Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network for Implementing a 2D Median Filter

While traditional neural networks could not solve the “exclusive or” (XOR) problem, it is known that current ones took the place of the universal Turing machine with the same level of computational capability. However, there was not enough vigorous research on how to construct neural networks to solve specific computational problems. Median filter is a nonlinear local signal processing technique that reduces noise from the original signal. It conducts more complicated operations compared to solving an XOR problem. In this paper, we developed a new neural network that performs 2D median filtering and conducted experiments by applying it to a variety of image data sets. Experimental results showed that the proposed XOR neural network could conduct median filtering with more than 99% accuracy. We observed that the accuracy is improved as the sizes of input images increase and also found that the accuracy was better for artificially created random images than natural images.
The Design of Information Security Risk Management: A Case Study Human Resources Information System at XYZ University

Human Resource Information System (HRIS) as one of the vital information systems at XYZ University, is expected to provide support in accelerating the achievement of XYZ University's strategic goals as a healthy university based on Good University Governance (GUG) through accelerating the provision of relevant, timely, and quality information. However, the management of HRIS is still not in compliance with information security standards, as can be seen from several incidents and the management of these incidents, which are still accidental. The purpose of this study was to obtain a design for HRIS information security risk management. This study uses a qualitative method where data collection is done by interview, observation, and literature review. ISO/IEC 27005:2018 is used as an information security risk assessment, while risk control recommendation uses SNI ISO/IEC 27001:2013. This study resulted in 40 of these risks: 12 High risks, 19 Moderate risks, six Low risks, and three Very Low risks. The results of this study are the design of HRIS's information security risk management at XYZ University.
Methods of Organization at the School of Additional Profi le Education of Students

The article deals with the actual problem of organizing additional specialized education of students in a general educational organization, which is an urgent problem. The purpose of organizing additional specialized education is not only the development of intellectual and creative abilities, subject, meta-subject and personal results of students, but also their vocational guidance and assistance in professional self-determination. The importance of vocational guidance work is emphasized in many federal state documents, in particular in the Federal Law "On Education in the Russian Federation" dated December 29, 2012 N 273-FZ, Federal State Educational Standards of General Education, the Strategy for the Development of Education in the Russian Federation for the Period up to 2025, An exemplary education program, including the module "Career guidance", etc., as well as in the speeches of the President of the Russian Federation Vladimir Putin. This article will allow the general education organization to build a system of additional specialized education for students, taking into account the peculiarities of the educational environment of the school. The organization of additional specialized education is a certain difficulty, however, the experience of such work is in one of the schools in the Moscow region. The article has a practice-oriented character and will be of interest to pedagogical and leading employees of general educational organizations, since it describes the goals, objectives and methods of organizing additional education for schoolchildren in the educational environment of a general educational organization.
Determination of Sentence Similarity Level Using Vector Space Model (VSM) and Word Relationship Weighting for Plagiarism Detection for Indonesian Documents

Plagiarism is an essential issue in writing for both articles and scientific works. One example is the error of taking a reference in a paragraph on someone else's scientific work. Suppose the manual checking process is carried out to determine plagiarism actions. In that case, it will be difficult and lead to the big problems. A computerized system is considered capable of quickly finding similarities in writing articles or scientific works that lead to plagiarism. In this study, detection of similarity in the document that leads to plagiarism is carried out by building an inverted index for each sentence in document in the database by performing data preprocessing then testing one by one for each sentence in the test document using the VSM method with term-frequency (TF) and inverse document frequency (IDF). The results obtained are that VSM with TF-IDF weighting can calculate sentence similarity with a high similarity value of 1 (100%). However, VSM with this weighting does not consider the word's position, then using the proposal of a 1-gram word weighting technique with a comparison process from left-to-right (left-to-right) and right-to-left (right-to-left) between database sentences and sentences. The test shows that there are differences in the similarity results. In the sentence examination time on testing, it is also found that the more the number of examinations, the more time is needed in the process of checking the similarity between sentences.
Feature Extraction and Analysis of Microseismic Signal Based on Convolutional Neural Network

