Reviewerly/exHarmony huggingface dataset:
Compressible Multikey and Multi-Identity Fully Homomorphic Encryption

With the development of new computing models such as cloud computing, user’s data are at the risk of being leaked. Fully homomorphic encryption (FHE) provides a possible way to fundamentally solve the problem. It enables a third party who does not know anything about the secret key and plaintexts to homomorphically perform any computable functions on the corresponding ciphertexts. In 2009, Gentry proposed the first FHE scheme. After that, its inefficiency has always been a bottleneck of the development of practical schemes and applications. At TCC 2019, Gentry and Halevi proposed the first compressible FHE scheme that enables the ratio of plaintext size to the ciphertext size (i.e., the compression rate) to reach <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mn>1</mn><mo>−</mo><mi>ε</mi></math> for any small <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mi>ε</mi><mo>&gt;</mo><mn>0</mn></math> under the standard learning with errors (LWE) assumption. However, it is only a single-key one, where the homomorphic evaluation can only be performed over ciphertexts encrypted under the same key. Compared with single-key FHE, multikey FHE is more practical. Multikey FHE enables ciphertexts encrypted under different public keys to be homomorphically computed without having to decrypt these ciphertexts using their own private keys. In addition, in a multi-identity FHE scheme, only identity information and public parameters are required when encrypting, which simplifies certificate-based key management in public key infrastructure. In this paper, a new compressible ciphertext expansion technique is proposed. Then, we use this technique to construct a compressible multikey FHE scheme and a compressible multi-identity FHE scheme to overcome the bottleneck of bandwidth inefficiency in the multikey and multi-identity settings. The two schemes proposed in this paper make it possible that the objects of homomorphic operation can be the ciphertexts encrypted under different keys or different identities before compression, thus solving the single-key defect of the work of Gentry and Halevi.
Remote classroom action recognition based on improved neural network and face recognition

In recent years, the field of computer vision is promoted by the development of intelligent technology and computer technology, and has made breakthrough progress. Intelligent hardware technology and computer technology lay the foundation for the development of computer vision field. At the same time, the continuous improvement and development of artificial intelligence technology has also promoted the rapid development of educational video system, and the video tracking of educational video system has made breakthrough progress. By fully using intelligent hardware and computer technology, and combining with artificial intelligence technology, the video tracking and recognition technology of educational video system has been further developed, and new recognition algorithm has been adopted. The accuracy of tracking recognition is greatly improved, which can accurately identify the action of the characters. At the same time, through the use of new action recognition algorithm, not only improve the accuracy of educational video recognition, but also improve the speed of recognition, which can accurately capture the changes of people’s behavior in the classroom. The time consumed by the action recognition algorithm is very short, and the speed of the algorithm is very high. This new algorithm greatly improves the efficiency of the education recording and broadcasting system, and improves the accuracy and accuracy of the education recording and broadcasting system. This paper studies a set of intelligent image recognition system for students’ classroom behavior. It compiles and explains the intelligent system software systematically. The operation of this system is no single. It operates through the joint operation of many modules. It can realize online distributed homework, accurately and quickly identify students’ classroom behavior, and can also help students to identify their classroom behavior accurately and quickly. The classroom behavior of the accurate analysis of students’ incorrect classroom behavior to make timely reminders, greatly improve the efficiency of the classroom, improve the degree of concentration of students. In this paper, many classroom behaviors are simulated, and the performance of this software platform is predicted through many experiments.
Kernel-based models for influence maximization on graphs based on Gaussian process variance minimization

The inference of novel knowledge, the discovery of hidden patterns, and the uncovering of insights from large amounts of data from a multitude of sources make Data Science (DS) to an art rather than just a mere scientific discipline. The study and design of mathematical models able to analyze information represents a central research topic in DS. In this work, we introduce and investigate a novel model for influence maximization (IM) on graphs using ideas from kernel-based approximation, Gaussian process regression, and the minimization of a corresponding variance term. Data-driven approaches can be applied to determine proper kernels for this IM model and machine learning methodologies are adopted to tune the model parameters. Compared to stochastic models in this field that rely on costly Monte-Carlo simulations, our model allows for a simple and cost-efficient update strategy to compute optimal influencing nodes on a graph. In several numerical experiments, we show the properties and benefits of this new model.
Distributed Time-Based Local Community Detection

