Reviewerly/exHarmony huggingface dataset:
Research on Students' Course Selection Preference based on Collaborative Filtering Algorithm

Due to the events caused by the COVID-19 pandemic, the education industry is no longer limited to offline, and online classroom education is widely used.The rapid development of online education provides users with more abundant educational course resources and flexible learning methods.Various online education platforms are also constantly improving their service models to give users a better learning experience.However, at present, there are few personalized information recommendation services in student course selection.Students receive the same course selection information and cannot be "tailored" according to their specific preferences.This paper focuses on the integration of collaborative filtering technology into a college course selection system to construct a rating matrix based on students' ratings of the courses they take through correlation between courses and correlation between students.Based on the collaborative filtering algorithm, a predictive rating matrix is generated to produce a recommendation list to achieve intelligent recommendation of suitable courses for students.The experimental results show that, based on the traditional collaborative filtering recommendation technique, the improved collaborative filtering algorithm based on both item and user weighting is used to achieve course recommendation with higher recommendation accuracy.The application of the improved collaborative filtering technique in the course selection recommendation system of colleges and universities is very good at recommending courses for students intelligently, and the recommended courses for students have good rationality and accuracy, and achieve more intelligent course selection for students, which has great practicality and practical significance.
Identification of the Aspects of Educational Deprivation of Students and Teachers of Higher Education Institutions in the Conditions of Distance Learning

In the current socioeconomic and epidemiological conditions, the use of distance learning technologies in Russia is increasing. The application of distance learning in higher education institutions during the COVID-19 pandemic and the analysis of the results of the survey of satisfaction of teachers and students with organizational, content, and technical parameters of such training allowed the authors to identify aspects indicating the manifestation of such a phenomenon as educational deprivation. The research aims to summarize the opinions of teachers and students of a pedagogical university, indicating the limitation of social and educational functions of distance learning during the professional training of future teachers. The paper summarizes the results of a survey of teachers and students at a pedagogical university held in November 2020. Analyzing some parameters in the answers, interpreted as states of deprivation, allows us to improve distance learning technologies further and prevent possible negative consequences of emerging limitations. The survey is used as a method of socio-psychological research. The authors use general scientific methods of analysis, generalization, and the mathematical method of calculating the percentage ratio to process the obtained data. The research results show the directions for further improvement of distance learning technologies used in professional training.
Using the Forms API, Part 1

In this chapter, I describe the Angular forms API, which provides an alternative to the template-based approach to forms introduced in Chapter 12 . The forms API is a more complicated way of creating forms, but it allows fine-grained control over how forms behave, how they respond to user interaction, and how they are validated. Table 21-1 puts the forms API in context.
Unsupervised Multi-camera Domain Adaptation for Object Detection in Cultural Sites

Domain adaptation approaches can be used to efficiently train object detectors by leveraging labeled synthetic images, inexpensively generated from 3D models, and unlabeled real images, which are cheaper to obtain than labeled ones. Most of the state-of-the-art techniques consider only one source and one target domain for the adaptation task. However, real world scenarios, such as applications in cultural sites, naturally involve many target domains which arise from the use of different cameras at inference time (e.g. different wearable devices and different smartphones on which the algorithm will be deployed). In this work, we investigate whether the availability of multiple unlabeled target domains can improve domain adaptive object detection algorithms. To study the problem, we propose a new dataset comprising images of 16 different objects rendered from a 3D model as well as images collected in the real environment using two different cameras. We experimentally assess that current domain adaptive object detectors can improve their performance by leveraging the multiple targets. As evidence of the usefulness of explicitly considering multiple target domains, we propose a new unsupervised multi-camera domain adaptation approach for object detection which outperforms current methods. Code and dataset are available at https://iplab.dmi.unict.it/OBJ-MDA/ .
Refined Sentiment Analysis by Ensembling Technique of Stacking Classifier

The biggest challenge in Electronic Commerce (E-Commerce) is understanding the demand of the market and customer perception regarding the products of online stores. Online user evaluations are faster than a direct poll for obtaining customers' perceptions. To predict the quality of service provided, sentiment analysis methodology is used, which identifies and classifies product reviews into positive and negative sentiments through different classifying algorithmic models. This study has used Random Forest, Logistic Regression, and Gaussian Naive Bayes to perform sentiment analysis to analyze customers' likes and dislikes. The “Clothing Products Reviews” dataset from ‘Kaggle’ is used. Due to the significant number of positive sentiments, the results showed the dependability dimensions that cause the biasness in results. Synthetic Minority Oversampling Technique (SMOTE) is applied to balance the positive and negative sentiments. Random Forest, Naïve Bayes, and KNN classifiers are combined into one Stacking Classifier to improve the results and get 97% accuracy.
Real Time Based Target Detection Method