Abstract Seismic detection technology has been widely used in safety detection of engineering construction abroad. Although it has just started in the field of engineering in our country, its role is becoming more and more important. Through computer technology, micro-seismic detection can provide accurate data for the construction safety detection of large-scale projects, which has important practical significance for the rapid and effective identification of micro-seismic signals. Based on this, the purpose of this article is to study the feature extraction and classification of microseismic signals based on neural games. This article first summarizes the development status of microseismic monitoring technology. Using traditional convolutional neural networks for analysis, a multi-scale feature fusion network is proposed on the basis of convolutional neural networks and big data, the multi-scale feature fusion network is used to research and analyze microseismic feature extraction and classification. This article systematically explains The principle of microseismic signal acquisition and the construction of multi-scale feature fusion network. And use big data, comparative analysis method, observation method and other research methods to study the theme of this article. Experimental research shows that the db7 wavelet base has little effect on the Megatron signal.
DATA BALANCING METHODS BY FUZZY ROUGH SETS

The robustness of rough sets theory in data cleansing have been proved in many studies. Recently, fuzzy rough set also make a deal with imbalanced data by two approaches. The first is a combination of fuzzy rough instance selection and balancing methods. The second tries to use different criteria to clean majorities and minorities classes of imbalanced data. This work is an extension of the second method which was presented in [16]. The paper depicts complete study about the second method with some proposed algorithms. It focuses mainly on binary classification with kNN and SVM for imbalanced data. Experiments and comparisons among related methods will confirm pros and coin of each method with respect to performance accuracy and time consumption.
Participant Vocabulary Use and Selection Differences in Serial Versus Parallel Presentation of Product Reaction Cards

This study looks at the effects on users’ choices when presented with Product Reaction Cards (PRC) in parallel and sequential order. Participants were given brief tasks on two websites and presented with PRC in parallel or sequential order to describe their sentiments. We found that participants selected 25% more words in the sequential condition, including the selection of more positive words (23%). However, the sequential condition took on average 5 minutes longer to complete word selection. Therefore, it is important to understand that the PRC presentation modality can affect the quantity and the choice of vocabulary used by participants.
Fracture Type Identification Using Extra Tree Classifier

Bones are used to construct the human body. Every movement of the body necessitates the support of both small and long bones. When a human body is subjected to significant weight or a potentially dangerous incident, this bone structure may provide numerous challenges. In this urgent situation, a timely and appropriate remedy is always recommended. There are many techniques which are available in medical world to solve it. In the advanced medical era, mostly use the digital way to resolve the fracture location that includes some manual support from human that may not be accurate sometimes. So, in order to overcome all of the issues that arise during the determination of fracture type and the subsequent process of identifying the location or locations, a digital system is required that not only assists the medical supervisor but also provides an accurate solution so that the medical supervisor can decide on further treatment for resolving fracture issues in the human body and return the situation to normal.
Emergency Virtual Machine Online Migration in Cloud Data Centers

Service provided by virtual machine hosted at cloud data center (DC) has become the mainstream mode. However, cloud data centers are facing the threat from natural disasters. For disaster-affected data centers, to ensure the sustainability of services, virtual machine hosted at such data centers should be migrated quickly to safe data centers. This paper investigates an emergency virtual machine online migration scheme where the migration operation is carried out within the early warning of disaster. In the proposed scheme, a virtual machine can be migrated through multiple paths and the migration data center need to meet the required bandwidth and delay of connection requests supported by the virtual machine. An Integer Linear Program (ILP) model is established to get the solutions for the proposed scheme. The objective of the ILP model is to maximize the number of virtual machines successfully migrated within the given early warning time and the constraints are the available network resources. Numerical results show that the proposed model can effectively implement emergency virtual machine online migration.
Hole Position Detection Technology Of Thin-Walled Parts Based On Binocular Vision

Aiming at the problem that the Hole position detection measurement of thin-wall part in traditional manufacturing is difficult and the accuracy is limited by manufacturing errors of fixtures and thin-wall parts, a method of hole position detection of thin-wall parts based on binocular vision was proposed. The detection system used the binocular cameras which installed at the end of the motion actuator to capture the image information of the target part and obtained disparity map by stereo vision matching. Through the digital image processing technology, the system got the target hole contours and its center position. The experimental results show that the measurement method is reasonable and effective, and the measurement accuracy meets the requirement.
Weather Forecast Semiotics: Public Interpretation of Common Weather Icons

