Reviewerly/exHarmony huggingface dataset:
Research on management methods of automotive cybersecurity tools

With the in-depth integration of informatization and automobiles, automobiles are transforming from traditional means of transportation to a new type of intelligent travel carrier <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> . While it has brought great changes to the automobile industry, it has also increased cyber security threats. Automotive cyberecurity tools are an inevitable element introduced in the process of automotive cyberecurity transformation, and become one of the important factors affecting automotive cyberecurity. Therefore, the management of automotive cyberecurity tools as a necessary link in automotive cyberecurity assurance has been paid more and more attention by various enterprises.
Project Activities in the Context of the Implementation of State Standards of the New Generation

Federal state educational standards of higher education contain requirements that indicate the priority direction of the formation of the competencies of modern graduates. Competence-based educational paradigm necessitates the search and implementation of ways to promote the formation of a qualified specialist. The implementation of project activities in higher education is revealed as the leading direction of activity, ensuring the achievement of a high level of formation of competencies of students of higher educational institutions. The purpose of the article is to consider the project activities of students of higher education in the conditions of implementation of state standards of the new generation. The authors present the characteristics of the project activity, its typology, set the possibilities, the criteria for evaluating the implementation of project activities. As an example, presents a large-scale project of higher school of organization of independent work of students, which includes relevant conditions, educational landscape, allowing students to participate in various projects, thereby forming respective competences laid down in the standard. The project activity of students in the framework of this project develops at a higher level.
A novel DeepCNN model for denoising analysis of MRI brain tumour images

Medical images must be introduced to the specialists or doctors with high accuracy for the diagnosis of critical diseases like a brain tumour. In this paper, a novel DeepCNN model is proposed to perform MRI brain tumour image denoising task and the results are compared with pre-trained DnCNN, Gaussian, adaptive, bilateral and guided filters. It is found that DeepCNN performs better than other filtering methods used. Different noise levels ranging from 5 to 50 and noises like salt and pepper, Poisson, Gaussian, and speckle noises are used to form the noisy images. Performance metrics like peak signal to noise ratio and structural similarity index are calculated and compared across all filters and noises. The proposed DeepCNN model performs well for denoising with the unknown and known noise levels. It speeds up the training process and also improves the denoising performance because of using 17 convolutional layers and batch normalisation.
INNOVATION WITHIN COMPLEX HIGHER EDUCATION SYSTEMS

Universities around the globe are under complexity pressure. The growing international competition, the globalization of education, the budget cuts, and the Information and Communications Technology (ICT) revolution that is re-shaping all the aspects of university life are examples of distinct pressures that together bring complexity to higher education systems. At the same time, Open Educational Resources and Open Educational Practices are being considered as an option to face the need of increasing the number of students, the accessibility, and the equality of Higher Education in times of budget cuts. This paper explores some dimensions of the complexity of higher education systems, focusing on emerging technologies and practices of students and staff mobility enhanced by ICT. Building on this analysis, we conclude that ICT and Open Education are not only making teaching and learning in universities more efficient and accessible; they are changing the fundamentals of academic contexts.
Dream pedagogics

A number of urgent problems of education and upbringing are considered from the theory standpoint humanitarian technological revolution. Transition from industrial to post-industrial development phase, from the world of cars to the world of society sets a number of new pedagogical problems. Expansion of educational system, the formation of an open educational system takes place in the course of project work in an age-diverse team in the process of social and scientifc-technical creation, aimed at solving specifc problems of the village, city, Russia. Herewith, a fundamental role is played by the formation of their own images of the future by the students, concrete efforts to make your dreams come true. It is shown how these approaches were embodied during the implementation of the Russian project «My country - my Russia» and forming around it a community of experts, innovative teachers, motivated youth and their parents, focused on their personal, subjective participation.
Digitizing Medical Papyri in Question-and-Answer Format

This chapter presents a general overview of the Greek medical papyri in question-and-answer format, focusing in particular on their layout and on the graphical strategies deployed by the ancient scribes in highlighting the main articulations of the texts. Discussion will then move from the ancient sources to their modern digital representation, where the current strategies to encode such ancient layout and graphical devices will be presented and analysed, pinpointing their fundamental relevance in our comprehension of this peculiar textual typology.
Reports of Conferences, Institutes, and Seminars

