Reviewerly/exHarmony huggingface dataset:
A Moving Target Tracking Framework Based on a Set and Its Topological Space

Moving target tracking is a technology that matches frames and images based on target characteristics.This technology is widely utilized in intelligent transportation, logistics transportation, public security, sports event broadcasting, and other fields.Existing research focuses primarily on improving target detection and tracking algorithms to improve target retrieval and tracking efficiency.However, the majority of studies focus on global and full-range retrieval.More importantly, in large video scenes with multiple camera collaborations, these methods rarely consider the efficiency of target retrieval and tracking.Based on relevant theories and methods of video GIS, set theory, and topology, in this paper, a set and its topology space covering road networks, cameras, videos, and key frames were constructed.Additionally, the positioning, tracking, and track representation of a moving target based on the set and its topology space were solved.Compared to the feature vector algorithm, video summarization and Meanshift algorithm, the experimental findings reveal that the target retrieval performance, algorithm stability, and robustness are improved.
Cloud Computing Challenges and Opportunities

Abstract: This paper introduces the technology of cloud computing, and the challenges and opportunities associated with it, it also finds solutions for security and high cost issues. In this paper we use open source tools to build an environment for cloud computing, it also solves the aforementioned cloud problems in addition to providing an enormous storage capacity.
Techniques for Detecting Video Shot Boundaries: A Review

Due to the Corona Virus Diseases (COVID-19) pandemic, education is completely dependent on digital platforms, so recent advances in technology have made a tremendous amount of video content available. Due to the huge amount of video content, content-based information retrieval has become more and more important. Video content retrieval, just like information retrieval, requires some pre-processing such as indexing, key frame selection, and, most importantly, accurate detection of video shots. This gives the way for video information to be stored in a manner that will allow easy access. Video processing plays a vital role in many large applications. The applications required to perform the various manipulations on video streams (as on frames or say shots). The high definition of video can take a lot of memory to store, so compression techniques are huge in demand. Also, object tracking or object identification is an area where much considerable research has taken place and it is in progress.
BENet: Boundary Enhance Network for Salient Object Detection

Although deep convolutional networks have achieved good results in the field of salient object detection, most of these methods can not work well near the boundary. This results in poor boundary quality of network predictions, accompanied by a large number of blurred contours and hollow objects. To solve this problem, this paper proposes a Boundary Enhance Network (BENet) for salient object detection, which makes the network pay more attention to salient edge features by fusing auxiliary boundary information of objects. We adopt the Progressive Feature Extraction Module (PFEM) to obtain multi-scale edge and object features of salient objects. In response to the semantic gap problem in feature fusion, we propose an Adaptive Edge Fusion Module (AEFM) to allow the network to adaptively and complementarily fuse edge features and salient object features. The Self Refinement (SR) module further repairs and enhances edge features. Moreover, in order to make the network pay more attention to the boundary, we design an edge enhance loss function, which uses the additional boundary maps to guide the network to learn rich boundary features at the pixel level. Experimental results show that our proposed method outperforms state-of-the-art methods on five benchmark datasets.
The impact of blockchain in banking and financial services

A blockchain is a Distributed Ledger Technology (DLT) that consists of a growing list of records, called blocks, that are securely linked together using cryptography (Bagrecha et al., 2020). Blockchain has been called the new internet and is fast multiplying and remaking economies, infrastructures, technologies, and enterprises, including those in the financial sector. Banks are denoted as one of the essential pillars or the basic structure of our economy, and it help in the growth and transformation in society. The financial sector acknowledges the transformative influence of blockchain technologies on various actions like forging unique revenue, furnishing operation efficiency, improving end-user experience, and reducing risk in business operations. This paper has intensely explored how blockchain works in banking and financial services.
About the role of JSC “Russian Railways” in the development of quantum communications of the Russian Federation

The problem of protecting the transmitted information from an unauthorized access appeared together with the first telephone communication lines, but it became especially urgent with the development of modern electronic means of communication and universal digitalization. One of the ways to ensure secure data transmission is the use of quantum communications technology. The paper considers the stages of development of quantum communications and analyzes the role of JSC “Russian Railways” in this process.
Estimation and correction of motion blur in digital images

