Reviewerly/exHarmony huggingface dataset:
Formation of Inclusive Competence Future of Social Workers

The successiveness of work in inclusive environment depends from the number of factors and from the inclusive competence at first. Wide implementation of ideas of inclusion in modern society demands making changes in the process of preparation of future specialist, where the problem of formation of inclusive competence of applicants of higher education gets special meaning. The essence, problems and ways of formation of inclusive competence in future social workers as the increasing of quality of provision of social services in conditions of inclusive environment are described in this article. The problem of formation of inclusive competence of future social worker is integrated personality formation on the basis of inclusive knowledge, special inclusive skills and abilities, professional and personally important qualities, personal motivation, mastering technologies, which cause his readiness and ability to professional activity in conditions of inclusive environment, combining accumulated knowledge and skills with modern innovational approaches. The solution of mentioned problem is in implementation of a number of measures in IHE. They are: first-rate direction to the proper system of preparation to work in the inclusive environment, adjustment of educational plans and introduction of disciplines with inclusive component, making new accents in organization of extra-curricular activity, implementation of interactive methods of teaching and internship, directing to work of students’ centers together with youth with special needs, involvement to volunteers work with people with invalidity.
Case Technology as an Effective Means of Building Professional Competence of Future Teachers of Humanities

The article considers case technology as an effective tool for implementing a competent approach in higher education, which contributes to the development of the students’ personality, activates them towards self-determination, self-education and the formation of professional competencies. Based on the content analysis of the key concept of the research, scientific views are systematized to determine the essence of case technology. The main aspects of use of pedagogical cases are revealed, which are based on situational approach and activate intellectual and creative students’ activity. The problem of using case technology in the process of forming professional competence among future teachers of humanitarian specialties is described. The technological features of the case method are determined in terms of the relationship with other pedagogical technologies. It focuses on the advantages of using case technology in the process of developing professional competence among future teachers of humanitarian specialties. The practical experience of using case technology and the main stages of working with the case are highlighted. Thus it is considered that case-technology plays fundamental role in the formation of both professional and personal qualities of high-quality and marketable specialists and makes them competitive at the labour market.
Butterfly Optimization Algorithm

The Butterfly Optimization Algorithm (BOA) is a recent addition in the family memetic algorithms. BOA is inspired by the natural foraging and mating process of butterflies. BOA has a very basic structure with few parameters. BOA is capable of performing global as well as local search during the process. This chapter presents the basic concepts of Optimization theory and detailed overview of BOA.
Concept Drift Adaptation in Intrusion Detection Systems Using Ensemble Learning

The primary challenge of intrusion detection systems (IDS) is to rapidly identify new attacks, learn from the adversary, and update the intrusion detection immediately. IDS operate in dynamic environments subjected to evolving data streams where data may come from different distributions. This is known as the problem of concept drift. Today's IDS though are equipped with deep learning algorithms most of the times fail to identify concept drift. This paper presents a technique to detect and adapt to concept drifts in streaming data with a large number of features often seen in IDS. The study modifies extreme gradient boosting (XGB) algorithm for adaptability of drifts and optimization for large datasets in IDS. The primary objective is to reduce the number of ‘false positives' and ‘false negatives' in the predictions. The method is tested on streaming data of smaller and larger sizes and compared against non-adaptive XGBoost and logistic regression.
Backpropagating the Unsupervised Error of Self-Organizing Maps to Deep Neural Networks

Previous research has shown the potential that Deep Neural Networks have in building representations that are useful not only for the task that the network was trained for but also for correlated tasks that take data from similar input distributions. For instance, recent works showed that representations built by a Convolutional Neural Network (CNN) are better than the state-of-art handcrafted features used for object classification and demonstrated that the representations learned by Google LeNet could be used for the task of Unsupervised Visual Object Recognition (UVOC), achieving about 75-90% of agreement with labels assigned by humans in an unseen dataset, when fed as input to a SOM-based clustering method. In this work, we propose an approach that combines SOM with Deep Learning in a synergic way to allow dealing with complex data structures, such as images and sound, by backpropagating the unsupervised error through layers of neurons.
Any Climb: A Wall Climbing Robot for Various Curvatures

