Reviewerly/exHarmony huggingface dataset:
Peer review declaration

All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information: • Type of peer review: Single-blind Single-anonymous: authors' identities are known to the reviewers, reviewers' identities are hidden from authors Double-anonymous: author and reviewer identities are hidden to each other Triple-blind: author and reviewer identities are hidden to each other, and from the Editor(s) Open: author and reviewer identities are known to each other • Describe criteria used by Reviewers when accepting/declining papers. Was there the opportunity to resubmit articles after revisions? Some criteria when accepting/declining paper were including: (1). The depth of the research methods and results, (2). Whether the papers were written properly, (3). If the topics of the research matches our conference, (4). All the figures were correlated to the discussion. Time given to authors in resubmitting after revisions was approximately 1 month. • Conference submission management system: We use Easychair.org platform in managing our conference submission. Submissions were available for 2.5 months prior to conference date. We generally accepted all the papers submitted for presentation in the conference as long as they suit the theme. After conference, the review process was conducted by Reviewers. • Number of submissions received: 53 papers • Number of submissions sent for review: 53 papers • Number of submissions accepted: 43 • Acceptance Rate (Number of Submissions Accepted/Number of Submissions Received X 100): 85% • Average number of reviews per paper: 2 reviews • Total number of reviewers involved: 10 reviewers • Any additional info on review process (ie plagiarism check system): • Contact person for queries: Mohammad Rohmaneo Darminto rohmaneo@its.ac.id/geoicon@its.ac.id Laboratory of Geoinformatics Department of Geomatics Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Please submit this form along with the rest of your files on the submission date written in your publishing agreement. The information you provide will be published as part of your proceedings.
TIME SERIES OUTLIER ANALYSIS FOR MODEL, DATA AND HUMAN-INDUCED RISKS IN COVID-19 SYMPTOMS DETECTION

Information systems are important references aiming to support the decisions of decision-makers. Information reliability depends on the accuracy and efficacy of data and models. Therefore, some risks may emerge in information systems concerning models, data and humans. It is important to identify and extract outliers in decision support systems developed for the health information systems such as the detection system of Covid-19 symptoms. In this study, the risks that are important in decision making in Covid-19 symptom detection were determined by the statistical time series (ARMA) approach. Potential solutions are proposed in this way. Moreover, outliers are detected by software developed by using the Box-Jenkins model and reliability and accuracy of data is increased by using estimated data instead of outliers. In the implementation of this study, time-series-based data obtained from laboratory examinations of Covid-19 test devices can be used. With the method revealed here, outliers originating from healthcare workers or test apparatus can be detected and more accurate results can be obtained by replacing these outliers with estimated values.
New Model of Educational Information Service in the Era of Big Data

Big data (BD) provides new ideas for the construction of college education informatization. Educational information is an important part of national informatization and an important foundation and condition for the realization of an informatized society. The construction of Hubei Normal University’s education informatization refers to the use of modern information technology in school teaching, scientific research, office, and other activities to achieve the purpose of improving the school's operating conditions and the level of Hubei Normal University and improving the quality of school personnel training. This article takes teacher education under the new normal as the background, from the perspective of education managers, and puts forward corresponding suggestions. This article puts forward the point of view of reforming the education management (EM) information in colleges and universities (CU) and puts forward a new concept of building a teaching model based on information management. Including the following points: Strengthen the construction of education management informatization (EMI) standard system, the establishment of EMI mechanism, promote the effective integration of EM information system, improve the overall information quality of education managers, promote the construction of EM information. This article also points out the focus of management innovation, that is, reform and improves the traditional EM mechanism, build a management environment, and strengthen the information quality of education managers.
An Efficient Image Preprocessing Technique for Land Use Analysis by Using Remote Sensing Images

With the widespread availability of image manipulating software and with ease of use, anyone with basic knowledge can modify the image without leaving any traces in the image. People believe in whatever they see in an image, but it is not necessary that the content has to be real. With the wide use of social media people with malicious intent share images that are modified and make you believe that they are real and by doing so they can influence people’s opinion which is a grave danger for society.In copy paste attack a part of the image is copied and pasted on to a different region of the same image, this pasted region can be detected by using the block-based method, but when the copied region is rotated orthogonally or if it is flipped before pasting on to the destination, such pasted regions are impossible to detect with the block-based approach.In this paper we are proposing a new technique to detect rotated or flipped pasted regions in an image and the performance of this new technique is compared with the SVD (Singular Value Decomposition) method used for the same purpose. The proposed new technique overcomes the rounding off errors in singular values of SVD method and computationally efficient than SVD and also computes the angle of rotation
The Determination Method of Battlefield Monitoring Interval Period Based on the Complicatedness of Situation Changes