Digital images play a very important role in developing computer-aided systems. The motion blur and blur in such types of images affect the accuracy of the system. Therefore, it is a challenging task to estimate and remove the blur in the images. In the present paper, an attempt is made to use a Convolutional Neural Network (CNN) model to estimate and remove the blur in the images. The CNN model with different functions helps to improve the accuracy of removing blur from the images. Different network functions, such as ReLU and Sigmoid, and their combinations are analyzed for the modeling of CNN. The performance of CNN is analyzed with different parameters, such as blur estimation, PSNR, RMSE, SSIM, and MSE. The performance is measured by considering different image categories, such as more blur images, less blur images, dark blur images, and biomedical images. Considering the parameters, it is observed that CNN with ReLU and Sigmoid functions is giving better performance than other network functions. It is observed that CNN models are giving successful performance to remove blur and correct the blur than any other traditional models.
Correlation between swayam learning and its achievement among prospective teachers - a study in kanniyakumari district

In the current scenario, Swayam learning is becoming a part of the curriculum of higher education. At this point, the prospective teachers are not staying away from the word "swayam." In the curriculum of the College of Education, there is a compulsory certificate course from Swayam Learning App. All the candidates who are getting a degree in education must complete any one of the online courses from Swayam. Therefore, the prospective teachers are very excited for the completion of the online Swayam course. After the results were revealed, certificates were given to the candidates who had completed the course. The ultimate aim of the investigator is to find the correlation between Swayam learning and its achievement among prospective teachers. For that, the investigators designed a tool called "Checklist for Swayam Learning" and administered it in the College of Education in Kanniyakumari District. The investigator used the survey method for the present investigation. The population of the present study was the Prospective Teachers who are studying in College of Education at Kanniyakumari District. In the present investigation, there is no significant difference between the residential locality and branch of the subjects, there is a significant difference between the results of the Swayam examination and finally, there is no significant difference between the father's and mother's occupations. The prospective teachers are requested to study the concepts clearly and watch the video classes frequently with concentration. The use of the online platform Swayam is not only for getting certificates but also to collect information about their learning.
Blockchain Technology: Opportunities and Challenges in Various Domains

Blockchain has been used in non-financial applications since it was first introduced with Bitcoin. Bitcoin uses peer-topeer technology to operate without a central authority or banks; the management of transactions and the issuance of bitcoins is carried out collectively by the network. Bitcoin is open source; its design is public, no one owns or controls bitcoin, and anyone can participate. In blockchain technology, no intermediaries are used to establish a decentralized system. Based on literature reviews, this paper discusses blockchain technology concepts, mechanisms, applications, opportunities, and other relevant solutions for real-world problems. Blockchain concepts are discussed in this paper using healthcare, education, and logistics examples. Finally, it will discuss the various blockchains, their permissions, architectures, and emerging features.
MSDB-based CNN architecture for image dehazing in driverless cars

With the booming development of image processing technology and computer vision technology, scene detection and image processing in special weather has become an important research direction in this field. Among them, images taken in foggy days are easily affected by fog or haze, resulting in blurred details and low contrast to the loss of important image information, and to solve such problems image defogging algorithms are born. To address these challenges, a lightweight convolutional neural network based on multi-scale dense connectivity, called MSDL, is proposed in this paper for reconstructing blurred images. DehazeNet is the End-to-End defogging system that takes the fogged image as input, its transmission map as output, and then uses an atmospheric scattering model for image reduction. The proposed MSDL uses the transformed atmospheric scattering model to jointly estimate the transmission map and atmospheric light. In addition, a novel feature extraction module MSDB is proposed. Finally, extensive experiments are carried out using synthetic and natural hazy images. The experimental results show superiority over both non-deep learning and deep learning methods in both qualitative and quantitative evaluation. The PSNR, SSIM and MSE metrics were measured on different datasets, and the advantages were obtained on NYU2 dataset.
Fairer Comparisons for Travelling Salesman Problem Solutions Using Hash Functions

Fitness functions fail to differentiate between different solutions with the same fitness, and this lack of ability to distinguish between solutions can have a detrimental effect on the search process. We investigate, for the Travelling Salesman Problem (TSP), the impact of using a hash function to differentiate solutions during the search process. Whereas this work is not intended to improve the state-of-the-art of the TSP solvers, it nevertheless reveals a positive effect when the hash function is used.
Crime Site Examination Features in Criminal Cases on Cyber Fraud