The analysis of large time evolving networks poses many challenges due to the immense amount of information available which implies that discovering smaller substructures allows further visualization and analysis that would otherwise be infeasible to compute. Nevertheless, most real-world graphs are constantly changing. Thus their study becomes more challenging. This work addresses the topic of local community detection in dynamic networks, a problem that has recently drawn significant research interest, using PHASR algorithm modified in a way to fit distributed processing standards. In time evolving networks, the challenge is not only to extract the nodes that form the community, but also to find the time interval that spans it’s existence. Some real world cases of local community detection are detecting communities in gene networks to study diseases, in social networks to examine the evolution of friendships and in terrorist networks to reveal terrorist groups.
Character Recognition of Offline Handwritten Marathi Documents Written in MODI Script Using Deep Learning Convolutional Neural Network Model

One of the major research areas in data science is deep learning. Convolutional Neural Networks play a huge role in the recent success of the artificial intelligence-based applications. Cursive character recognition is still an active research problem from many years. This paper deals with recognition of offline handwritten Marathi documents written in MODI script using deep learning convolutional neural network model. The algorithm explained in this paper makes use of Rectified Linear Unit neuron activation function in the convolutional layers as it gives better performance and computationally least expensive and softmax neuron activation function in its fully connected layer. The max pooling layer is used for feature detection. The model designed uses 32 convolutional filters of size 3 × 3 × 1. The model developed is trained by using more than 40000-character samples belonging to 31 different class labels. The trained neural network is tested on more than 10000-character samples from 31 different class labels, giving an accuracy of more than 60%.
Learning to Detect Local Features Using Information Change

In this paper we propose that features extracted from deep convolutional neural networks have the structure and information necessary to detect location and scale of the local keypoints. Unlike the previous supervised and unsupervised methods, we define a local feature as an outcome of information change across different receptive fields around an image region. Exploiting the existent representation hierarchy in the deep convectional neural network, we propose a trainable information accumulation pyramid that allows us to relate the change in the receptive field with information change. The network is trained in an unsupervised fashion by applying random set of transformations over the images and minimizing the covariant loss. We demonstrate the efficacy of our proposed keypoint extractor by evaluating its performance for repeatability and matching scores. Our approach results in 3.7% and 5.2% higher over the state-of-the-art algorithms in repeatability and matching score, respectively.
A systematic review and meta-analysis of flipped learning among university students in Korea: Self-directed learning, learning motivation, efficacy, and learning achievement

Purpose: This study aimed to provide a systematic review and meta-analysis of research on flipped learning effects applied to university students.Methods: The random effect model was applied to 21 papers to calculate the effect size. To verify the moderation effect, a meta regression analysis and meta ANOVA were performed. Publication bias was verified through a funnel plot, and then an Egger's regression test was conducted.Results: The overall average effect size was .69 (95% CI: .51-.87), showing a median effect size, which was statistically significant. The outcome variables were in the order of learning motivation (Hedges’ g=.83), self-directed learning (Hedges’ g=.78), learning achievement (Hedges’ g=.66), and efficacy (Hedges’ g=.50), which were statistically significant.Conclusion: Flipped learning was found to be statistically significant in improving self-directed learning, learning motivation, efficacy, and learning achievement amng university students. It is suggestd that this method be actively applied in university education.
Pocket book based on Android: Physics learning practice media in the 21st century

The student needs easy and engaging learning media to study independently to meet the 21st century demands. Technological developments encourage renewal in the learning process. This research aims to produce a compact book as a practical medium to learn physics independently. The research method used was Research and Development with Lee & Owens model development process. The Android-based pocket book contains short materials, learning videos, sample questions, and interactive evaluation issues that are packaged in a mind map form. The resulting product is tested for the feasibility of the material, learning media and software by experts using a valid and reliable four-scale instrument. From the aspects of software engineering, aspects of media and aspects of learning materials, the resulting pocket book was declared feasible as a medium for learning physics independently.
Improving Language Translation Using the Hidden Markov Model

