative approach. The subjects were taken from the teachers at SDN Maghileko through interviews, observation and documentation to get data. Then the data analized by the interactive model of Miles and Hubberman, that consist of reduction, presentation, verification, and conclusion stages. The validity of the research data was tested by triangulation, both sources and techniques triangulation. The results of this study indicated that there were several factors that cause teachers were difficult using learning media. The first, teachers' perceptions on learning media. The use of instructional media were seen as things that make them more bussy. Second, the teachers have no sufficient ability to use instructional media, so far they do not want to use them,  especially the high-tech media. Third, the problem of creativity in making dynamic and creative learning media. Fifth, teachers felt comfortable when they teach in the class without using media because they are familiar with the lecture method. Based on the above results can be concluded that the main factors why teachers did not use learning media at SDI Maghileko were caused by the internal condition of the teachers themselves.This condition includes both cognitive, affective, and conative dimensions of teachers that limit themselves to use learning media. Negative perceptions like reluctance or fear of trying, lack of creativity, and tendency to maintain old habits make them difficult to use media in learning.
Combining scenario approach and Choquet integral in decision making

Abstract Scenario-based approach is applied to evaluate alternatives. Using the ideas of Dempster-Schaeffer a capacity distribution is defined over scenarios. This allows describing possible future developments in terms of individual and integrated expert judgments and transforming expert judgments into scenario assessments. To get a final numerical estimate of alternatives, we use integration over non-additive measures applying the Choquet integral.
Compound Fuzzy Clustering Anomaly Detection Based on Production Process Coupling

In recent years, the network security problem of industrial control systems is very severe. It is particularly important to detect abnormal situations in the industrial control system network correctly and timely to avoid disasters in the physical world. This paper proposes a compound fuzzy clustering algorithm, taking TE process as the research object, to analyze its working mechanism and distinguish security threats it may encounter. Considering the coupling relationship between the production process unit in the industrial control system, fuzzy clustering is used for each process unit, which performs detection and comprehensive decision-making to obtain the final anomaly detection result. Experimental results show that the proposed compound fuzzy clustering algorithm can realize anomaly detection in industrial control systems.
Enhancing Underwater Image Using Multi-scale Generative Adversarial Networks

Wavelength-dependent light absorption and scattering will reduce the quality of underwater images. Therefore, the characteristics of underwater images are different from those taken in natural. Low-quality underwater images affect the accuracy of pattern recognition, visual understanding, and key feature extraction in underwater scenes. In this paper, we enhance the underwater image using a multi-scale generative adversarial network with adjacent scale feature addition. Adjacent scale feature addition allows the network to more effectively capture the relevant characteristics between two image domains. The multi-scale discriminator can let the enhanced image more closer to the natural image. Our method does not rely on transmission maps and atmospheric light estimation. Experiments on a large amount of synthetic data and real data show that our method is superior to the state-of-the-art methods.
Verifying Absence of Hardware-Software Data Races using Counting Abstraction

Device drivers are critical components of operating systems. However, due to their interactions with the hardware and being embedded in complex programming models implemented by the operating system, ensuring reliability of device drivers remains to be a challenge. In this paper, we focus on the interaction of the driver with the device and present an approach for modeling this interaction and verifying absence of hardware-software data races. Specifically, we use the counting abstraction technique to abstract dynamic process creation in response to I/O acknowledgements sent by the device. We present the results of our approach on the modeling and verification of several Linux device driver models.
Saliency Detection by Superpixel Ranking for Person Re-identification

The research described in this paper consists in developing a person re-identification framework for multiple non-overlapping camera system. The proposed approach consists of three main steps. Firstly, human images are segmented into atomic regions using the concept of the superpixel. A saliency detection framework based on the manifold ranking is then carried out to estimate a saliency score map, which emphasizes the perceived important regions of an image. Finally, a flexible matching procedure is introduced to estimate the similarity between two images and to make the final decision of person re-identification. The performance of our system is evaluated on the well-known VIPeR dataset. The experimental results show that the proposed system leads to satisfactory results.
A Visual Analytics Technique to Compare the Performance of Predictive Models