The paper discusses about the implementation of a system for detecting the target and giving warning about the detected target. The proposed system detects both the moving as well as stationary targets. The framework used in the system extracts target features from the images and videos captured by a camera. The processing of the images and videos is done by a weighted gain filter for removing noise and thus enhancing the image quality. A number of experiments are done on the stationary and moving targets in order to prove the efficiency of the system. The proposed system detects and warns about the target by using low cost camera and simple design and implementation. Python is used as base for programming and is integrated with the camera for increasing the applications of the proposed system. The detection of the target is done on the basis of color of the target in the moving and stationary videos or images.
A Comparison of Deep Learning Methods for Inebriation Recognition in Humans

Excessive alcohol consumption leads to inebriation. Driving under the influence of alcohol is a criminal offence in many countries involving operating a motor vehicle while inebriated to a level that renders safely operating a motor vehicle extremely difficult. Studies show that traffic accidents will become the fifth most significant cause of death if inebriated driving is not mitigated. Inversely, 70% of the world population can be protected by mitigating inebriated driving. Short term effects of inebriation include lack of balance, inhibition and fine motor coordination, dilated pupils and slow heart rate. An ideal inebriation recognition method that operates in real-time is less intrusive, more convenient, and efficient. Deep learning has been used to solve object detection, object recognition, object tracking and image segmentation problems. In this paper, we compare deep learning inebriation recognition methods. We implemented Faster R-CNN and YOLO methods for our experiment. We created our dataset of sober and inebriated individuals made available to the public. Six thousand four hundred forty-three (6443) face images were used, and our best performing pipeline was YOLO with a 99.6% accuracy rate.
Sr-Net: A Super-Resolution Image Based on Dwt and Dcnn

Recently, a surge of several research interests in deep learning has been sparked for image super-resolution. Basically, a deep convolutional neural network is trained to identify the correlation between low and high-resolution image patches. In other side, profiting from the power of wavelet transform to extract and predict the “missing de- tails” of the low-resolution images, we propose a new deep learning strategy to predict missing details of wavelet sub-bands in order to generate the high-resolution image which we called a super-resolution image based on discrete wavelet transform and deep convolutional neural network (SR-DWT-DCNN). By training various images such as Set5, Set14 and Urban100 datasets, good results are obtained allowing to validate the effectiveness and efficiency of our proposed method based on evaluation of PSNR and SSIM metrics. The reconstructed image achieves high resolution value in less run time than existing methods.
One Size Does Not Fit All: Strengths and Weaknesses of the Agile Approach

Agile project management techniques, such as iterative sprints and granting workers task autonomy, have become commonplace in many organizations. We experimentally examine how these techniques affect performance in two innovation settings: (1) a product development setting, represented by a task in which participants build connected word structures using letters of the alphabet, and (2) a business model innovation setting, represented by a task in which participants search for the best combination of business attributes on a multidimensional solution landscape. Our results suggest that the effects of Agile on performance are not uniform and depend on the innovation setting and on the performance measure. Agile improves average performance in the product development setting but lowers average performance in the business model innovation setting. In both settings, Agile techniques lead to more incremental (less radical) strategies, which narrows performance variance. Together, these results caution against uniform adoption of the Agile approach, and suggest that the choice of the approach should depend on the nature of the project and on the desired risk-return profile of the firm.
Extracting and Developing Spectral Indices for Soil Using Sentinel-2A to Investigate the Association of Soil NPK

Spatial soil nutrient information is very essential for agricultural risk assessment and decision making. Remote sensing data act as the chief constituent of soil nutrient information that can be converted to digital soil database which is found to be cost effective and less time consumption compared to manual soil testing procedure. This study investigated the soil nutrients through extracting the soil spectra from multispectral satellite data (Sentinel-2A). Soil samples were taken and tested in spectroradiometer laboratory as per standard procedure. Spectral indices extracted from sentinel data that show the soil properties were correlated with the in situ laboratory spectral results. The degrees of accuracy were identified, and the justifications for using remote sensing technology alternative to manual soil testing were revealed in this study. Mathematical correlations were employed to derive R2 value using OriginPro 8.5 software. R2 value derived from in situ laboratory spectra and the spectra derived from the sentinel data were validated against the independent set of samples which clearly gives pattern of soil properties followed in the project area (Pollachi, Tamil Nadu). The outcome of this study elucidates the extensive usage of satellite data in exploring the soil health information in large scale. Considering the increased availability of freely available remote sensing data, soil information at local and regional scales can be predicted with relatively little financial and human requirements.
Systematic Review of Educational Methodologies Implemented During the COVID-19 Pandemic in Higher Education in Ibero-America

