Reviewerly/exHarmony huggingface dataset:
The Impact of International Students’ Social Adaptation on the Training Process in Higher Education

The article deals with the impact of international students’ social adaptation on the training process in higher educational institution. The article aims to highlight and analyze the main aspects of international students’ social adaptation as one of the effective factors that impact the quality of training. The tasks of the article are to study and characterize the features of the process of international students’ social adaptation (phases, types, components); to analyze challenges of social adaptation that international students meet; to introduce ways to overcome the problems of international students’ social adaptation in higher education.&#x0D; General theoretical research methods (analysis, synthesis, system-structural) have been used to reach the research purpose. &#x0D; The result as follows: the international students’ social adaptation is a continuous process of adaptation to new conditions in training, cultural and social environment. That is, as a process in which a student adapts to different activities (learning, scientific, etc.), acquires knowledge and skills, adapts to a new way of life in society (living conditions), adopts cultural norms, values, and interacts with the social environment, develops communicative abilities (language, communication). A number of challenges (psychological, training, socio-cultural) that international students meet were identified. Effective ways such as a proper organization of training process and individual work; expanding friendly contacts with local students; social-pedagogical support; were proposed to overcome international students’ social adaptation challenges. &#x0D; We have concluded that the sufficient international students’ social adaptation has as a positive impact on the academic process as on students’ mental and physical state.
A PERFORMANCE BASED COMPARATIVE ENCRYPTION AND DECRYPTION TECHNIQUE FOR IMAGE AND VIDEO FOR MOBILE COMPUTING

When data exchange advances through the electronic system, the need for information security has become a must. Protection of images and videos is important in today's visual communication system. Confidential image / video data must be shielded from unauthorized uses. Detecting and identifying unauthorized users is a challenging task. Various researchers have suggested different techniques for securing the transfer of images. In this research, the comparative study of these current technologies also addressed the types of images / videos and the different techniques of image / video processing with the steps used to process the image or video. This research classifies the two types of Encryption Algorithm, Symmetric and Encryption Algorithm, and provides a comparative analysis of its types, such as AES, MAES, RSA, DES, 3DES and BLOWFISH.
Crack Detection Method of Highway Side Slope Based on Computer Vision

Crack problem is a common disaster symptom of highway side slope, and the discussion on the crack detection method of highway side slope continues to deepen. From the traditional manual inspection in the early stage to the modern crack detection of highway side slope based on computer vision technology, the technological reform corresponds to the reduction of detection cost, the reduction of manual intervention, and the improvement of safety factor. Based on the computer vision technology, the side slope crack detection technology uses a professional-grade camera to shoot the details of slope cracks and cooperate with manual annotation and analysis, so as to realize the establishment of highway side slope crack data set. Based on the ideas of deep learning and dilated convolution, a crack detection model of highway side slope is built to accurately identify and divide the cracks in the slope. This work mainly discussed the application of computer vision technology in highway side slope crack detection, and analyzed the ideas, processes and methods of highway side slope crack detection based on computer vision in detail, expecting to provide corresponding guidance and reference for the intelligent detection of highway side slope.
Towards a framework for graph-based keyword search over relational data

Keyword-based access to structured data has attracted research and industry as a means for facilitating access to information. In recent years, the research community and big data technology vendors put a lot of effort into developing new proof of concept systems for the task at hand. Two major limitations have been identified for such prototypes to transition into fully developed products: 1) systems are not designed to scale up; 2) the absence of a complete evaluation approach oriented towards effectiveness. This work presents a framework for supporting the development and the evaluation of graph-based keyword search systems. Furthermore, the implementation of a core module of this framework is detailed and shared open-source with the community.
COVID-19 Article Classification Using Word-Embedding and Extreme Learning Machine with Various Kernels