Decisions that people make every day are affected by the information available in a given moment. Predictive models are used to estimate future values. For a given set of data and an analysis goal, the results of the models can vary, so it is important to select the most accurate model for the set of data. This paper proposes a Visual Analytics technique for comparing the performance of predictive models. It consists of four main components that support the tasks of the Keim’s Visual Analytics Mantra: “analyze first, show the important, zoom, filter and analyze further, details on demand”. The first component, analyze data, by building predictive models using various machine learning algorithms; the other three components are interactive visualizations that show the important results found by the models, zoom and filter on results of interest and finally, further analyze the selected results by showing details on the data.
Tradicionālā loģika – arhaisms vai fenomens ar attīstības iespējām

The aim of the article is to show the specifics of traditional logic. To a large extent, logic initially operates as Aristotle's logic. The traditional formal logic appears as a combination of individual elements, which are obtained from logical systems of various historical eras and directions. The author believes that the subject of logic is objectively linked to the category of the form, but the difficulty in understanding the form lies in the complexity of the form’s nature. The author claims that the content, which differs from the form, is not included in it. There is a functional difference between the form and the content. It is assumed that the types of logic appear as a function of a certain form, and diversity of logic is a consequence of the various manifestations of the form. Logic is interested in the correct form. Such assessment of the form includes an idea of the form as a specific condition. The correct form is a condition for the safety of information included in the statements or their sets. The author argues that logic can be characterized as a science of formal conditions and ways to achieve information security of certain parameters. Supposedly, traditional logic is a formal (form-based) science, which however is limited to certain substantial conditions, and that is one of the factors that forms the specifics of science. The author emphasizes that the tools offered by traditional logic allow to find solutions related to novelty.
Analysis of the use of software baits as a means of ensuring information security

this article analyses the usage of software baits as an information security asset. They provided close research about honeypot types, their advantages and disadvantages, possible security breaches, configuration and overall system effectiveness. Often, the entire electronic business of the organization is at stake, and even with the most reliable system of protection, a one-hundred-per cent guarantee of invulnerability of internal company data will not be given in principle.
LADRC-based Path Following Control for Cylindrical Drilling Platform Towing System

The towing process is the precondition to put a cylindrical drilling platform into use, which is saturated with risk due to the complexity of the towing environment, towing maneuvering, and the sudden and severity of accidents. Therefore, to control the cylindrical drilling platform towing system safely following a predefined course to reach the target see area becomes increasingly important. For environmental disturbances caused by wind and currents changing with time, a linear adaptive-disturbance-rejection-control (LADRC) based path following control method for cylindrical drilling platform towing system is proposed. Firstly, on the basis of both the mathematical modeling group model and the catenary model, three degrees of freedom nonlinear model of the cylindrical drilling platform towing system is built to obtain its real-time motion state. Then, a LADRC controller based on a two-dimensional trajectory tracking guidance law is designed for real-time path following control. Finally, simulation experiments of the path following control for the cylindrical drilling platform towing system is conducted. The results illustrate that the LADRC can effectively resist influences of the environmental disturbances and has a better path following performances than the traditional proportional–integral–derivative (PID) controller.
Score-based Causal Discovery from Heterogeneous Data

Causal discovery has witnessed significant progress over the past decades. Most algorithms in causal discovery consider a single domain with a fixed distribution. However, it is commonplace to encounter heterogeneous data (data from different domains with distribution shifts). Applying existing methods on such heterogeneous data may lead to spurious edges or incorrect directions in the learned graph. In this paper, we develop a novel score-based approach for causal discovery from heterogeneous data. Specifically, we propose a Multiple-Domain Score Search (MDSS) algorithm, which is guaranteed to find the correct graph skeleton asymptotically. Furthermore, benefiting from distribution shifts, MDSS enables the detection of more causal directions than previous algorithms designed for single domain data. The proposed MDSS can be readily incorporated into off-the-shelf search strategies, such as the greedy search and the policy-gradient-based search. Theoretical analyses and extensive experiments on both synthetic and real data demonstrate the efficacy of our method.
Opportunities and threats of distance education at the university for employment university graduates