The main objective of the proposed work is to develop a wall climbing robot, which has the ability to perform various tasks. This can be performed by controlling the 6 DC motors through the microcontroller where 4 motors are for the legs while 2 are for vertical and horizontal movements which will be controlled through the assembly program written in Embedded C. In any sloped vertical plane, aerodynamic techniques are utilised to cling the robot to the plane. Also, this robot intends to have manual and automatic communication model. Manually it is done through a mobile app to which the microcontroller is connected through an ESP8266 Wi-Fi module. The alternate way to control the model is through the program code where the path as well as time is preset. This paper covers both the electrical and mechanical aspects of the project.
OrBI – A Hands-free Virtual Reality User Interface for Mobile Devices

The purpose of this paper is to present OrBI, a framework for developing hands-free user interfaces for interacting with low-cost Virtual Reality systems through mobile devices. The proposed implementation was developed and used in various Virtual Reality apps. To assess OrBI’s precision and ease to use, we submit the framework to a quantitative and qualitative evaluation. The findings indicate that the proposed interface model is easy to use and have a suitable precision. The findings also showed that the framework might be challenging for older people to use and, in some cases, tiresome.
Initialization method of genetic algorithm based on improved clustering algorithm

In order to improve the search ability, the convergence performance and algorithm results of genetic algorithm, we propose a population initialization method of genetic algorithm based on improved k-means algorithm. Firstly, the initial individuals walking evenly in the decision space are created by uniform design method, and then the initial individuals are processed by local search such as step-by-step search. Secondly, through the similarity degree between the individuals, several main search spaces are aggregated. Based on the search space, the individuals needed by the final initial population are clustered by K-means method.
GECCO 2022#

tutorial Share on GECCO 2022#: embedding knowledge into optimization process Author: Amir H Gandomi University of Technology Sydney, Australia University of Technology Sydney, AustraliaView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 922–936https://doi.org/10.1145/3520304.3533641Published:19 July 2022Publication History 0citation44DownloadsMetricsTotal Citations0Total Downloads44Last 12 Months44Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Graybox optimization and next generation genetic algorithms

tutorial Share on Graybox optimization and next generation genetic algorithms Author: Darrell Whitley GECCO GECCOView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1171–1199https://doi.org/10.1145/3520304.3533639Published:19 July 2022Publication History 0citation34DownloadsMetricsTotal Citations0Total Downloads34Last 12 Months34Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A FRAMEWORK FOR APPROACHING CHATBOTS IN PROJECT-BASED LEARNING

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 8707-8714ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.2077Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
A SYSTEMATIC LITERATURE REVIEW OF LEARNING ASSESSMENT MATERIAL AUTHORING SYSTEMS

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 5334-5344ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1263Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
A MOBILE TREASURE HUNT GAME FOR LANGUAGE LEARNING

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 5029-5033ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1200Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
THE INTEGRATED PERSONALISED ADAPTIVE LEARNING SYSTEM IN THE EDUCATIONAL PROCESS TRAJECTORY

1 Kazan (Volga Region) Federal University (RUSSIAN FEDERATION)2 Almetyevsk State Oil Institute (RUSSIAN FEDERATION)3 Kazan State Power Engineering University (RUSSIAN FEDERATION)
ENHANCING KNOWLEDGE ACQUISITION IN CHEMICAL ENGINEERING STUDIES THROUGH “QUIZIZZ” PLATFORM CLASS COMPETITIONS

ENHANCING KNOWLEDGE ACQUISITION IN CHEMICAL ENGINEERING STUDIES THROUGH “QUIZIZZ” PLATFORM CLASS COMPETITIONS
MORE ACCESSIBLE E-LEARNING: ADAPTING E-LEARNING MATERIALS FOR STUDENTS WITH SPECIAL NEEDS

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 7490-7493ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1756Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
Acoustical impacts on hard disk drive dynamics

