Reviewerly/exHarmony huggingface dataset:
A Baseline Statistical Method for Robust User-Assisted Multiple Segmentation

Recently, several image segmentation methods that welcome and leverage different types of user assistance have been developed. In these methods, the user inputs can be provided by drawing bounding boxes over image objects, drawing scribbles or planting seeds that help to differentiate between image boundaries or by interactively refining the missegmented image regions. Due to the variety in the types and the amounts of these inputs, relative assessment of different segmentation methods becomes difficult. As a possible solution, we propose a simple yet effective, statistical segmentation method that can handle and utilize different input types and amounts. The proposed method is based on robust hypothesis testing, specifically the DGL test, and can be implemented with time complexity that is linear in the number of pixels and quadratic in the number of image regions. Therefore, it is suitable to be used as a baseline method for quick benchmarking and assessing the relative performance improvements of different types of user-assisted segmentation algorithms. We provide a mathematical analysis on the operation of the proposed method, discuss its capabilities and limitations, provide design guidelines and present simulations that validate its operation.
Publishing in hijacked Web of Science journals: Analytical and legal study

This study aimed at a legal and scientific analysis of the plagiarized journals within the Web of Science system, in a time when the phenomenon of plagiarism and piracy spread to the titles of scientific and humanities magazines. Researchers sought to analyze the concept of plagiarism of international scientific journals based on its definition in scientific publishing, its dimensions and danger at the academic level of the researcher, and the academic and institutional level of the university. It also analyzed the types of plagiarism that include cloning, copying, substitution, mixing, and repetition as well as the scientific publishing of forged magazines that are no less dangerous and effective than the spoofed magazines. The study obtained several results, the most important of which is the risk for researchers to lose academic promotion and face direct penalties that hinder their path when appropriating funds through unaccredited scientific publications. The study has also reached specific criteria that the researcher must adhere to when seeking publication, the most important of which is the use of accredited websites of scientific journals and international publishers.
Optimal Value for Number of Clusters in a Dataset for Clustering Algorithm

It is essential to know the parameters required to clustering the dataset. One of the parameters is the number of clusters k and it is very important to select the k value to get deficient results on clustering. There are few algorithms to find the k value for k-means algorithm and it requires specifying a maximum value for k or a range of values for k as an input. This paper proposes a novel method Optimal cluster number estimation algorithm (OCNE) to find the optimal number of clusters without specifying the maximum or range of k values or knee point detection in the graph. In the experiment, this method is compared with the different existing methods with deficient real-world as well as synthetic datasets and provides good performance.
Expert System to Diagnose Eye Disease Due to Frequently Using Computer with Bayes Theorem Method

Eye disease due to frequent use of computers is one of the dangerous diseases in health because if not treated quickly it will result in blindness. These eye diseases can be diagnosed through the symptoms that arise in humans or through their clinical picture, through these symptoms an expert system can be made to make a diagnosis. An expert system is a system that seeks to adopt human knowledge to a computer that is built to solve problems like an expert. The expert system made in carrying out the diagnosis uses the Bayes theorem method to calculate the probability of an event occurring based on the influence obtained from the results of observations and experts. The system was built using PHP and MySQL programming as a database. The method used for tracing is Bayes' theorem. While the results of the diagnosis will inform about the results of the diagnosis containing a list of symptoms entered, information on the results of the rules regarding the eye disease suffered and information about possible treatments that can be carried out as well as treatment solutions.
Research on Psychological Problems and Countermeasures of Contemporary College Students Based on Data Analysis

Due to absence of good and high prevalence rate of the comprehensive method of automatic assessment, the current mental health assessment is still subject to manual answer questionnaires. The main drawback of this is that the detection of mental health issues of contemporary college students through the manual answer questionnaire is not satisfactory. Mental health problems of the students cannot be properly detected, and as a result, they remain deprived of timely psychological counseling and treatment. At the same time, in order to improve the accuracy of automatic selection of mental health and the popularity of this solution, this study first presents a detailed analysis of the possible mental health problems encountered by contemporary college students. A radial basis function (radial basis function, RBF) neural network application in the automatic selection of mental health status and comparative verification. The results proved the higher accuracy and warning of the RBF nervous system in comparison to the mental health of contemporary college students. The research further investigates the dependence of the psychological problems and solutions of contemporary college students on data analysis. It can be rightly said that the study is of great importance due to its provision of new research direction and relevant countermeasures for the psychological problems of college students.
Strong-separation Logic