Purpose: to analyze the opportunities offered by distance learning at the university and the threats that arise from the perspective of expert employers. Discussion: the modern period of study of university students is characterized by the forced use of the expansion of distation form. Currently, there is no accurate data on the potential and threats of this form of training. However, the duration of their ex-panded use was not yet known, and students needed to be prepared for both the real consequences of distance learning and how they were evaluated by employers. Results: the Analise of expert opinions of employers – representatives of the real sector of the economy showed that in general, the quantitative parameters of threats are rated higher than opportunities. The most significant opportunities: when gaining skills in remote mode; mastering computer literacy; lifelong educa-tion. The most significant threats are the lack of practical training and, accordingly, the acquisition of practical skills; reducing the emotional component of education; the negative effect of a long-term stay with the computer on the health of students.
3D Segment and Pickup Framework for Pancreas Segmentation

Locate and segment (LAS) framework is an effective method for segmenting pancreas from abdominal CT. Coarse-to-fine is the most widely used LAS framework which has achieved excellent pancreatic segmentation results collaborated with many network architectures. However, inaccurate location of the region of pancreas reduces performance of LAS methods. To solve these problems, we propose the segment and pickup (SAP) framework, which uses manual annotation to directly calculate the ROI of pancreas during training and trains a neural network to segment the pancreas in the ROI. In the testing process, we first use the well-trained segmentation network to segment the pancreas from the whole CT scan, then use the region growing method to pick up the final segmentation results from the noise. We used ResNet combined with the SAP framework to conduct experiments on the NIH data set, and achieved 86.96 DSC scores, proving that our SAP framework performs better than the regular LAS framework on pancreas segmentation.
Resilient Business Process Modeling and Execution Using BPMN and Microservices

Process Modeling Languages (PMLs) help to define, structure and organize operational workflows. The Business Process Model and Notation 2.0 (BPMN), one of the most prominent PMLs, allows the definition and execution of process models including distributed participants and systems. An increasing number of BPMN use cases take place in unreliable communication environments, where connectivity may be intermittent or broken. Resilient processes need to avoid failures that may result in process interruptions or complete breakdowns. Considering the particular requirements of unreliable communication environments, this paper addresses shortcomings when modeling and executing business processes. With resilient BPMN (rBPMN), the BPMN meta model is extended to allow resilient process designs by domain experts. Exemplary realizations of the introduced resilience strategies use state of the art technologies such as microservices and container virtualization. A proof-of-concept implementation illustrates the resilient design and execution of process models, serving as a guide for other use cases exposed to unreliable communication.
Enhancing Clustering Techniques with Hybrid Optimization

In today's creation, there is a demand to evaluate and withdraw intelligence from data. Clustering is a particular method that analytically affects the circulation of data into a class of exact objects. Each one class formed as a reflex is recognized as a cluster, which dwells of objects that have favor in the cluster and inequality with the objects in alternative troops. The present project intends to dig into and evacuate data clustering design using hybrid combination to provide optimization. One of the most known's algorithms is the k-means clustering algorithm which is zestfully enforced to innumerable disputes. Here the algorithm comes with benefits like high-speed and ease of employment, but it encounters the problem of local optimal. So, a hybrid combination of the Fuzzy c-Means Clustering Algorithm along with Particle Swarm Optimization can be used for optimization and better results. Fuzzy clustering is a common problem that leads to effective exploration in a few realistic practices. Fuzzy c-means (FCM) algorithm comes with merit like most used, productive, frank, and effortless performance. As known FCM is very susceptible to load, also it gets ambushed in native capital. To solve many breakthrough problems Particle swarm optimization (PSO) is the solution. In this paper, we have tried a combination of fuzzy clustering approaches. Fuzzy Particle Swarm Optimization can be combined so that we can try taking benefits from both these methods.
Lightweight solution to background noise in crowd counting

This paper proposed Dilated Compact Convolutional Neural Network (DCCNN) for single-image crowd density estimation from the original lightweight C-CNN. DCCNN is an enhancement of lightweight C-CNN compensated for lack of mechanisms to alleviate background noise using dilated convolution and average pooling. The performance of our proposed model improves significantly on medium and spared crowd scenes in ShanghaiTech part B dataset, achieving 18% lower MAE compared to C-CNN while requiring virtually no additional computational costs.
Development of 3D Imaging Technology for Hypsometric Map Learning Materials