Acoustical output from air movers in computing systems can reach sufficiently high sound pressure levels and in frequency bands such that performance of Hard Disk Drives (HDD) is degraded. In this paper air mover acoustical characteristics and comparisons to standalone measurements and models relevant to the performance issues are explored. Sound pressure levels, frequency bands, directivity, and explicit source descriptions are considered for air mover sources, and duct transmission, statistical energy, acoustic diffusion, and measured transfer factors are considered for noise transmission along acoustic path(s) from air mover to HDD within computing systems.
AI assisted multi-objective cloud computing model for optimized task scheduling and enhanced QOS

In recent decades, cloud computing has gained popularity due to the extensive collection of autonomous systems with a flexible framework and diversified features. Various communities require a cloud computing paradigm to maximize their revenue due to its commercial reality. Scheduling of resources to the cloud consumers dramatically influences the cost-benefit of the service providers. Several kinds of research have already been made, focusing on task scheduling and resource utilization. Job shop scheduling is one of the strong NP-hard problem for the production of optimal scheduling strategies. Evolutionary algorithms such as genetic algorithm and tabu search have been emerged to perform optimal job scheduling in cloud computing environments, but that are confined to perform a single objective. Hence to meet the multiple objectives in cloud computing platforms, we proposed a novel artificial intelligence-based task scheduling strategy to facilitate minimum makespan, energy efficiency, reduced computational cost, and reliability. The proposed modified sheep flock heredity algorithm (MSFHA) facilitates the optimal task scheduling strategy by selecting the job schedules with the Longest job to the High-speed processor (LJHP), Smallest job to the High-speed processor (SJHP), and high-affinity values.
intelligent deep learning based product image recommendation and classification system

Recently, recommendation system (RS) has gained significant attention in several industries and business sectors. At the same time, image recommendation is also found helpful to determine the relevant objects that exist in the form of image. It is mainly based on the extraction of different features and utilize it to get recommended outcomes. The recently developed deep learning (DL) models can be applied to design effective product image recommendation and classification systems. In this aspect, this paper designs an intelligent deep learning enabled product image recommendation and classification (IDL-PIRC) system. The proposed IDL-PIRC technique aims to examine the input image to recommend and classify products based on the user query. In addition, the proposed IDL-PIRC technique involves Gaussian filtering (GF) based pre-processing to eradicate the existence of noise exist in it. Moreover, the fusion-based feature extraction technique uses the Grey-Level Run Length Matrix (GLRLM) and Residual Network (ResNet152) models. Furthermore, the kNN based ranking approach is employed for product recommendation and cascaded neural network (CNN) is utilized for product classification. A wide range of simulations take place and the results are inspected interms of different evaluation parameters.
On Adversarial Robustness of Semantic Segmentation Models for Automated Driving

Several research works have been proposed to evaluate the robustness of deep-learning-based semantic segmentation models for automated driving under adversarial attacks. However, the types of tested adversarial examples and evaluated segmentation models are limited in the previous empirical studies, which imposes the restrictions on the understanding of the robustness of semantic segmentation models. To alleviate these problems, we would promote the research on the robustness of semantic segmentation models systematically from two aspects of influence factors: internal factors of model structures and external factors of environmental perturbations at the data level. In this paper, we provide a comprehensive study using these typical models with different internal structures: Fully Convolutional Networks (FCN), Pyramid Scene Parsing Network (PSPNet), DeepLabV3+, and SegNet with different backbones. These models would be evaluated on the metrics of robustness under both white-box attacks and black-box attacks. Based on our experiment, we make qualitative and quantitative analyses of the robustness of diverse models under different influence factors. With more empirical study cases, our work gives inspiration to the robustness study of semantic segmentation for automated driving which is meaningful and beneficial to the safety of the intended functionality (SOTIF).
Efektivitas audiovisual berbantuan QR-Code dalam pembelajaran teks eksplanasi di SMKN 1 Simpang Rimba