Translation software has become an important tool for communication between different languages.People's requirements for translation are higher and higher, mainly reflected in people's desire for barrier free cultural exchange.With a large corpus, the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation, ignoring context.To support the ongoing improvement of translation methods built upon deep learning, we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation, our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set, our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation, avoids the combination of invalid words, and enhances the readability and meaning of the resulting translation.
Usage of the Java Language by Novices over Time: Implications for Tool and Language Design

Java is a popular programming language for teaching at university level. BlueJ is a popular tool for teaching Java to beginners. We provide several analyses of Java use in BlueJ to answer three questions: what use is made of different parts of Java by beginners when learning to program; how has this pattern of use changed between 2013 and 2019 in a longstanding language such as Java; and to what extent do beginners follow the specific style that BlueJ is designed to guide them into? These analyses allow us to see what features are important in object-oriented introductory programming languages, which could inform language and tool designers -- and see to what extent the design of these programming tools can have an effect on the way the language is used. We find that many beginners disobey the guidelines that BlueJ promotes, and that patterns of Java use are generally stable over time -- but we do see decreased exception use and a change in target application domains away from GUI programming towards text processing. We conclude that programming languages for novices could have fewer built-in types but should retain rich libraries.
Research Direction of Smart Home Real-time Monitoring

This research introduces six different applications of Smart Home Real-time Monitoring technology, specifically on their implementations. The ultimate goal of the real-time monitor research is to use smart home technology to provide a better living environment for people in the future. This research provides five models, namely: Sleep Monitoring System Using Kinect Sensor; Smart Home Energy Management System; Smart Home-Based Health Platform for Behavioural Monitoring and Alteration; Distributed smart sensor system for indoor climate monitoring; Innovative Smart Home System Powered by Botanical IoT and Emotion Detection. Smart home real time monitoring represents an important first step in the field of smart environment-based health monitoring and assistance, energy management system and indoor environment management system as well. The architecture can be used to monitor the behaviour of the residents, for example, activity, diet, and exercise compliance and evaluate the effects of alternative medicine and behaviour regimens. Also, this architecture can be used to maintain the comfort state of the indoor environment, which is safe, convenient, environmental-friendly and healthy. We believe these technologies are essential for providing accessible, low-cost life assistance in an individual's own home and for providing the best possible quality of life for individuals in the near future.
Synthesis of active-RC filters using genetic algorithms

Traditionally, analogue filter designers synthesise their circuit topologies using pencil-and-paper method. Based on their previous experience, they generally use their intuition to reach proper network configuration for the desired filter function. However, this approach might be very time-consuming and cumbersome. With the advent of computer technology, the generation of analogue filters (or any other types of circuit for that matter) can be done using software. Taking advantage of this high computing power, some evolutionary methods, like genetic algorithms (GAs), can be employed to obtain the desired circuits sophistically, which generally leads reaching the goal faster. In this paper, automatic synthesis of filters utilizing GAs is presented. Although the approach can be applied to all analogue filters, this paper specifically focuses on active-RC filter topologies, which compose of an active element, e.g. operational amplifier, and resistors and capacitors. Some of the filter topologies that are generated by the constructed software are presented. Experimental results regarding to GA performance are also included.
Knowledge Extraction via Decentralized Knowledge Graph Aggregation

In many industrial manufacturing processes, human operators play a central role when it comes to parameterizing the involved machinery and dealing with errors in the process. However, large parts of the acquired process knowledge are tacit, leading to difficulties sharing the knowledge between operators. Therefore, knowledge extraction is a necessary but time and cost intensive process, requiring both specially trained personnel and experienced operators. In contrast, we propose that by gathering insights into what influenced operators' actual parameter choices, tacit process knowledge can be extracted during production in an example-based manner. This decentralized knowledge-decentralized in regards to who holds knowledge and where it was extracted-is then aggregated to a coherent knowledge graph. We showcase our methodology on a real-world dataset in the domain of fused deposition modeling (FDM), which is generated by operators providing their insights without additional assistance using extended human machine interfaces. Furthermore, we compare rules extracted from the aggregated knowledge graph against an established FDM knowledge base showing the viability of our approach even with limited amounts of data.
Twitter Emotion Analysis for Brand Comparison Using Naive Bayes Classifier

