Reviewerly/exHarmony huggingface dataset:
Combining Pre-Trained Language Models and Features for Offensive Language Detection

Nowadays, people often express their abusive and offensive thoughts to others on social media easier. The abusive and toxic comments hurt others seriously. Therefore those abusive and toxic comments should be detected properly through natural language processing. In this paper, we focus on two types of features in offensive language: word-level and sentence-level fea-tures. We use lexicon-based and standard bag-of-words features as the word level. We introduce BERT-based and DeepMoji-based features as the sentence level. We apply the four features to a machine learning approach: support vector machines. We evaluate the method using the combinations of four features with a dataset, Curious Cat. The best F1 score was generated by the method with all features. This result shows the effectiveness of our proposed method. In addition, the experimental result indicates that DeepMoji generated from Twitter data is better than BERT which is generated from written language, for an offensive language detection task about social media data.
Automatic extension of editing rules for program analysis during creation process

This paper focuses on edits of a program that a programming learner is creating. The proposed method in this paper defines EAST (Extended Abstract Syntax tree), an extension of AST (Abstract Syntax Tree), to analyze programs subject to non-compilable edits. EAST adds editing rules to ASTs, and the proposed method automatically creates them from a given set of ASTs. EAST helps define the learner's program editing behavior as editing on a tree structure. The proposals in this paper provide a foundation for future research that will contribute to a more detailed study of improving the learning efficiency of program learners.
Skeleton Neural Networks via Low-Rank Guided Filter Pruning

Filter pruning is one of the most popular approaches for compressing convolutional neural networks (CNNs). The most critical task in pruning is to evaluate the importance of each convolutional filter, such that the less important filters can be removed while the overall model performance is minimally affected. In each layer, some filters may be linearly dependent on each other, which means that they have replaceable information. Redundant information can be removed without significantly affecting information richness and model performance. In this paper, we propose a novel low-rank guided pruning scheme to obtain skeleton neural networks by alternatively training and pruning CNNs. In each step, training is performed with nuclear-norm regularization to low-rank the filters in each layer, followed by filter pruning to maintain the information richness via the maximally linearly independent subsystem. A novel “smaller-norm-and-linearly-dependent-less-important” pruning criterion is proposed to compress the model. The training and pruning processes can be repeated until the model is fully trained. To investigate the performance, we applied the proposed joint training and pruning scheme to train the CNNs for image classification. We considered three benchmark datasets: MNIST, CIFAR-10 and ILSVRC-2012. The proposed method successfully achieved a higher pruning rate and better classification performance compared to state-of-the-art compression methods.
A Novel Blockchain-Assisted Aggregation Scheme for Federated Learning in IoT Networks

With the wide range of Internet of Things (IoT) applications, federated learning (FL) is commonly adopted to protect the privacy of IoT data. FL enables privacy-preserving model training while keeping the data locally available. To alleviate the additional load caused by FL, an improved hierarchical aggregation framework is presented in this article to decentralize the model aggregation tasks based on end-device clusters. However, when applying FL to IoT networks, how to keep high efficiency and reliability remains open challenges due to a large number and vulnerability of IoT end devices. In this article, we propose a blockchain-assisted aggregation scheme for FL in IoT networks, where the aggregation node selection is applied for efficiency improvement as well as blockchain for performance verification. During model aggregation, a selection strategy is obtained by the deep deterministic policy gradient (DDPG) algorithm and aims to select the optimal subset of IoT end devices based on multiple metrics. Furthermore, a new performance verification based on the characteristics of blockchain is applied to achieve mutual verification among a number of untrustworthy nodes with the optimal stopping theory, which provides reliable model performance proofs. Simulation results show that the proposed scheme can maintain FL efficiency and reduce the system latency while protecting data privacy.
Exploring the Use of Multiagent Systems in Educational Robotics Activities

Multi-Agent Systems (MAS) have been demonstrated to be a useful tool for addressing complex problems by breaking them down into simpler tasks. In this paper, we explore the potential of the MAS approach in designing educational robotics learning experiences. Specifically, we aim to identify the pedagogical possibilities and technical considerations of using MAS in designing robot-based activities. We have designed three educational robotics experiences based on the MAS approach. The first experience describes the use of the MAS approach in creating a learning activity based on Multi-Robots Systems (MRS). The second experience describes the use of the MAS approach for implementing theater-based activities with MRS. Finally, the third experience outlines the implementation of an artificial cognitive architecture based on MAS to control the educational actions of an anthropomorphic social robot. The results reveal that MAS and MRS offer opportunities to design distributed learning environments with distributed artificial intelligence.
HANM: Hierarchical Additive Noise Model for Many-to-One Causality Discovery