Most automated verifiers for separation logic are based on the symbolic-heap fragment, which disallows both the magic-wand operator and the application of classical Boolean operators to spatial formulas. This is not surprising, as support for the magic wand quickly leads to undecidability, especially when combined with inductive predicates for reasoning about data structures. To circumvent these undecidability results, we propose assigning a more restrictive semantics to the separating conjunction. We argue that the resulting logic, strong-separation logic, can be used for symbolic execution and abductive reasoning just like “standard” separation logic, while remaining decidable even in the presence of both the magic wand and inductive predicates (we consider a list-segment predicate and a tree predicate)—a combination of features that leads to undecidability for the standard semantics.
Efficient and Robust Background Modeling with Dynamic Mode Decomposition

Abstract A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use.
Behavioral Information Security: Defining the Criterion Space

The success of information security appears to depend in part upon the effective behavior of the individuals involved in its use. Appropriate and constructive behavior by end users, system administrators, and others can enhance the effectiveness of information security while inappropriate and destructive behaviors can substantially inhibit its effectiveness. The present research focuses on “behavioral information security” which is defined as the complexes of human action that influence the availability, confidentiality, and integrity of information systems. Because research in this area is so new, in the present in study we focused on delineating and understanding the behavioral domain. Our goal for this study was to construct and test a taxonomy of information security behaviors. We expect that this knowledge can support later research efforts that focus on understanding the antecedents and consequences of information security behavior.
Differentiable Dynamic Wirings for Neural Networks

A standard practice of deploying deep neural networks is to apply the same architecture to all the input instances. However, a fixed architecture may not be suitable for different data with high diversity. To boost the model capacity, existing methods usually employ larger convolutional kernels or deeper network layers, which incurs prohibitive computational costs. In this paper, we address this issue by proposing Differentiable Dynamic Wirings (DDW), which learns the instance-aware connectivity that creates different wiring patterns for different instances. 1) Specifically, the network is initialized as a complete directed acyclic graph, where the nodes represent convolutional blocks and the edges represent the connection paths. 2) We generate edge weights by a learnable module, Router, and select the edges whose weights are larger than a threshold, to adjust the connectivity of the neural network structure. 3) Instead of using the same path of the network, DDW aggregates features dynamically in each node, which allows the network to have more representation power.To facilitate effective training, we further represent the network connectivity of each sample as an adjacency matrix. The matrix is updated to aggregate features in the forward pass, cached in the memory, and used for gradient computing in the backward pass. We validate the effectiveness of our approach with several mainstream architectures, including MobileNetV2, ResNet, ResNeXt, and RegNet. Extensive experiments are performed on ImageNet classification and COCO object detection, which demonstrates the effectiveness and generalization ability of our approach.
Satisfaction of Novosibirsk faculty with library and information support

Promoting the regional educational potential strengthening, including means of meeting the teaching staff information needs is one of the priority tasks, facing the largest Novosibirsk scientific libraries. This study purpose has been to identify the character of teachers’ information addresses to the sources of the State Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences (SPSTL SB RAS) and Novosibirsk State Regional Scientific Library (NSRSL).The analysis of teachers’ information needs, based on job responsibilities and knowledge requirements according to the Unified Qualification Directory of positions of managers, specialists and employees, has divided them into three categories: doctors of sciences, candidates of sciences and teachers without a scientific degree. The analysis of SPSTL SB RAS statistics has shown that among teachers who signed up for the library in the period 2016–2021, the share of candidates of sciences is 47,9%, doctors of sciences among teachers – users of the library is 9,6%. If considered by specialty, readers-teachers are mainly representatives of humanitarian (43%), technical (16%) and economic (11%) ones.The analysis of demands has shown that teachers’ information needs are satisfied with the help of book publications, among which textbooks especially stand out. About 30,5% of circulation are books published before 1991. Need for information in their subject area is one of the basic factors that bring teachers into libraries. The analysis of statistics has shown that candidates of sciences most often turn to electronic resources, and this category of teachers also has the highest demand for exhibitions, open funds, references, xerocopying and the Internet class services.In the whole, we can ascertain a comparatively high level of information needs satisfaction in the subject area. The average level of satisfaction is the need for information about teaching the subject methodology and information of a general pedagogical nature. Low level of satisfaction are needs for information related to the organization of the educational system.
Penggunaan Konsep User Experience Terhadap Layanan Situs Web Perpustakaan