The article reviews some tactical problems and solutions related to crime site examination in criminal cases on cyber fraud caused by specifics of the reviewed crime. The authors also analyze a number of difficulties arising in the investigator’s work and related to identification, withdrawal and recording of traces including material ones.
Cloud-based systems of open science in educational institutions

In the modern educational and research university environment there are new models of organization of investigations, as well as educational and scientific activities, based on innovative technological solutions and pedagogical approaches to open science. Issues of setting up cloud-based open science tools and services for users, forming open science systems within the educational and scientific environment of the educational institution in order to maximize the pedagogical potential of their use, improving learning and research outcomes, and improving the organization of research and educational activities are the subject of the article. Conceptual aspects of formation and development of high-tech learning and research environment of educational institution with the use of cloud-oriented systems of open science, as well as organization of access to electronic resources and services to support investigative activities based on cloud computing and research infrastructures are outlined.
Linguistic interpretation of contextual knowledge of domain ontology in pervasive and ubiquitous computing devices

In recent decades, ubiquitous and pervasive computing has been used to increase people's knowledge awareness in a variety of contexts in healthcare, education, agriculture, and so on. However, these computing systems have significant shortcomings in terms of supporting knowledge sharing, reasoning, machine readability, and sufficient controls over how information about individuals is used and shared with others. Ontology is a semantic web technology that provides a sophisticated knowledge framework for users and systems to share knowledge, represent knowledge, and achieve interoperability and machine readability. This paper discussed linguistic interpretation that may allow more adaptive and optimized solutions for knowledge sharing between domain ontologies, which is required for better communication in pervasive and ubiquitous computing.
Quantum Privacy

Emerging technologies are changing the world as legal scholars and lawmakers try to keep up. Privacy is an area that been profoundly impacted by the creation and expansion of such technologies. We are now entering the Quantum Age, with quantum technologies promising to be one of the most impactful emerging technologies of the century. Quantum information science and technology (QIST) suggests quantum devices will either replace or supplement many of todayâ€™s technologies. As governments and the private sector race to achieve quantum supremacy, it is crucial for the legal community to understand and analyze how these technologies will impact societies and consumer experiences. This essay offers an overview of how QIST could impact privacy as we know it today. Among the many promises of QIST, this essay studies quantum computers, quantum internet, quantum encryptions, and quantum sensing to understand their potential interference with privacy. It introduces these emerging technologies to legal scholars and provides an overview of how they could potentially impact privacy. The present essay then offers a final novel analytical framework for future scholarly work on QIST privacy impacts and concludes that QIST could have primary and secondary effects on privacy that would both improve and undermine privacy. Understanding the challenges that privacy scholars will soon face and having a framework to work with is crucial for future research in this area.
Transfer Learning for Natural Language Processing

Recent years have witnessed the fast development of natural language processing (NLP). Particularly, the pre-training technique has been playing a key role in common NLP tasks. In this chapter, we show how to perform fine-tuning using the pre-trained language model on a sentence classification task. To save space, we will only introduce the important code snippets in this chapter. For complete code, please refer to the link: https://github.com/jindongwang/tlbook-code/tree/main/chap15_application/nlp .
Comparative analysis of machine learning approaches for sentiment analysis of students' online learning feedback during covid-19

Sentiment analysis aids in determining if a person's feelings are neutral, negative, or positive. Many machine learning and deep learning algorithms exist for assessing people's attitudes on various social media networks. Many researchers focused on students' emotional identification. The purpose of this paper is to analyze the sentiments of academic students regarding the online class experience conducted during the COVID-19 pandemic situation. For this work, the Term Frequency-Inverse Document Frequency (TF-IDF) model is used for the feature extraction and comparison of eight machine learning models were tested for the classification, such as Support Vector Classifier, Multinomial Naïve Bayes, Decision Tree, K-Nearest-Neighbors (KNN), Random Forest, AdaBoost Classifier, Bagging Classifier, Extreme Gradient Boosting Classifier (XGB) and F-Score, accuracy, precision, and Recall are the performance criteria examined. With a test accuracy of 0.97 and precision of 1.0, Multinomial Naive Bayes achieves the highest accurate model.
Transfer Learning for Computer Vision

Today, most of the deep learning algorithms, tutorials, and talks are using computer vision tasks as benchmarks. For instance, the common “Hello world” example of deep learning tutorial is MNIST digits classification and the ImageNet challenge has dramatically boosted the rapid of deep learning. To now, ImageNet is still the common benchmark in many areas.
Privacy Protection Under Incomplete Social and Data Correlation Information