Discovering causal relationships among observed variables is a new research focus in the area of data mining. Methods based on the additive noise model have been proved to be efficient in the identification of cause-effect pairs. However, when trying to determine many-to-one causality, additive noise models often fail to identify the causal direction due to the complex interrelationships and interactions even though the generation of each causal relation follows the additive noise model, and become unreliable in practical applications. In this work, to identify the causal direction, we propose a Hierarchical Additive Noise Model (HANM) to convert many-to-one causality into an approximate one-to-one causality by generalizing multiple factors into an intermediate variable with a variational approach, and use asymmetry in the forward model and backward model of HANM to identify causal direction. Experiments using synthetic data show that many-to-one causality can be effectively identified through asymmetry with our proposed HANM and the accuracy of HANM is higher than the best existing model. By applying the model to real-world data, it can be seen that HANM can greatly augment the application scope of functional causal models for causal discovery.
Optimizing Multidimensional Perceptual Quality in Online Interactive Multimedia

Network latencies and losses in online interactive multimedia applications may lead to a degraded perception of quality, such as lower interactivity or sluggish responses. We can measure these degradations in perceptual quality by the just-noticeable difference, awareness, or probability of noticeability ($p_{\text{note}}$pnote); the latter measures the likelihood that subjects can notice a change from a reference to a modified reference. In our previous work, we developed an efficient method for finding the perceptual quality for one metric under simplex control. However, integrating the perceptual qualities of several metrics is a heuristic. In this article, we present a formal approach to optimally combine the perceptual quality of multiple metrics into a joint measure that shows their tradeoffs. Our result shows that the optimal balance occurs when the $p_{\text{note}}$pnote of all the component metrics are equal. Furthermore, our approach leads to an algorithm with a linear (instead of combinatorial) complexity of the number of metrics. Finally, we present the application of our method in two case studies, one on VoIP for finding the optimal operating points and the second on fast-action games to hide network delays while maintaining the consistency of action orders.
Analysing the Effect of Quantum Network Interconnect on the Performance of Distributed Quantum Computing

We investigate how a dynamically reconfigurable network supports short-range distributed quantum computing in a data center scenario by analysing the penalties and benefits of adopting such architecture in the NetSquid platform utilising two use cases.
Reflections on teaching and learning issues of integrated education in China based on UDL concept

Universal Design for Learning (UDL) has the potential to provide equal learning opportunities to various kinds of learners, which not only help address differential treatment in segregated education but also provide students with the same quality education. This study compares the current problems of integrated education in China in order to explore the necessity and countermeasures for the application of UDL in integrated education in China.
Analysis the character of J.S.Bach’s Sinfonia in A major (BWV 798) – based on the dynamic and research on related acoustic parameters

Various annotations of the character of J.S.Bach’s clavier music in piano perform seem to be under discussion by the pianists and musicologist. However, the extent of dynamic and the way of dynamic changes that had been considered appear scarce. In the essay below, the extent of the dynamic and the way of dynamic changes of J.S.Bach’s Sinfonia in A major (BWV 798) in piano perform is under analysis and discuss.
Information Teaching in Computer-Aided Design Course

In view of the characteristics of computer-aided design courses and the particularity of teaching objects, information teaching is integrated into the teaching process, and a series of reform measures related to teaching content, teaching methods, practical teaching and assessment methods are proposed, and practice shows that students’ enthusiasm for learning is greatly improved and the teaching effect is significantly improved.
Cultural Heritage Game Design Based on the Collective Memory Reconstruction Model

Current methods for constructing collective memory of urban cultural heritage mainly rely on landscaping and museum exhibitions, however, these methods lack engagement and fun, making it difficult for people to form deep memories and a sense of place for the city. This paper proposes an urban cultural heritage game based on the collective memory reconstruction model. Combining location-based service and augmented reality technologies, the game provides a firstperson gaming experience with narratives and levels correlated with historical and cultural attractions in the Nantou Ancient City, Shenzhen. Subsequently, a controlled experiment was conducted to verify the effectiveness of the game design. A total of 25 users participated in the experiment. Results show that the proposed design can enhance players’ touring experience, cultural perception, and place attachment. This paper explores the theory and application of collective memory reconstruction and provides an empirical reference for cultural heritage game design.
Excellent Traditional Culture Entering College Campus: Mechanism Innovation and Practical Strategies

In the context of the excellent traditional culture entering the campus, the inheritance education of traditional culture in vocational colleges is faced with the present situation of congenital deficiency and the lack of strength, which is embodied in the lack of attention and awareness of traditional culture. Taking a traffic vocational college which has implemented traditional culture education for many years as an exemplar, this paper probes into the mechanism innovation and practical strategy of excellent traditional culture in vocational colleges, so as to provide reference for the inheritance of traditional culture in vocational colleges.
“Production-Oriented Approach” and New technology in Vocational Chinese Teaching