Building a website should be designed based on the experience of the user, as the user who uses the web service. The basics of user experience are needed in designing a library website. The better the user experience in using a library website, the better the quality of a library's web service will be. Building a library web system requires knowledge of User Experience (UX), so that user satisfaction and convenience can be met. Web-oriented user experience can provide an impressive experience for users with features and content that is not confusing and easy to understand. With the steps in arranging UX into elements, we can better understand the overall task of building a website. According to Jesse James Garett, these elements are divided into five, namely: the strategy plane, the scope plane, the structure plane, the skeleton plane, and the surface plane.
Deep Learning Based Real-Time Facial Mask Detection and Crowd Monitoring

During the Covid pandemic, the importance of wearing mask has been noted globally. Additionally, crowded human clusters facilitated the transmission of the virus, which brings up the need for new systems for monitoring such situations. To address such issues, this research proposes an object recognition visual system based on deep learning to monitor the mask-wearing in a certain space and the control of the number of people indoors as an important tool during an epidemic. This research mainly investigates two types of identification. The first is to monitor whether people entering the site wear a mask at the entrance and exit of the field, and the second is to count the number of people entering a specific area. Experimental results show that by utilising the visual sensor, it is possible to detect and identify the people who frequently enter and exit in real-time. An advanced transfer learning approach has been employed to achieve the best discrimination performance. The actual training results prove that the migration learning Mask R-CNN algorithm produced by this method and the original Mask R-CNN algorithm have increased the mAP by 3 %, reaching a mAP of 96 %. In addition, the accuracy of the random sampling and identification in actual scenes has reached 92.1 %. The developed deep learning vision system has an enhanced identification ability for the verification and analysis of actual scenes and has a great application potential.
Accurate Matching of Invariant Features Derived from Irregular Curves

High-quality feature matching is a critical prerequisite in a wide range of applications. Most contemporary methods concentrate on detecting keypoints or line features for matching, which have achieved adequate results. However, in some low-texture environments where these features are both lacking, previously used approaches may result in an insufficient number of matches. Besides, in repeated-texture environments, feature matching is also a challenging task. As a matter of fact, there exist numerous irregular curves that can be detected in all kinds of images, including low-texture and repeated-texture scenes, which inspires us to move a step further and dig into the research of curves. In this paper, we propose an accurate method to match invariant features from irregular curves. Our method consists of two stages, the first of which is to match the curves as accurately as possible by an elaborate three-step matching strategy. The second is to extract the matching features with the presented self-adaptive curve fitting approach. Experiments have shown that the matching performances of our features in ordinary scenes are comparable to previous keypoints. Particularly, our features can outperform the keypoints in low-texture and repeated-texture scenes.
Artificial Intelligence in the View of Philosophical Methodology: an Educational Track