The impact of the COVID-19 pandemic on the socially networked world cannot be understated. Entire industries need the latest information from across the globe at the earliest possible. The business world needs to cope with a very volatile market due to the pandemic. Businesses need to be swift in sensing potential profit opportunities and be updated on the changing consumer demands. Technological advances and medical procedures that successfully deal with COVID-19 can help save lives on the other side of the world. This seamless passage of crucial information, now more than ever, is only possible through the networked world. There are on average 821 articles published online on COVID-19 a day. Manually going through around 800 articles in a day is not feasible and highly time-consuming. This can prevent the industries and businesses from getting to the relevant information in time. We can optimize this task by applying machine learning techniques. In this work, six different word embedding techniques have been applied to the title and content of the articles to get an n-dimensional vector. These vectors are inputs for article classification models that employ Extreme Learning Machine (ELM) with linear, sigmoid, polynomial, and radial basis function kernels to train these models. We have also used feature selection techniques like the Analysis of Variance (ANOVA) test and Principal Component Analysis (PCA) to optimize the models. These models help to filter out relevant articles and speed up the process of getting crucial information to stay ahead of the competition and be the first to exploit new market opportunities. The experimental results highlight that the usage of word embedding techniques, feature selection techniques, and different ELM kernels help improve the accuracy of article classification.
Social Mining System for Start-Ups with Popularity Prediction

Social mining system or social media listening proposes a great way to listen to brand mentions and conversations regarding the brand or start-up on the Internet. The diverse data obtained from targeted Web sites like Reddit, Twitter, and YouTube can be used to visualize, analyze, and interpret meaningful information for important assessment. With this proposed model, the research on multilayer perceptron (MLP) and random forest algorithm’s predictive capabilities with valence–arousal–dominance (VAD) analysis along with sentiment analysis using NLTK can be studied in detail and can be effectively trained to improve its efficiency. In this paper, we have done a comparative study of how data behaves differently on different social media platforms and how they require different machine learning models to give an accurate result. In addition to that, we have also pointed out how different attributes make a huge difference in predicting outreach of a social media post. In this research paper, we have also elaborated on how we performed sentiment analysis and how we have categorized five intricate emotions into two distinctive categories, i.e., positive and negative. A detailed visualization consisting of multiple charts and graphs on a dashboard can act as a great tool in not only understanding the brand or start-up’s target audience, but also create a strong Internet persona for themselves.
Getting Started with Python 3 and Jupyter Notebook

I hope that all of you have read the introduction and the table of contents. This is very important because if you are a complete beginner, please do not skip this chapter. The entire field of machine learning and artificial intelligence requires solid knowledge of the tools and frameworks used in the area. This chapter serves as the foundational chapter for Python programming for machine learning that we will cover in this book. It introduces the novice readers to the Python Programming Language, Scientific Python Ecosystem, and Jupyter Notebook for Python programming.
REVISING THE ACADEMIC PROGRAM FOR THE PURPOSE OF PROFESSIONAL DIGITAL COMPETENCIES DEVELOPMENT

The research subject is the issue of training personnel capable of being actively involved in digital transformation through applying necessary digital competencies. This paper considers an option for revising the bachelor’s degree program 15.03.05 “Design and Technological Support of Machine-Building Industries” (specialization “Technology of Mechanical Engineering”). The intended result of the revised academic program for students is the acquisition of the applied digital technologies in their future profession and the achievement of set professional targets formed by the enterprises. The algorithm for the development and implementation of the project revision includes three stages. The first stage results in the competence model of a graduate as the foundation of the key changes in training personnel for jobs in digital industries. The second stage focuses on the titles and contents of the courses. Examples of designing a digital competencies profile are provided. The third stage involves designing of academic materials for lecture-type classes, video lectures, video courses, and the materials allowing evaluating the level of digital competencies development. The paper describes the activities implemented at the Chair of Engineering Technology, Manufacturing Machines, and Tools and aimed at revising the content, methods, and forms of an educational activity to prepare graduates highly demanded in the digital economy. The conducted research and analysis of technologies used for digitalization of educational content, and methods of teaching have identified areas for further development of the issue, such as revision of the other academic programs of the specialty 03.15.05 and the Master’s degree program.
Collision Avoidance Control of Robot Arm Considering Shape of Obstacle

Control Barrier Functions (CBFs) have attracted much attention in recent years as a control method to guarantee safety. Previous studies have shown the effectiveness of obstacles and robots of various shapes. However, there is no study using a CBF that considers the shape of the target system and the obstacle simultaneously. In this paper, we derive a control law of considering the shape of the target system and design CBF for non-convex safe sets. Then, we construct a human assist input by using the CBF. Moreover, we confirm the effectiveness of the proposed method by experiments of an arm robot.
161P Bayesian network structure for predicting two-year survival inpatients diagnosed with non-small cell lung cancer