Data reporters have privacy concerns when they are requested to contribute personal data to a data collector. Such privacy concerns are strengthened by data correlation and social relationship, as the data correlation could inevitably cause privacy issues to their socially-connected individuals who even do not report the data. However, both factors are hard to quantify precisely in practice due to their private nature. Such an incomplete information situation poses great challenges for the data reporters to determine their coupled privacy-preserving strategies and for the data collector to choose a proper privacy-preserving mechanism. This motivates us to propose a novel Bayesian game-theoretic framework to analyze the data reporters’ behaviors. We show that the game has a symmetric Bayesian Nash Equilibrium (BNE) with a threshold structure, which builds a connection between the data reporter’s action and privacy concern under incomplete information. The complicated relationship between the BNE and the data collector’s strategy makes it difficult to solve the data collector’s optimization problem. However, by exploiting the unimodal feature of the problem, we present a low-complexity algorithm to compute the optimal privacy-preserving mechanism. Through analytical and numerical studies, we find that the lack of complete information could cause the data reporters to adopt more conservative strategies but make the data collector adopt a less conservative mechanism, resulting in an overall privacy protection degradation. The simulations further demonstrate that the degradation could be alleviated by stronger data correlation and social relationship, and a higher probability of serious privacy concerns.
Competent methods to shield the SCM from new age blockchain attacks

The interior technology of the blockchain is the decentralized ledger, which can be used to record and secure transaction data across many parties in a blockchain. Cryptocurrencies such as Bitcoin, Ethereum, and other coins can use the blockchain to perform transactions between infinity and unknown parties without the need for an intermediary. A blockchain-based supply chain can support participants in recording price, location, cost, certification, quality, and other significant information. As a characteristic of blockchain, the information available within the blockchain can provide traceability of the material supply chain, reduce losses from the fake and grey markets, improve visibility and agreement over outsourced contract manufacturing, and effectively enhance an organization's position as a leader in responsible manufacturing. This paper proposed a Supply Chain Management (SCM) framework and tested it against various blockchain-based new age attacks, like peer-to-peer network-based attacks and consensus and ledger-based attacks, to analyze the impact of SCM.
Revisiting the modality and redundancy effect in an augmented reality-based language learning environment

This study examines the effect of modality and redundancy in an Augmented Reality (AR) based language learning environment for teaching vocabulary in a foreign language. The study aims to determine the effectiveness of the redundancy and modality principles, examine the relationship between cognitive load, satisfaction, anxiety, willingness, and achievement, identify predictors of achievement, and understand how students perceive AR-based vocabulary learning in three different presentation modes (animation + narration + text, animation + narration, and animation + text). The study used a pre- and post-test control group quasi-experimental design, with 97 Turkish-speaking undergraduate students from the Faculty of Education at a state university participating. The study participants were divided into three groups and practiced using the same AR-based flashcards in three different modalities (animation + narration + text, animation + narration, and animation + text). After completing a short distractive task during the practice session, the participants were given an achievement test. The analysis of the data revealed that while the group that used animation + narration outscored the other two groups, the difference was only significant between the animation + text group and the animation + narration group, which supported the modality principle. However, there was no significant difference between the animation + narration + text group and the animation + narration group, which did not support the redundancy principle. The study also included qualitative data from a focus group interview to support the quantitative findings. The conclusion of the study is that the principles of CTML (Cognitive Theory of Multimedia Learning) should be reconsidered in relation to the use of AR technology in instructional settings.
Analysis of Agricultural Drought Monitoring Based on Vegetation Health Index in East Java Indonesia Using MODIS Satellite Data

The agricultural sector is highly susceptible to drought, heavily relying on rainfall. This study used the Vegetation Health Index (VHI) of MODIS Data as a drought indicator to monitor drought in East Java, Indonesia. The VHI combines the vegetation index and land surface temperature, both critical satellite products for remote sensing of drought. The VHI describes vegetation health using the TCI and VCI indices. The study found that the distribution of drought areas increased by 3.69% between 2017 and 2018, with the highest distribution occurring between July and October. On average, 22.24% of the paddy field area was affected by drought in 2017 and 25.93% in 2018, from a total area of 1.174,586 km2. Moderate drought was the dominant type of drought in the agricultural area of East Java, based on the VHI values obtained between 2017 and 2018.