Introduction . The problematic of artificial intelligence (AI) is one of the central issues for modern scientific and business communities. Despite the relevance and particular demand for AI professions, there is currently an acute need in the labor market for both highly qualified and young specialists in this area. According to analysts, the shortage is due to shortcomings in the system for training AI specialists. The reasons that determined the crisis of educational programs in the field of IT are considered. The main emphasis is focused on the new approaches of gaining knowledge in the training of IT specialists, appropriate to the specifics of the new realities. Methodology and sources . The methodology of comparative analysis, interdisciplinary and philosophical approaches to the consideration of AI as an information and communication phenomenon of the new reality is used. Sources are based on the special literature of foreign (K. Schwab, N. Davis, P. Dougherty, J. Wilson and etc.) and Russian (E.M. Proydakov, V.B. Tarasov, P.M. Gotovtsev and others) authors, scientific researches (collective research of Russian scientists “Strong Artificial Intelligence”, 2021; and others), publications and websites devoted to the problems of AI, including the educational sphere (O.E. Baksansky, U. Pakhomov, etc.; www.trends.rbc.ru/trends/education and others). Results and discussion . Main tendencies of artificial intelligence development are determined. AI is opening out both as a special field of scientific knowledge and as a huge AI-industry. The specifics of educational track in training young AI specialists are revealed; the question of the necessity of developing new educational model, implementing the needs of digital economy in terms of training highly qualified personnel in the field of IT is raised. Conclusion . In the present conditions, when the volume of information is constantly increasing and the practice of AI development is ahead of the theory, the training of AI specialists has still a narrow profile. In this connection, we consider the latest modification of STEM education – i-STEAM, which, like STEM, is aimed at ensuring end-to-end interaction between applied tasks, fundamental research and educational system, but, unlike STEM, includes humanitarian and innovative components, which is especially relevant for training young AI specialists. However, as applied to the AI direction, the humanitarian component must be necessarily supplemented by the subject field of digital humanistic with the main emphasis on digital applied ethics as a prerequisite for progressive development of AI. It is focused that this approach of the training of AI profile meets the requirements of the human-centered digital era.
Chaos Adaptive Particle Swarm for Physical Exercise Health Assessment

Particle crowd algorithmic rule is a mayor examination hotspot in the authentic optimization algorithmic rule respond. Based on the PSO algorithmic rule to make optimal the RBFNN example, an amended order of nonlinear adaptable laziness power supported on the contest of population variegation is intended to extend the fixedness of population unlikeness performance and hunt capabilities to preclude the algorithmic rule from dripping into a topical extreme point prematurely, thereby further improving the prophecy correctness. Simulation experience shows that the amended PSO-RBFNN standard has open advantageous in the fixedness and sharp convergency of the prognosis proceed. In fashion to reprove the justness of reverse kinematics of robots with composite make and supercilious degrees of liberty, an amended adaptative suffix abound optimization (IAPSO) is spoken. First, the motoric equality of the 6-DOF strength-example avaricious robot design is established by the amended DH (Denavit-Hartenberg) argument course; second, on the base of the existent morsel abound algorithmic rule, the population Manhattan ceremoniousness is interested to lead the maneuver condition of the population in aqiqiy measure. And bound the adaptative lore substitute accordingly to the dissimilar maneuver possession and then adopt distinct site and hurry update modes; lastly, the fitness province with handicap substitute is present to trial the honest-prick and extended course transposition of the robot mold, and the delusion is not joint product major than 0.005 rad. The feint inference shows that the established kinematics shape is chasten, and the amended algorithmic program captures into recital the nicety, uniqueness, and velocity of the inverted resolution of the existent PSO algorithmic program, as well as higher deliverance truths. We conduct an experiment on the Brazilian jiu-jitsu. The results have clearly shown the advantage of our method.
The Concept of Design Competence Formation in Future Bachelors of Applied Computer Science in the Digital Educational Environment of the Higher Education Institution

The study aims to develop the concept of forming design competence in future bachelors of applied computer science in the digital educational environment of higher education institutions. The article presents the content, purpose, formation principles, types, forms, and structure of design competence formation in future bachelors of applied computer science in the digital educational environment of higher education institutions. The scientific novelty of the research lies in providing insight into aspects of design competence of future bachelors of applied computer science, which consists of intellectual and creative abilities, informational and analytical competence, and economic and social literacy of future bachelors of applied computer science. It is expressed in value attitude to work with modern computer technologies, studying modern programming languages, coding, designing and practical use of professional activity results. As a result, it has been shown that the concept of forming design competence in future bachelors of applied computer science in the digital educational environment of the higher education institution is necessary to improve the training of programmers who can perform tasks using modern digital technologies and design new digital products following the social demand of modern society.