This column covers sessions from the 2018 Charleston Library Conference, an informal annual gathering of librarians, publishers, electronic resource managers, consultants, and vendors of library materials in Charleston, South Carolina, in November. Topics include the impact of open access and open research, assisting faculty with locating and integrating these resources into their courses through partnerships and open applications, and managing gifts and donations with restricted resources.
Modified Artificial Bee Colony Algorithm with Multilevel Threshold Segmentation and Boundaries Evaluation for Shadow Detection

The appearance of shadows typically causes severe problems in pc vision. There are various methods have already put forward but scope in this field is open. In this article Shadow Detection and Removal Using Modified artificial bee colony (MABC) Algorithm with Multilevel Threshold segmentation is proposed. The proposed method uses three threshold and corresponding boundaries, associated curvature, edge response, gradient, and MABC algorithm. First data pre-processing is applied to find the correlation between the pixels then three threshold and corresponding boundaries evaluated to accurately differentiate pixels as foreground. The edge response, curvature, gradient are applied to find the boundaries. Finally, MABC has been applied for detecting the shadow. The results show improvement in comparison with other existing methods.
Recognizing Human Facial Expressions with Machine Learning

we develop a organized correlation of machine learning techniques connected to the issue of completely programmed acknowledgment of facial emotions. We investigate consequences on a progress of researches looking at acknowledgment engines, combining AdaBoost, support vector machines, linear discriminate analysis. We likewise investigated highlight choice strategies, including the utilization of AdaBoost for highlight choice before order through SVM or else LDA. Best outcomes are gotten through prefering a subset of Gabor conduit develop AdaBoost pursued through order with Support Vector Machines. The framework works continuously, within addition to got 93% right speculation novel matters intended for a 7-way compelled alternative going the Cohn-Kanade articulation information. The yields of the classier alteration easily an element of time and in this way can be utilized to gauge outward appearance elements. We connected the framework to fully automated recognition of facial activities (FACS). The current framework arranges 17 activity units, regardless of even those coming as one or else within combine with different activities, with a mean precision of 94.8%. The design fundamental consequences intended for applying this framework to facial emotions.
Sensor network design for smart building

This article has two major contributions. The first one deals with energy efficiency and pollution issues in buildings. Our solution is to develop, deploy and operate a sensor network for building monitoring. Regarding existing solutions, our network is based on generic and modular sensors that can collect several data such as temperature, carbon monoxide, etc. Subsequently, these data are forwarded to a base station that stores it in a database. By providing a human machine interface, these data can be visualised and processed in real time. The second contribution is related to a feedback from the development of our sensor network. We identified challenges related to this development including significant development time, energy constraints, large-scale network, costs, etc. To overcome these challenges, this article proposes also to explore new development methodology based on a model driven approach that reduces costs and efforts and ensures consistency of the development.
Historical formation of Russian children’s literature

In the context of the modern information society, it is difficult to overestimate the educational and cultural potential of children’s books. A carefully selected children’s book is always a strong effective means of mental education and the formation of artistic and aesthetic preferences and tastes of the child. Children’s literature can be used in the process of moral, economic, and environmental education. Children’s literature is created specifically for children (preschool, primary school and secondary school age) and includes the best classical, folk, poetic and prose works.
A Statistical Method for Evaluating Performance of Part of Speech Tagger for Gujarati

Part of Speech Tagging has continually been a difficult mission in the era of Natural Language Processing. This article offers POS tagging for Gujarati textual content the use of Hidden Markov Model. Using Gujarati text annotated corpus for training checking out statistics set are randomly separated. 80% accuracy is given by model. Error analysis in which the mismatches happened is likewise mentioned in element.
Frameworks for Animal and Human Detection using Camera Images to Detect Anomaly

Human and animal interacting events in video image frames will leads to anomalies and it cannot be predicted. These anomalous events was happened due to the interactions of human, animals and birds with each other. Some of the human and animal anamolous interactions will leads to anomalous actions in camera surveillance sites and are to be considered as a serious issue. All anomalies will leads the system or the authorities who dedicated for monitoring to the suspicious events. To detect the anomalies, the animals and human objects must be identified in each image frames first. After object identification events detection phase has to be done. Before going to the event detection phase, noise elimination, shadow detection, object classification can be carried out according to the needs. This paper reveals general detection methodology for animal detection and human crowd. The proposed work mainly concentrated on the detection of animals in human territory based on its palm print images and video images. Human crowd can be treated as smoke- screen for all types of anomalies and so this paper mainly concentrated to detect the crowd well in advance and thereby preventing the crowd before it is happening.
Implementation of Open Source 3D Modeler for Sustainable CAE Exercise Class in College of Technology