Since the interruption of face-to-face classes due to the COVID-19 pandemic, Higher Education institutions adapted their courses to a virtual format in order to serve students during the contingency. This work analyzes, through a systematic review of a twenty-four-article corpus, which study the pedagogical models adopted by such Institutions in Ibero-America. The reviewed works have been published between 2020 and 2021, and analyze the implications of reorganizing the teaching-learning processes, based on the framework of the Community of Inquiry model. The results show that a high percentage of the research focuses on Teaching presence, especially in the educational and organizational design, and much less focuses on Cognitive presence, and a limited production on the Social presence dimension and the analysis of indicators that reinforce learning.
Advanced Web

AbstractThrough the study of Chap. 1, you may have a basic understanding of Web challenges. However, in actual competitions, the challenges are often composed of multiple vulnerabilities, and the Web vulnerabilities mentioned in Chap. 1 are often the introductory part of some complex challenges. For example, the back-end system password is obtained through SQL injection, and there are upload vulnerabilities in the back-end system. How to bypass uploading Webshell to get the flag becomes the key.
Physician perceptions of surveillance: Wearables, Apps, and Chatbots for COVID-19

To characterize the global physician community's opinions on the use of digital tools for COVID-19 public health surveillance and self-surveillance.Cross-sectional, random, stratified survey done on Sermo, a physician networking platform, between September 9 and 15, 2020. We aimed to sample 1000 physicians divided among the USA, EU, and rest of the world. The survey questioned physicians on the risk-benefit ratio of digital tools, as well as matters of data privacy and trust.Descriptive statistics examined physicians' characteristics and opinions by age group, gender, frontline status, and geographic region. ANOVA, t-test, and Chi-square tests with P < 0.05 were viewed as qualitatively different. As this was an exploratory study, we did not adjust for small cell sizes or multiplicity. We used JMP Pro 15 (SAS), as well as Protobi.The survey was completed by 1004 physicians with a mean (standard deviation) age of 49.14 (12) years. Enthusiasm was highest for self-monitoring smartwatches (66%) and contact tracing apps (66%) and slightly lower (48-56%) for other tools. Trust was highest for health providers (68%) and lowest for technology companies (30%). Most respondents (69.8%) felt that loosening privacy standards to fight the pandemic would lead to misuse of privacy in the future.The survey provides foundational insights into how physicians think of surveillance.
Streaming Detection of Significant Delay Changes in Public Transport Systems

Public transport systems are expected to reduce pollution and contribute to sustainable development. However, disruptions in public transport such as delays may negatively affect mobility choices. To quantify delays, aggregated data from vehicle location systems are frequently used. However, delays observed at individual stops are caused inter alia by fluctuations in running times and the knock-on effects of delays occurring in other locations. Hence, in this work, we propose both a method detecting significant delays and a reference architecture, relying on the stream processing engines in which the method is implemented. The method can complement the calculation of delays defined as deviation from schedules. This provides both online rather than batch identification of significant and repetitive delays, and resilience to the limited quality of location data. The method we propose can be used with different change detectors, such as ADWIN, applied to a location data stream shuffled to individual edges of a transport graph. It can detect in an online manner at which edges statistically significant delays are observed and at which edges delays arise and are reduced. Such detections can be used to model mobility choices and quantify the impact of regular rather than random disruptions on feasible trips with multimodal trip modelling engines. The evaluation performed with the public transport data of over 2000 vehicles confirms the merits of the method and reveals that a limited-size subgraph of a transport system graph causes statistically significant delays.
Development of Virtual Reality and Computer Technology Application

Virtual reality technology, as a cutting-edge media technology, is widely used in various fields to enhance the user's autonomy and selectivity, make the user feel like in the real world, and realize the user's immersive experience. In this paper, through the related definition of virtual reality technology, at the same time, the integration of computer technology and virtual reality technology development research, through the survey, we can see that the number of people who choose to be optimistic is 71, accounting for 81.6% of the total number. The application of virtual reality technology in the development is inseparable from the help and promotion of computer technology, and the better integration of the two can achieve the purpose of common development.
Understanding the Implications of Digital Competence for the Education Process in Romania. A Literature Review