Commanders are aware of the battlefield situation (BS) by monitoring the battlefield periodically. When the monitoring interval period is too short, there will be a huge cognitive load, which seriously affects commanders’ situation awareness (SA). When the monitoring interval period is too long, there will be omission of important information, which also affects commanders’ SA. Therefore, it is necessary to set the reasonable interval period of monitoring battlefield. In this paper, BS signal is defined to represent BS changes, according to the characteristics of BS changes. Then Fourier transform (FT) is used to analyze frequency characteristics of BS signal in order to describe the complicatedness of BS changes. The discriminant of reasonable monitoring interval period (DRMIP) is derived, according to the commanders’ requirements accuracy of BS. When the complicatedness of BS changes cannot be obtained previously, the highest frequency iteration algorithm (HFIA) is proposed to obtain the best monitoring interval period according to iterative BS signal. In the case of setting the threshold of difference, the validity of proposed method was verified by simulation calculation method.
AUTOMATIC COLORIZATION USING CONVOLUTIONAL NEURAL NETWORKS﻿

Automatic Colorization helps to hallucinate what an input gray scale image would look like when colorized. Automatic coloring makes it look and feel better than Grayscale. One of the most important technologies used in Machine learning is Deep Learning. Deep learning is nothing but to train the computer with certain algorithms which imitates the working of the human brain. Some of the areas in which it is used are medical, Industrial Automation, Electronics etc. The main objective of this project is coloring Grayscale images. We have umbrellaed the concepts of convolutional neural networks along with the use of the Opencv library in Python to construct our desired model. A user interface has also been fabricated to get personalized inputs using PIL. The user had to give details about boundaries, what colors to put, etc. Colorization requires considerable user intervention and remains a tedious, time consuming, and expensive task. So, in this paper we try to build a model to colorize the grayscale images automatically by using some modern deep learning techniques. In colorization task, the model needs to find characteristics to map grayscale images with colored ones.
Criminal Detection: Study of Activation Functions and Optimizers

Face is the primary means of recognizing a person, transmitting information, communicating with others, and inferring people’s feelings, among others. Our faces will reveal more than we think. A facial image may show personal characteristics such as ethnicity, gender, age, fitness, emotion, psychology, and occupation. In addition to the recent specialisation of deep learning models, the exponential output and memory space growth of computer machines has greatly increased the role of images in recognising semantic patterns. Facial photographs can reveal those personality features in the same way as a textual message on social media reveals the author's individual characteristics. We investigate a new degree of image comprehension by using deep learning to infer a criminal proclivity from facial images. A convolutional neural network (CNN) deep learning model is used to differentiate between criminal and non-criminal facial images. Using tenfold cross-validation on a set of 5500 face pictures, the model's confusion matrix, training, and test accuracies are registered. In learning to achieve the highest test accuracy, CNN was more reliable than the SNN, which was 8% better than the SNN's test accuracy. Finally, CNN's dissection and visualization of convolutional layers showed that CNN distinguished the two sets of images based on the shape of the face, eyebrows, top of the eye, pupils, nostrils, and lips. In this project we focus on Activation functions and optimizers. Activation functions are of two types Saturated and Non-Saturated. Here we use non saturated activation functions like ReLU, SELU and SOFTMAX. When we combine ReLU and SOFTMAX, we get 99.3 percentages as test accuracy. By combining SELU and SOFTMAX we get 99.6 as test accuracy. Therefore, SELU and SOFTMAX combination give the better accuracy
Feature Engineering Based Credit Card Fraud Detection for Risk Minimization in E-Commerce