The incidence of lung cancer has been increasing. Healthcare providers are trying to acquire more knowledge of the disease’s biology to treat their patients better. However, the information available is more than humans can comprehend. Predictive models such as Bayesian networks, which can intricately represent causal relations between variables, are suitable structures to model this information. However, the conventional methods for developing Bayesian network structures are limited in healthcare. 545 NSCLC patients treated primarily with (chemo)radiation therapy from Maastro clinic in the Netherlands between 2010 and 2013 were considered to develop this Bayesian network structure. All continuous variables were binned before learning the structure process. Patients with missing survival status and variables with more than 25% missing information were excluded from the structure-building process. The causal relationship (arcs) between variables in the data was determined using the hill-climbing algorithm with domain experts’ restrictions over bootstrap runs (B=400). The final structure consists of nodes and arcs, where arcs (relationships) are present in at least 70% of all bootstrap samples. Structural performance was assessed by computing the area under the curve (AUC) values and producing calibration plots based on a repeated 5-fold cross-validation. In addition, an adapted pre-specified expert structure was compared with a structure developed from the method in this study. Tumor load was removed from the final Bayesian network structure due to its high percentage (37%) of missing information. The final cohort used to develop the structure was reduced to 499, excluding 46 (08.4%) patients with missing survival status. The resulting structure’s mean AUC and confidence interval to predict two-year survival was 0.614 (0.499 - 0.730). The performance of the compared structures was only slightly above the chance level, but the structure based on the method in this study was clinically more correct. The aim of this study was attained and shows that Bayesian network structures which combine expert knowledge with a rigorous structure-learning algorithm produce a clinically valid structure.
Study and Analysis of Phishing Attack

Now a day there is lot of data security issue. Hackers are very intelligent now, they using their knowledge to hack someone system and grab their information such attack is call phishing attack. In phishing attack scammers collect personal information from user. Attacker sends fraud e-mails, text message, telephone calls to users to gather their information. Phishing is same like fishing in lake, instead of trying to capture fish, phishers are stealing personal information. The personal information could include credit card details, username and password, bank details. After gathering the information attacker could commit crimes such as financial loses and identity theft. Phishing attack is common in online communication and transaction. This paper gives fair idea about phishing attack.
Link-Information Augmented Twin Autoencoders for Network Denoising

Removing noisy links from an observed network is a task commonly required for preprocessing real-world network data. However, containing both noisy and clean links, the observed network cannot be treated as a trustworthy information source for supervised learning. Therefore, it is necessary but also technically challenging to detect noisy links in the context of data contamination. To address this issue, in the present article, a two-phased computational model is proposed, called link-information augmented twin autoencoders, which is able to deal with: 1) link information augmentation; 2) link-level contrastive denoising; 3) link information correction. Extensive experiments on six real-world networks verify that the proposed model outperforms other comparable methods in removing noisy links from the observed network so as to recover the real network from the corrupted one very accurately. Extended analyses also provide interpretable evidence to support the superiority of the proposed model for the task of network denoising.
The Timbre of Tears

This commentary discusses the author’s relationships to certain musical sounds, and how those correspondences relate to several key themes examined in a selection of essays within this journal issue. The commentary closes with a brief reflection on the work of musician and composer Diane Moser.…
Blockchain Architecture and Policy for Transforming Healthcare Industry

Time and again in the modern era, a considerable hike in technology has been seen. Lately, the development in the healthcare industry is not only because of the emergence of the Internet of Things, but also the blockchain has gained impressive influence in this field. Factors like security, decentralization, and maintenance of digital data all are massive breakthroughs by blockchain technology in this industry. In the last few decades, data digitization and decentralization continue to grow in the healthcare industry. The thought of blockchain captivates researchers who want valuable insights into this sector. In the healthcare domain, analysis of data by utilizing patient therapy records and wellbeing information blockchain technology has been a growing struggle for many years. Blockchain technology has many implementations in healthcare, such as electronic medical records, supply of drugs and medicines, and remote patient monitoring. These use cases are implemented when applying blockchain properties such as immutable data storage, secure transactions, and sharing of clinical profiles of patients. They attempt to foresee the reoccurrence of significant issues which enable more precise outcomes. This chapter bridges the existing healthcare data and emerging technologies of blockchain. It focuses on describing the architecture for transforming health-related data.
Energy efficient OEO conversion and its applications to photonic integrated systems