This chapter aims to investigate the role that information and communication technologies (ICT) must play in the educational system from Romania, focusing in particular on the next generations of specialists in the context of the “digital revolution”. A review study was set up to present a complete and recent overview of empirical studies published in the last 20 years. To search for relevant studies, a comprehensive search will be performed using “ANELIS PLUS”, between 2000 and 2021. This browser includes many databases such as ERIC, SAGE, SCIENCE DIRECT, SCOPUS, EBSCO, etc. To search for potential references, the term “ICT” will be combined with the following terms: “Romania”, “digital skills”, “digital competencies”, “digital divide”, “education system”, “teachers”, and “compétences numériques”, “les enseignants”, or “la Roumanie”, to also have access to the French language databases Persee and Cairn. Also, to complete the list of studies, we use and search engines Google Scholar and ReseacheGate. Through this analysis, we will obtain a detailed picture of the relationship between ICT and the education system from Romania proving the issues of accessible ICT, the expected level of digital competencies of future specialists.
Pedestrian information collation through image-based person identification for pedestrian information sharing using V2V communications

The purpose of this research is to share pedestrian information among multiple vehicles by vehicle-to-vehicle communication and detect the positions of all pedestrians around the vehicle. In the past, we have proposed a method of collating shared pedestrian information through image-based person Re-Identification (ReID). In this paper, in order to improve the accuracy of pedestrian information collation, the state-of-the-art ReID network is adapted to our scenario by fine-tuning. In addition, a new pedestrian image dataset for sharing pedestrian information was constructed. In our experiments using our dataset, the proposed method improved the pedestrian information collation accuracy.
Database Concentration Method for Efficient Image Retrieval Using Clustering and Image Tag Comparison

As the world moves towards a digital civilization, we generate more and more data along the way. With this increase in the number of images on the internet and with social media companies, faster and scale-able image retrieval algorithms are needed for efficiency. In this paper, we propose a tag based image retrieval algorithm which also uses clustering for better efficiency in large data sets. Although there are multiple existing algorithms that use tags most of them use tag completion but not new tag generation. So, they cannot be used for completely unlabelled data. The proposed algorithm makes use of IBM’s(International Business Machines) VisualRecognitionV3 to generate new tags for completely unlabelled data. We make use of K-Means algorithm to firstly cluster the images into different clusters based on features extracted using ImageNet weights of ResNet50 architecture. We then use IBM’s VisualRecognitionV3 to label the folders based on its contents. Similar steps are also carried out with query image and using word-vectorization and cosine similarity, top-n most relevant folders are found out. Then relevant images from these best folders are saved as output based on similarity of image features, found using ResNet-50 model on ImageNet Weights.
Parallel Global Sequence Alignment with Arbitrary Scoring Scheme: Algorithm Formulation and Evaluation

Alignment of the sequences is useful in many biological and non-biological domains. Needleman-Wunsch algorithm is deemed as the basic algorithm for finding the optimal alignment between two sequences. It is a dynamic programming based algorithm which has quadratic time and space requirement. Nonetheless, as a result of increase in the capacity of sequence databases in recent years, sizes of sequences may contain billions of characters for computationally intensive problems. Comparison of such large sequences using sequential methods is avoided as it takes unacceptable amount of time for producing the results. In this paper, we proposed a parallel variation of the Needleman-Wunsch algorithm by reformulating the dependencies in the prevalent dynamic programming solution. Proof of correctness of the proposed algorithm is established. It evinces certain characteristics such as it allows arbitrary linear scoring scheme when comparing the sequences which is important to obtain peerless alignment. Moreover, it ensures load balancing among the computational units and maximizes the utilization of resources. It is compared with various state-of-the-art approaches. Experimental results using the real DNA and user trajectories sequences over graphic processing units (GPUs) suggest that the proposed algorithm is notably superior to the existing approaches that allow arbitrary linear scoring schemes. It achieved the GCUPs up to 14.06 and maximum speedup of 48.18x compared to the sequential implementation.
Positive Definite Kernels

In data analysis and various information processing tasks, we use kernels to evaluate the similarities between pairs of objects. In this book, we deal with mathematically defined kernels called positive definite kernels. Let the elements x, y of a set E correspond to the elements (functions) $$\Psi (x), \Psi (y)$$ of a linear space H called the reproducing kernel Hilbert space.