Abstract Weather icons are some of the most frequently used visual tools that meteorologists employ to communicate weather information. Previous research has shown a tendency for the public to make inferences about weather forecast information on the basis of the icon shown. For example, people may infer a higher likelihood of precipitation, assume a higher intensity of precipitation, or determine the duration of expected precipitation if the weather icon appears to show heavy rain. It is unknown to what extent these inferences align with what the meteorologist who chose the icon intended to convey. However, previous studies have used simulated weather icons rather than ones currently in use. The goal of our study was to explore how members of the public interpret actual weather icons they see on television or in mobile applications. An online survey distributed by broadcast meteorologists through social media was used to collect 6253 responses between August and September of 2020. Eleven weather icons currently used by broadcast meteorologists were included in the study. We also tested eight common weather phrases and asked people whether they thought the icons were good illustrators of those phrases. In addition, people were asked to assign a probability of precipitation to the icons. The findings of our study offer new and unique insights that will improve the communication of weather information by giving meteorologists information about how their audiences interpret weather icons. Significance Statement Millions of people are shown weather icons during daily weather broadcasts. This study used two approaches to determine whether these icons are effective elements of weather messaging. For the first approach, we showed people an icon alongside a common weather phrase and had them tell us whether the icon was a good illustrator of the weather phrase. The second approach involved showing people an icon and having them assign a probability of precipitation to it. Across eight weather phrases, none of the icons were thought to be good illustrators, but bad illustrators were clear. These results can be used to improve how icons are used as tools to communicate weather forecasts.
Development of skills to work with digital sound in the school informatics course

The development of digital competencies of students is currently one of the main pedagogical tasks. The solution of this task is possible by paying attention to issues related to the skills of students to create their own digital content, for example, multimedia, combining various types of information (text, graphics, video and audio). The article provides a brief analysis of the content of teaching issues related to the study of digital sound processing technologies in a school informatics course. Taking into account the relevance of this issue, a thematic module "Digital Sound" is proposed, the content of which does not require binding to specific software. When studying the module, the emphasis is on mastering specific technologies for processing sound information. The module can be implemented as an optional course for students in 7–9th grades and/or as an elective course for students in 10–11th grades. At the same time, particular blocks of the module can be used at various stages of teaching informatics at school
An Efficient Anomalous Action Recognition Model Based on Out-of-Distribution Detection

Detecting anomalous data is very important for the security issues of machine learning. Misjudging anomalous data as normal data may cause serious consequences. For supervised machine learning methods, detecting anomalous data is a big challenge, because anomalous data may be very diverse and it is difficult to collect all possible anomalous data. In recent years, action recognition has been widely used in surveillance systems and home care systems. The recognition of anomalous actions has also become an important requirement of the action recognition system. In this paper, we apply the method that has successfully detected anomalous data on 2D images to identify anomalous actions in videos. The proposed approach can directly identify anomalous actions as long as we train on normal action data. The experimental results show that the proposed approach has achieved significant improvements on anomalous action recognition.
Autonomous identification of high-contact surfaces from convolutional neural networks

Abstract The rapid spread of the SARS-CoV-2 virus has highlighted many social interaction problems that favor the spread of disease, particularly airborne spread, which can be addressed by adjusting existing systems. Of particular interest are places where large numbers of people interact, as they become a focus for the spread of these diseases. This paper proposes and evaluates an autonomous identification scheme for certain surfaces considered high risk due to their continuous handling. These high-contact surfaces can be identified by an autonomous system to apply specific cleaning tasks to them. We evaluate three convolutional models from a proprietary dataset with a total of 2000 images ranging from wall switches to water dispensers. The objective is to identify the ideal architecture for the system. The ResNet (Residual Neural Network), DenseNet (Dense Convolutional Network), and NASNet (Neural Architecture Search Network) models were selected due to their high performance reported in the literature. The models are evaluated with specialized metrics in non-binary classification problems, and the best scheme is selected for prototype development.
Advanced Encryption Standard Mobile Application to Improve College Entrance Security in UNIMAS