We present a system that enables 3D imaging applications to help students easily understand the concept of a topographic map. The integration of emerging technologies into teaching is the trend of modern education development. Traditional teaching mostly uses paper materials and/or films to teach the topography and geomorphology. Most of the students acquire the geological knowledge from the textbooks, but the fact they learn about geological phenomena not as well as they seem to understand. Especially for the understanding of the concept of 2D-to-3D correspondence of Topographic map units, it is difficult for some students who have a weak sense of three-dimensional space or first time learn of the topographic map to understand the mapping relationship between the color of the map and the height of the terrain. Currently, popular sandbox experiments can simulate many geological features. And some manufacturers have developed Augmented Reality Sandbox teaching materials for students to easily understand new knowledge and concept of topography. Both sample sandbox experiment tools help the audience understand the formation process of geological phenomena and Topographic maps. However, Space limitation and popularity are the biggest problems in the promotion of teaching applications.
Using Canvas Commons to Transform Information Literacy Instruction

As more courses are offered online, many academic librarians need to determine how to teach information literacy in a virtual environment. Starting in the Summer of 2019, a team of five librarians embarked on transforming their instruction offerings through the use of Canvas Commons. The librarians wanted to deliver online information literacy content by creating downloadable library instructional modules based on curriculum mapping at the programmatic level. The need to reconsider these practices was further exacerbated by the impact of COVID-19 on higher education. When the university moved all courses online in the Spring of 2020 due to COVID-19, the team was able to quickly pivot and offer library instruction through Canvas Commons modules, replacing face-to-face instruction. In this chapter, the authors describe the implementation of information literacy modules on campus, lessons learned, and future plans for the project in light of the pandemic.
Eligibility determination ofQurbananimals using K-Nearest Neighbor

Abstract The Qurban is one of the most important worship services for Muslims. Choosing an eligible Qurban animals according to the shari’ah (law) of Islam and health is the main thing. This study aims to utilize technology in determining the eligibility of Qurban animals both based on the Islamic law and health. The K-Nearest Neighbor (KNN) algorithm is used to determine the eligibility of Qurban animals based on several input parameters, including animal type, weight, age, gender, sight, health, body condition, and mental. The experimental results show that KNN is successfully implemented and is able to help determine the eligibility of animals for Qurban well.
Isolation Forest Wrapper Approach for Feature Selection in Software Defect Prediction

Abstract Software defect prediction is one of the hot research topics in the software engineering application. The performance of predictor largely depends on the quality of dataset used for learning the predictor. High dimensionality is a noteworthy characteristic of software defect dataset, which has some side-effect on the predictor building using data mining or machine learning algorithm. Feature selection, being an effective measure of dimensionality reduction, uses the optimal feature subset to represent the entire feature space and alleviate the dimensionality curse problem. In this paper, a wrapper feature selection approach applying genetic algorithm as a search strategy to find the optimal feature subset is firstly introduced. Secondly, an improved isolation forest based defect prediction method is proposed. The exploring experiments on 5 real NASA software defect datasets demonstrate the proposed method can improve the defect prediction performance to some extent and proves the positive effect of feature selection in SDP application.
Unsupervised learning for monocular depth and motion with real scale

In Computer Vision, the method of using a single image to learn depth information through the CNNs has gradually improved, especially for the unsupervised learning methods which trains without the ground-truth are based on the image process and vision. However, the depth map generated from these methods are relative depth values instead of absolute depths, therefore these methods can't be well applied to practical applications.In this paper, we use the monocular video sequence as input, and get partial true depth value by using a prior conditions, so as to constrain the depth and the relative pose of the entire sequence with the "real" scale. Here we solve the previous scale ambiguous problem by fusing the geometry information, which correct the scale of the entire sequence by our rescaled module. This paper only uses monocular sequences to accomplish these tasks.Moreover, our method is specifically effective in the field of automated guided vehicles with only a camera as the requirement which reducing the dependence on other devices but improved the quality.