In today’s financial business, financial fraud is a rising concern with far-reaching repercussions, and data mining has a crucial role in identifying fraudulent transactions. However, fraud detection in a credit card can be challenging because of significant reasons, such as normal and fraudulent behaviours of the profiles change frequently, scarcity of fraudulent data, dataset being highly imbalanced, and so on. Besides, the efficiency of fraud identification in online transactions is greatly impacted by the dataset sampling method and feature selection. Our study investigates the performance of five popular machine learning approaches such as Logistic Regression (LR), Random Forest (RF), Support Vector Classifier (SVC), Gradient Boosting (GBC), and K-Nearest Neighbors (KNN) in terms of feature selection. Feature selection is done by Sequential Forward Selection in addition to extending the models’ performance by handing imbalanced data using Random Undersampling and feature scaling using PCA transformation & RobustScalar for both numerical and categorical data. Finally, the performance of different machine learning techniques is assessed based on accuracy, precision, recall, and F1-measure on a benchmark credit card dataset.
DEDUCTIVE-EMPIRICAL APPROACH TO THE STUDY OF MATHEMATICAL CYCLE DISCIPLINES IN NATURAL SCIENCE COURSE IN MATHCAD

Nowadays, the main trends in the reform of higher education naturally lead to a significant decrease of classes in the educational process, which negatively affects the study of the disciplines of the mathematical cycle due to their abstraction and often complexity of material presentation. Thus, there is a need to increase the effectiveness of contact work and to strengthen the role of independent study of students. One of the factors contributing to improving the effectiveness of contact work is the preliminary propedeutic independent study of the material when applying the empirical component of the deductive-empirical approach to teaching higher mathematics. The proposed new deductive-empirical approach to the organization of non-auditory independent work in the study of the disciplines of the mathematical cycle by students of natural sciences should include the following directions/stages: 1) providing historical information on the empirical approach to solve mathematical problems arising in solving natural science and technical problems, 2) experiments leading to the expediency of introducing new concepts and experiments illustrating definitions, 3) empirical justification of difficult-to-prove and not obviously feasible theorems, 4) application of experimental method to find numerical values of quantities, 5) use of the experimental method to illustrate and check the results of solving a problem, 6) monitoring the effectiveness of applying a deductive-empirical approach to teaching mathematics. To increase the efficiency of propedeutic independent study, it is necessary to use appropriate methodological support. And the last (depending on the section being studied), you can use interactive methodological guidelines integrated with applications written in various high-level languages, emulators of devices for conducting mathematical experiments, etc. Thus, students of natural sciences should study mathematical disciplines in the inextricable unity of the traditional deductive presentation of educational material with an empirical approach to learning with the wide use of interactive technologies.
Multi-Photonic Entanglement, A Geometric Approach

A new genuine tripartite entanglement measure is revealed, interpreted as the area of a so-called concurrence triangle. The new measure is found superior to previous attempts. Specific examples are illustrated.
Computation Acceleration

This chapter first focuses on model compression and hardware acceleration for edge learning. It covers many aspects, including the learning algorithms, learning-oriented communication, distributed machine learning with hardware adaptation, TEE-based privacy protection, algorithm, and hardware joint optimization, etc. The essential objective is to implement an integrated algorithm-hardware platform, to optimize the implementation of emerging machine learning algorithms, to fully explore the potential of modern computation hardware, and to promote novel intelligent applications for sophisticated services. Then, we introduce straggler tolerance schemes that can avoid the overall training performance seriously degraded by faulty nodes, and can adequately utilize the computation power of slow nodes. At last, we introduce computation acceleration technologies for inference at the edge.
Examination of cleaning tools for the development of a cleaning robot for grease in exhaust ducts

In this study, the authors started to develop a robot that can clean grease dust in ventilation ducts. In this paper, we investigated the required specifications of the robot, constructed a model of the cleaning brush, and developed grease dust samples. We also conducted cleaning experiments to confirm the cleaning performance of cleaning tools with different bristle and stiffnesses. Form the experiments, we confirmed that the contact area of the brushes affected on the cleaning performance and the cleaning performance of the nylon brush was 68%, which was the highest compared to other brushes.
Bayesian Feature Discovery for Predictive Maintenance

This paper considers predictive maintenance, which is the task of predicting rare and anomalous events (typically, system failures) using event logs data, which are series of time-stamped symbolic codes emitted at regular or irregular intervals by a monitored system. Our objective is to find small sets of codes (called itemsets or patterns) that occur shortly before failures. Current prediction methods either produce patterns at a high computational cost or resort to kernel approaches which are often difficult to interpret. We introduce Bayesian Pattern Feature Discovery (BPFD), a new generic algorithm for pattern discovery. Our method, based on a pattern mining technique, produces informative and explainable features and is computationally efficient. The performance of BPFD is highlighted on real-world data sets, showing that enriching the feature space with the discovered patterns improves significantly the prediction power of a broad range of predictors and offers useful insight on the predictive maintenance task.
Emotional Communication from Writers to Readers on a Message Including Emoji