Kobe City College of Technology Department of Mechanical Engineering was starting the new curriculum from 2017. In the new curriculum, Computer Aided Engineering (CAE) exercise was start for the fifth grade students at 2021 because of necessary skill for mechanical engineer. In this study, we attempt to establish a sustainable CAE education environment by adopting open source 3D modeler software, FEM software, computer monitoring and classroom management software, and CFD software for CAE classes. In addition, the adoption of these open source software was easily applied to online classes. On the other hand, open source software have no organized tutorial, which is prepared for commercial software. Therefore, we also wrote the original textbook.
Image Completion using Deep Convolutional Generative Adversarial Networks

Deep learning recently became the state-of-the-art in many pattern recognition tasks. Advance-ment of computational power and big datasets brings opportunity to use deep learning methods for image processing. We have used deep convolutional generative adversarial networks (DCGAN) to do various image processing tasks such as deconvolution , denoising and super-resolution. With DCGAN we can use a single architecture to perform different image processing tasks . While the results sometimes shows slightly lower PSNR for DCGAN compared to traditional methods but it tries to achieve competitive psnr scores. Thus , it allows to view quite appealing then other methods While it can learn from big data-sets very efficiently and allows itself to add high-frequency details automatically which traditional methods can’t. The architectgure in DCGAN is based on two neural networks of generator and discriminator which both tries to deceive each other and allows it to generate more appealing and realistic images from the datasets.
Practical Fall Detection System using Vision and Wearable sensors

Fall detection is an important and challenging research problem in healthcare domain. The fall detection system required to operate and give true alert in real time. Many of the existing approaches generates false fall alert which again causes inconvenience for the end users. Hence, there is a need to have robust and accurate fall detection approach with low latency in decision making. In this work, we have proposed and evaluated three different approaches of fall detection system based on a wireless accelerometer based embedded system, RGB Image processing based Software modelling approach and Kinect based depth processing approach. These proposed approaches try to improve on the mentioned drawbacks until we obtain a robust, running in real-time system with high accuracy and low processing time. In all of the demonstrated methods, we do not require any knowledge of the scene and computationally intensive classifiers. The accelerometer based embedded system consists of economic components and is easy to setup. RGB Image processing based Software modelling is simulated on MATLAB have been extensively researched and implemented in real-time. Kinect based depth based techniques are the most recent advancement on the issue and have resolved many discrepancies of the previous methods. The performance of each method is compared against each other. It is shown that our Kinect based depth processing provides promising accuracy of 94% which is better than the other approaches while simultaneously working in real time of 30 frames/second.
Graph Analysis

A graph is the primary data structure for representing different types of networks (for example, directed, undirected, weighted, signed, and bipartite). Networks are most naturally represented as a set of nodes with links between them. Social networks are one very prominent class of networks. The Internet is the biggest real-world network, easily codified as a graph. Furthermore, road networks and related algorithms to calculate various routes are again based on graphs. This chapter introduces the basic elements of a graph, shows how to manipulate graphs using a powerful Python framework, NetworkX, and exemplifies ways to transform various problems as graph problems. This chapter also unravels pertinent metrics to evaluate properties of graphs and associated networks. Special attention will be given to bipartite graphs and corresponding algorithms like graph projections. We will also cover some methods to generate artificial networks with predefined properties.
Cyber Forensics

Cyber Forensics is a science wherein the scientific examination and analysis of digital evidence are done so that the information obtained is put up into the presentable format which can be used as appropriate proof in the court of law. Cyber forensics has very much vast context associated with digital forensics including homeland security, information security, corporate espionage, economic spying, white collar crime, child pornography, traditional crime, incident response, employee monitoring, privacy issues, etc.
3D Construction of Brain using 2D MRI Slices

Medical image analysis plays a more and more major role in medicine. Detection and analysis of risks causing cancer and other medical issues in early-stage help to prevent death. PET, CT, X-ray and MRI are the medical imagining techniques used. There have been a number of researches have done on constructing the 3D model for better analysis of the structure. The paper is proposed about the construction of a 3D brain model using 2D slices of MRI. The 3 different images of the brain are considered (T1, T2 and PD). The image registration is applied to T1, T2 and PD and fusion is done. The fused image includes of the complete detail of the brain, these 2D slices are used to construct the 3D model. The 3D map is created and surface to volume construction is applied to build the 3D model.
An Adaptive Neuro-Fuzzy Model for Quality Estimation in Wireless 2D/3D Video Streaming Systems