We describe nanophotonics-based opto-electric devices and their femtofarad integration. The combination of these devices and optical interference units has a potential to provide energy-efficient and ultra-low latency optical information processing.
Body Tracking

Depth cameras have many practical, real-world applications, from 3D object scanning to environment understanding, to Augmented Reality. Since 2009, though, Kinect has been famous for one thing: human body tracking.
Study on the Evaluation Method of the Clarity of Critical Areas of Digital Images

In response to the current needs of machine vision systems for digital image clarity evaluation methods, this paper proposes a scientific and objective image critical area clarity evaluation method, focusing on the critical areas of the image, fusing the edge features and texture features of the critical areas of the image, and Using the machine learning method of Support Vector Regression (SVR), and established an accurate evaluation model for the clarity of critical areas of digital images from the feature information to the clarity score. The evaluation result of the definition of the critical area of the image is consistent with the subjective image quality of the database.
Magnetic resonance brain volume property-based accelerate medical image algorithms using graphics processing unit

This paper elaborates the design and implementation details of parallel image processing techniques that are used to accelerate the medical image algorithms with compute unified device architecture (CUDA) supported graphics processing unit (GPU). The algorithms are chosen from denoising, morphology, clustering, and segmentation. Three parallel computing models are developed based on the properties of algorithms and magnetic resonance imaging (MRI) principles. The acceleration of parallel algorithms is compared with that of sequential central processing unit (CPU) implementation measured in terms of speedup folds (×).
Research on Utilization of Network Resource Base of Teaching Chinese as a Foreign Language under Digital Humanities Based on Information Technology

The progress of science and technology and the popularization of network education have made great changes in the teaching and learning mode of teaching Chinese as a foreign language. The convenience of the network provides a large number of learning resources and platforms for students to learn Chinese. It is convenient for students and teachers with the same learning hobbies to communicate and discuss. Using the principle of computer information retrieval technology, a large number of resources are organized and stored in a certain way and law to form a set of information. Users can use specific retrieval instructions, search words and retrieval strategies to quickly and efficiently find information according to their own needs. Many teachers of Chinese as a foreign language are willing to share the problems and solutions encountered in their teaching process on the Internet, its content is becoming more and more abundant, and the network resource bank of Chinese as a foreign language is becoming more and more perfect. This paper will briefly outline what is the network teaching resource bank of Chinese as a foreign language, the advantages and disadvantages of its utilization and the advantages and disadvantages of the network resources of teaching Chinese as a foreign language, and hope that computer research and development personnel and teachers of Chinese as a foreign language will cooperate. Strive to develop more computer information retrieval resources suitable for teachers of Chinese as a foreign language.
Applications—Privacy-Preserving Image Processing

In the following chapters, we introduce the fusion of machine learning, image processing and security as an emerging paradigm to protect the privacy of clients and classifier. If the model of machine learning image processing is supervised, then it can be divided into two phases: (i) the training phase during which the algorithm learns a model $$\varphi $$ from a data set of labeled samples, and (ii) the classification phase that runs a classifier $$\mathcal {C}$$ over a previously unseen feature vector y, using the trained model $$\varphi $$ to output a prediction $$\mathcal {C}(y;\varphi )$$ .
Investigation of the GAN-SSL Classifier Properties for Identification Expertise

The generative adversarial nets (GANs) are investigated for classification problem. GANs for semi-supervised learning (GAN-SSL) is proposed for complex classification problem. The identification expertise problem is challenging for classification because of complex structure of classes, unbalanced samples and cross-classes. We use semi-supervised learning to solve unbalanced classes problem. Two groups of experiments were carried out. The first group of experiments for the model dataset that consist of classes of points normally distributed about vertices an eight-dimensional hypercube. The second groups of experiments for the petrol identification expertise dataset we get from laboratory of petrol quality. The experiments with good model examples get good quality more than 97%. The analysis of network parameters and generator properties is made. The classification model for petrol identification expertise was created and has 94% quality. In this work we use GAN-SSL classification on petrol identification expertise example, but this classification model can be used for diesel fuel, household chemicals items, different oils and for various other objects.