Every brand created by the company plays a vital role in its recognition, differentiation and sustaining loyal customer base. These brands are analysed at different stages of their life cycle to understand their personality, sentiments and emotions that are perceived and expressed by their customers, thereby helping the companies to achieve a positive brand image. Real time experience data about brands are available in abundance in the form of short texts on Twitter. In this paper, based on Plutchik’s wheel of eight primary emotions-joy, sadness, trust, fear, surprise, disgust, anger and anticipation, we have proposed a brand comparison tool which carries out emotion analysis on the tweets with the help of Synonym based Naive Bayes retry approach, thus quantifying emotions from tweets about the brands. The tool has achieved an overall accuracy of about 82.5%. This brand comparison tool helps companies to understand their strengths and shortcomings among their competitors. The feedback from this tool can be used to realign the marketing strategies and goals of the companies.
Supply chain finance and trade finance

This chapter provides a detailed analysis and description of blockchain-led transformation that is likely to occur in supply chain finance and trade finance (TF). We discuss the current challenges such as financing and look at blockchain’s potential to address these challenges. It tries to describe how blockchain facilitates small firms’ access to supply chain and TF by providing lenders with high-quality and relevant data about potential borrowers and their activities. It addresses how blockchain can help meet regulatory standards in supply chain and TF. It also probes the influence of blockchain in fighting fraudulent practices. Case studies of some blockchain-based TF solutions have been used to illustrate these issues.
Anonymous Information Transmission Method based on Dual Cloud Structure

The transmission of anonymous information on the Internet is a research hotspot in the field of data privacy protection. On the basis of studying traditional information security (the confidentiality, integrity and authenticity of transmitted data), scholars are paying more and more attention to how to protect the identity information of communication users. That is, the anonymity of transmission. In response to the new needs of network communication for information security, this article conducts in-depth research and analysis of the most successful anonymous communication system Tor, and analyzes the Tor network protocol, the status quo and the challenges it faces; and based on this Improved, designed an anonymous information transmission method based on the dual-cloud structure in response to the problems of Tor, and conducted performance analysis and testing of the system.
Analysis for Augmented Reality's Effects on Tourists’ Satisfaction: Lantern Festival in Taiwan

In addition to extending traditional customs, the Lantern Festival of Taiwan is aimed to attract more tourists, showing cultural connotation. Recently, augmented reality creates more chances for tourism. With the help of augmented reality, tourists can access more information and knowledge on the telephone, which increases interaction of experiential learning and effect of study tour. In an individual case of Lantern Festival of Taipei entitled "Praying Together to Safeguard Taipei. Exploring This City ' s Hundred Years of Glory" in 2020, we explore the audience feedback for application of augmented reality to festival activities through questionnaire. According to this study, combing the traditional lantern process show with augmented reality cannot only create a new experience for tourists, but also improve tourists' interests in traditional lantern.
Incremental Multi-Feature Tensor Subspace Learning Based Smart Traffic Control System and Traffic Density Calculation Using Image Processing

In times, urban centers are growing at a high rate. Growing with them is a road traffic jam. Traffic jams, especially at peak hours, became routine. As a result, traffic management is one of the foremost pressing issues in today's towns. Several alternatives are being sought to affect the matter. These include expanding road networks, regulating the number of vehicles on the roads, and deployment of Intelligent Transportation Systems (ITSs). Aside from the ITSs, the opposite alternatives (however significant) have many practical challenges in their implementation. ITSs have supported a good range of technologies like loop sensors and video surveillance systems. Vision-based ITSs have proved advantageous over the standard methods supported loop sensors. In these modern systems, video surveillance cameras are installed along the roads and road intersections where they're wont to collect traffic data. The info is then analyzed to get traffic parameters like road traffic density. This paper presents a comfortable and stylish approach for estimating the road traffic density during daytime using image processing and computer vision algorithms. The video data collected is first weakened into frames, which are then preprocessed during a series of steps. Finally, the vehicles are detected and extracted from the pictures and Density estimated. The traffic density is then obtained because of the number of vehicles per unit area of the road section. The proposed approach was implemented in MATLAB R2013a and average vehicle detection accuracy of 96.0% and 82.1% were achieved for fast-paced and slow-moving traffic scenes.