Emojis are extensively used to convey emotional nuances non-verbally in computer-mediated communication. Previous studies have examined how emojis are used and how they change the expression of a sentence, as well as the subsequent impression of the reader. Regarding whether the writer’s emotion is understood correctly by readers, evidence shows that emojis can enhance specific emotions. However, the effects of variation in the kinds of sentence, emotion, and emoji on the shifting emotion conveyed have not been studied. In this study, we examined the various factors that affect the shift in emotions being communicated. Our results demonstrated that positive emotions can be communicated more correctly compared with negative emotions and that smiley emojis (a.k.a. emoticons) can communicate emotions more clearly compared with other kinds of emoji.
Optimization of activities for the education of personnel of the internal affairs bodies of the Russian Federation on the basis of a personality-oriented approach

The article deals with optimization of activities for the education of personnel of the internal affairs bodies of the Russian Federation. In the internal affairs bodies of the Russian Federation, special attention is traditionally paid to the education of employees. At the same time, the process of education is distinguished by its special content, forms and methods, and its main meaning is to improve the performance of personnel in solving professional problems. The purpose of this work is to consider the features of the implementation of a personality-oriented approach in the process of educating employees of the RF Internal Affairs Department. Subject of the research: features of the application of an individual, personality-oriented approach in the education of employees of the RF Internal Affairs Directorate. The object of the research is the directions of optimization of activities for the education of the personnel of the internal affairs bodies. The article discusses the results of an expert survey of managers and employees of various levels of educational work units (Moscow region, Smolensk region, Lipetsk region, Republic of Sakha). 36 people were respondents.&#x0D; It should be emphasized that a personality-oriented educational impact should have the ultimate goal of a comprehensive and holistic personal development of employees of internal affairs bodies, preparing them for better and more efficient performance of official and official duties.
Measuring the use of students of the Department of Information Studies for social networks ... Section page on Facebook, model

Social networking is one of the most recently used technologies because of its advantages, spread and interaction. It is one of the most prominent applications of the second generation Web 2.0, which has effectively imposed itself on the users of the internet. Facebook network comes second after search engine at the global level, "Google". It is noted that they are highly efficient in providing information services and representation of information institutions and facilities in the virtual world. it is agreed that the progress of the societies is measured according to their ability to free and fast access to information and to use it to generate knowledge that reaches wisdom, progress and excellence. At this point, the importance of studies of information is shown in general and Webometrics in particular, which means that the set of statistical methods and measurements used to study the quantitative and qualitative aspects of information resources, structures, uses and techniques on the web, is found to be bibliometric studies designed to study and analyze reference citations, can be applied to the information resources available on the web such as the links of web pages and the use of those sites. Therefore, the study aims to monitor students' attitudes towards the use of social networking sites in general, and the Facebook page of the department of information studies of the Libyan Academy particularly. To identify the view of the students of the information management division and the management of the archive to their identify satisfaction with the information services provided by the page, and the extent of knowledge of the links of electronic sources of information, and the extent to meet their needs and scientific desires, and to what extent related to academic and research interests.
Part-Aware Spatial-Temporal Graph Convolutional Network for Group Activity Recognition

Group activity recognition, a challenging task that requires not only recognizing the individual actions of each person but also inferring relationships among persons, has received considerable attention. Previous methods infer coarse-level relations based on holistic features of individuals. This paper goes one step further beyond existing methods by learning part-aware spatial-temporal graph convolutional network (PSTGCN) to model fine-grained relations using part-level features. The PSTGCN includes two core graphs, the locally-connected graph, and the fully-connected graph. The locally-connected graph in which all parts of the same person are connected mines structural information of individuals. The fully-connected graph in which all parts of persons are connected extracts the latent relationships among individuals in part-level. Extensive experiments on two widely-used datasets: the Volleyball dataset and the Collective Activity dataset, are conducted to evaluate our PSTGCN, and the improved performance demonstrates the effectiveness of our approach. Visualizations of the part-based relation graph and the group-level features indicate our method can capture the discriminative information for group activity recognition.