Rafflesia College is one of the colleges in UNIMAS located outside of campus in Desa Ilmu, Kota Samarahan. Currently, the college does not have a clear log for visitors who enter and leave the college. There is also the chance that the visitor will enter the college using a fake identity. This paper implements improved entrance security with the Advanced Encryption Standard (AES) algorithm. In addition, this solution also ensures the confidentiality of the visitors&#x2019; data. This system has three users which are administrators, students, and security officers. This enhanced entrance security allows the students to put in a request for the visitor by giving the visitor&#x2019;s to the security officer for approval. This proposed system is an Android-based application developed using the Waterfall methodology. This application also tracks the entrance and leave time of the visitors. A questionnaire was conducted during the process and about 95% of the respondents find the application improves the security of the entrance.
Comparative Analysis of Genetic Algorithm and XML Filtering Technique for Multi-Tenant SaaS Configuration Management

The use of Software-as-a-Service (SaaS) cloud model is increasing rapidly. Among different SaaS deployment models, Single Instance Multi-Tenant (SIMT) has multiple advantages, e.g., high scalability and lower cost, but it also has practical concerns for management, as tenants may have distinct requirements or even conflicting preferences, and the number of features and tenants may be large. Offering the best user satisfaction, dealing with tenants' preferences and operational environment changes is crucial but challenging for effective SaaS management. Genetic algorithms (GAs) have been applied to the optimization problem in many fields. Further a GA-based approach, GAFES, specifically adapted to constraint-based feature selection optimization in Software Product Line (SPL) for multi-tenant SaaS has been reported. On the other hand, an XML filter technique, Yfilter, has been applied to various problem domains and recently to SIMT for SaaS. The aim of this paper is to experimentally evaluate the performance of GAFES and Yfilter for SaaS configuration with recourse constraints. We have conducted experiments to evaluate the two very different multi-tenant SaaS configuration techniques. The results show that both approaches can meet the user satisfaction, but Yfilter has much higher performance compared to GAFES.
Models and Filtering of Cylindrical Images with Harmonic Components of the Covariance Function

In a number of practical situations, images defined on cylinders are analyzed, for example, a pipeline, a part of rotation, a blood vessel, etc. Such images are conveniently represented by autoregressive models. Sometimes these images and their covariance functions (CF) have harmonic components. In this paper, we propose autoregressive models for representing such images and pseudo-gradient algorithms for their filtering.
A Dictionary-Based Method for Classification with Universum Data

In fact, the collected examples included the third-class examples, they do not belong to positive samples or negative samples, which are referred as the Universum data. And Universum data can make better performance for the classifier. In this paper, a dictionary-based method for classification with Universum data is proposed to construct a unified model. In the proposed method, we embed the dictionary and Universum data to construct a unified framework, and the Universum data is introduced into the framework by the ɛ-insensitive loss. For the optimization, the SVD algorithm and gradient-based optimization methods are utilized to alternately optimize and update the dictionary, and the Lagrangian function is used to iteratively optimize the unified framework to obtain the classifier. Finally, extensive experiments are conducted on the benchmark datasets to evaluate the performance of the proposed U-DL method and baselines. The results have shown that the proposed U-DL method makes better performance than previous methods.
Concept Formation

In this Chapter, it is shown that variational induction can also be employed for concept development. To this purpose, concept formation in phenomenological and theoretical science is distinguished. The differences between phenomenological and theoretical concepts largely parallel those between phenomenological and theoretical laws as discussed in previous chapters. In particular, phenomenological concepts are local and are mostly used for prediction and manipulation. By contrast, theoretical concepts are universal and mostly figure in grand explanatory schemes. In phenomenological science, both definitions and empirical laws can be formulated in terms of inus complexes of circumstances relative to a constant background. In the case of definitions, necessity and sufficiency have a normative character, in the case of phenomenological laws, they have an empirical character. Since variational induction does not depend in essential ways on this character of necessity and sufficiency, it can be employed both for the inference of empirical laws and for concept formation in phenomenological science. In data science, many machine learning algorithms relying on variational induction are indeed used for concept development.
Class Balanced Sampling for the Training in GANs

Recently Top-k fake sample selection has been introduced to provide better gradients for training Generative Adversarial Networks. Since the method does not guarantee class balance of selected samples in class conditional GANs, certain classes can be completely ignored in the training. In this work, we propose class standardized critic score based sample selection which enables class balanced sample selection. Our method achieves improved FID score and Intra-FID score compared to prior Top-k selection.
Understanding the Importance of Data/Application Security

“Secure your data and applications,” “identify security vulnerabilities,” and “introduce security metrics” are few of the most common statements you will hear in any type of organization, and those statements always highlight the importance of security.