Delivering high Quality of Experience (QoE) is essential to the success of today’s subscription for internet video streaming services. Quality of Service (QoS) metrics are considered by the research community as the most influential factor on video QoE. Therefore, establishing QoS-QoE correlation becomes critical for improving video QoE estimation. This paper presents experimental development of effective correlation between QoE and QoS for both 2D and 3D video streaming services. This is then used to build an objective QoE estimation model for real-time streaming of both 2D and 3D video contents over wireless networks. This model is based on using Adaptive Neural Fuzzy Inference System (ANFIS) to estimate the perceived video QoE. The proposed QoE model was trained with a set of media and packet layers’ metrics, taking into account the effect of video content type, dimension, and different packet loss metrics. The performance of the proposed QoE estimation model shows a considerable estimation accuracy with a correlation coefficient of 92% and 0.167 RMSE.
Pengembangan Perangkat Pembelajaran IPA Berbasis Model Pembelajaran Inkuiri Pada Materi Sistem Pencernaan Untuk Melatih Keterampilan Proses Siswa Smp Kelas VIII Dan Meningkatkan Hasil Belajar Siswa

Abstract: This research has purpose to produces learning materials that proper theoritically and empirically that consists of lesson plan, student work sheet, student book on digestive system that supported by increasing of student achievementand student responses towards learning that try process skils with junior high school research subject in Sidoarjo. This research used 4D development model but this research did not use dissiminate with one group pretes posttest in try out. Data collecting method, questionnaire, test and observation. Data analysis used qualitative descriptive technic. Research findings showed that validation of learning materials, namely syllabus, lesson plan, student book, and student worksheet was 82.5%, 91.3%, 80.3%, 85.0%; respectively lesson plan implementation was high and students response very positive (80.3). Learning materials that developed was proper to be used in learning and increased student achievement.
Open Education Resource for School Children with Down Syndrome

Action recognition in video sequences is a challenging problem of computer vision due to the similarity of visual contents, changes in the viewpoint for the same actions, camera motion with action performer, scale and pose of an actor, and different illumination conditions. Also, there is no designated action recognition model for hazy videos. This model proposes a novel unified and unique model for action recognition in haze built with Convolutional Neural Network (CNN) and deep bidirectional LSTM (DB-LSTM) network. First, every frame of the hazy video is feed into the AOD-Net (All-in-One Dehazing Network). Next, deep features are extracted from every sampled dehazed frame by using VGG-16, which helps reduce the redundancy and complexity. Later, the sequential and temporal information among frame features is learnt using DB-LSTM network, where multiple layers are stacked together in both the forward and backward passes of DB-LSTM to increase its depth. The proposed unified method is capable of learning long term sequences and can process lengthy videos (even hazy videos) in real time by analyzing features for a certain time interval. Experimental results on both synthesized and natural video datasets show decent results on par with other state of the art methods in action recognition using the proposed method on the benchmark data set UCF-101. This helps the Down Syndrome Students to recognize an action faster.
Alignment

What you will learnThe purposes of the alignment process are to measure distances/similarities between strings and thus to locate origins of Next Generation Sequencing (NGS) reads in a reference genome. Alignment algorithms like BLAST that can be used to search for the location of a single or a small number of sequences in a certain genome are not suitable to align millions of NGS reads. This led to the development of advanced algorithms that can meet this task, allow distinguishing polymorphisms from mutations and sequencing errors from true sequence deviations. For a basic understanding, the differences between global and local alignment and the underlying algorithms are described in a simplified way in this chapter, as well as the main difference between BLAST and NGS alignment is described in a simplified way in this chapter. Moreover, different alignment tools and their basic usage are presented, which enables the reader to perform and understand alignment processes of sequencing reads to any genome using the respective commands.
Detecting Faces in Noisy Images using Hit-Miss Transform (HMT)

The object of this paper is to detect faces in noisy images. All sample images show homogeneous background faces. The transformation of the Hit-Miss is used to detect object boundaries and remove the noise effect. Two filters are cascaded to handle high noise levels in a special way. An application for face detection in noisy conditions is presented. The algorithm is implemented on faces taken from the Manchester face database after adding Gaussian noise to them.