This research was conducted on the lack of interest and enthusiasm of the students in the use of learning media that affect student learning outcomes. The goal is how far the effectiveness of the QR Code in understanding the explanatory text at SMK Negeri 1 Simpang Rimba, South Bangka. This learning is based on the theory of learning approach based on higher order thinking skills. This research method uses One Shot Case Study. The research data are in the form of pretest and posttest that understanding the explanatory text in formative test with high level questions and questionnaires. The results of the study are showing video footage with QR Code is more effective in understanding explanatory text and it can help students think critically.
Book recommendation system based on an optimized collaborative filtering algorithm

Collaborative filtering is widely applied in recommendation systems. The traditional method usually adopts the cosine similarity algorithm or Pearson algorithm, but a sparse rating matrix may lead to inaccurate recommendation results. The optimized algorithm adds penalty terms according to the number of score vector elements to reduce the impact of sparsity. More purchase behaviors are taken into account in the optimization algorithm, including user activity, product popularity, and the time cost of user preferences. Due to the validity of the data set, the top-k method is adopted to select k users with the highest similarity (1) as the recommendation basis. Compared with the traditional method, the numerical results have a lower root mean squared error, and the algorithm execution time is significantly shortened. The optimized collaborative filtering algorithm can effectively alleviate the impact of sparsity and consider more purchasing behaviors, thus improving the algorithm efficiency and rating reliability of the book recommendation system.
Application Research of Attention Mechanism in Machine Reading Comprehension

In recent years, machine reading comprehension has become one of the latest and most popular topics in natural language processing, attention mechanism has been widely used as an important method for extracting relevant information from articles in machine reading comprehension. This paper aims to summarize the development process of the attention mechanism and its application in machine reading comprehension. On the basis of the introduction of the derivation process of the attention mechanism, the network framework and the weight calculation method of the input data, it further introduces three kinds of attention-based Mechanisms for machine reading comprehension models. Finally, the future development trend of the attention mechanism in the field of machine reading comprehension is analyzed. The attention mechanism can help the model to extract important information and make the model make more accurate judgments. In the future, it will be more widely used in various tasks of machine reading comprehension.
Research on Informatization Management of Ward Based on Hadoop Platform

research-article Share on Research on Informatization Management of Ward Based on Hadoop Platform Author: Siqi Chen Zhuhai Campus of Zunyi Medical University, China Zhuhai Campus of Zunyi Medical University, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 216–220https://doi.org/10.1145/3544109.3544149Published:18 July 2022Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Study of Data Retrieval Optimization Techniques Based on User Interest Ontology

Keyword expansion is the core technique in data retrieval optimization technology that can effectively improve retrieval efficiency. In this paper, a keyword expansion optimization model based on user interest ontology is designed. First, user search logs are analyzed to identify user search intent; then a user interest ontology is constructed by combining the generic ontology WordNet; finally, the conceptual similarity model between the user interest ontology and user keywords is used to optimize keyword expansion. The method can effectively improve the information search accuracy and solve the problem of poor semantic and thematic properties.
Image steganography algorithm for blind detection based on optimal wavelet packet decomposition

research-article Image steganography algorithm for blind detection based on optimal wavelet packet decomposition Author: Fen Li Wuhan Qingchuan University, China Wuhan Qingchuan University, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 999–1003https://doi.org/10.1145/3544109.3544397Published:18 July 2022Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Combined Method of Image Enhancement Based on Self-adaptive Median Filtering and Homomorphic Filtering Algorithm

A combined method of image denoising and brightness correction based on the combination of self-adaptive median filtering and homomorphic filtering algorithm is proposed for the problems of uneven illumination and noise effects in the images of anomalous behavior study of rail traffic crowd. The combined method first uses a self-adaptive median filtering algorithm to achieve filtering and denoising processing in the null domain, and then uses a homomorphic filtering algorithm to correct the luminance of images with uneven illumination in the frequency domain, so as to improve the visual effect of the target image. The experiments show that the combined method of image enhancement can effectively achieve noise filtering of the image, making the target area of the image more even and clear, while preserving important information such as contours and edges, improving the visual effect and helping the subsequent image analysis, and providing a new image enhancement technique and method for pre-processing of image recognition in the study of anomalous behavior of rail traffic crowds.