The idea of “Production-Oriented Approach” (POA) is to solve the disadvantage of “separation of learning and application”. This paper analyzes how to do each process and puts forward specific teaching strategies in vocational Chinese teaching. It includes workplace case analysis, dilemma and demand orientation, disciplinary integration, student immersion participation. New technology can make learning more diverse. It includes the use of modern media technology, development of three-dimensional intelligent teaching materials and development of professional web learning resource library. POA connect with new technology can improve vocational Chinese teaching.
Study on the Application of Task-Based Language Teaching in Middle School Oral English Teaching in China

Speaking is an essential aspect of English learning. It reflects the student’s ability to express and comprehensively use the language. In the 21st century, international exchanges have become increasingly frequent. Therefore, English is more important as a tool of international communication. Despite this, under the long-term pressure of examinations, English teaching in our country only focuses on reading, writing, and other content closely related to testing scores, while oral English teaching is easily neglected. Even in the classrooms where oral English teaching is implemented, the teaching activities and methods are relatively monotonous and out of touch with the actual situation, resulting in students’ low enthusiasm for oral English and unwillingness to speak English. In order to solve this problem, this paper studies an effective oral English teaching method --- Task-Based Language Learning (TBLT). This teaching method abandons the disadvantages of the traditional oral English teaching method and better improves students’ oral English ability. TBLT is a teaching method that gradually emerged and developed in the 1980s. In language teaching, TBLT requires language teaching based on tasks, and students should learn the language while completing the tasks. TBLT allows students to use English to complete various actual tasks related to life, study, and work, making classroom teaching real and task-oriented and cultivating students’ ability to use English in practice.
Innovative application and optimized governance of blockchain in the media industry

Blockchain technology was originally used in the financial industry, and was applied to Bitcoin as the underlying technology and infrastructure. At present, the value of application has expanded from the financial industry to other industries such as transportation, medical care, and media. Then the article focuses on the possibility of transfer of new value and social collaboration of blockchain, the application and practice in the field of cultural media, and the problems faced by the development of "blockchain + cultural media", together with discussing the innovative application of blockchain in the media industry.
Causes for Public Figures to Experience Cyber Violence and Prevention Measures

Most of the performances of public figures are watched by the public, and people like to comment on these celebrities on the internet. However, a lot of critical remarks mean that cyber violence occurs. As a result, most public figures who experienced cyber violence suffered from depression. This paper aims to mitigate the negative effects of cyber violence. Through the study of the behavior of victims and abusers, the purpose of this paper is to find out the causes of cyber violence and analyze the effective methods to prevent cyber violence according to the causes. After the study, the causes of cyber violence include two aspects. One reason is that abusers are jealous of public figures whose lives and jobs seem so easy to succeed. Moreover, these abusers do not have a good way to release their stress, and they can only choose to attack others online to satisfy their needs. Another reason is that victims do not like to communicate with others like professional psychologists, and they are not confident at all. To prevent cyber violence, the country can enact laws to publish people who comment aggressively online and provide free psychological counseling for the public. For public figures who will experience cyber violence easily, they should go to counseling regularly and talk to others. If cyber violence is reduced, the whole network environment and even the social environment will be better.
Exploring the Safe Harbor Principle and the Allocation of Platform Liability in the DMCA

The Digital Millennium Copyright Act's safe harbor clauses, which was established more than 20 years ago, are no longer able to reconcile the current widespread problem of copyright infringement in social media. Therefore, this paper examines the allocation of responsibilities between social media platforms, users and copyright owners in the event of digital copyright infringement in social media, the negative impact of the DMCA provisions on stakeholders in social media and possible solutions to the problem. The main problem with the current DMCA's safe harbor doctrine is that the exemptions provided shift too much responsibility and liability to copyright owners and social media users, fail to provide effective remedies, and place an extremely heavy burden on them to enforce their rights. This has had a negative impact on the development of social media and creative output. For this reason, Congress need to change the DMCA's upregulating to increase the bar for safe harbor designation and impose additional requirements on platforms. In order to more effectively contribute to the growth of the social media business and the preservation of digital copyright pairs in the field of practice, this study primarily adopts a legal doctrinal research technique, comparative research approach, and literary research method.
Modified Hill Cipher with Invertible Key Matrix Using Radix 64 Conversion

Cryptography is one of the most important technological areas which will provide security to the data by using various cryptographic algorithms. The Hill cipher (HC) is a known symmetric encryption algorithm using linear matrix transformation. In spite of the simplicity of Hill cipher algorithm, there are several advantages such as masquerading letter frequencies of the plaintext and high throughput. The traditional Hill cipher founds a serious setback due to the vulnerability against known plaintext–ciphertext attacks. So, to enhance security, a new variant of the Hill cipher method is proposed. The proposed algorithm develops in three stages, firstly, convert the plaintext into 8-bit binary form; in the second stage, we remove the last two bits from MSB positions as they are 0 in the MSB and 1 in the MSB-1 position, respectively, and this reduces the size of the data. Now, apply Radix 64 conversion to each row; in the last stage, we perform the traditional Hill cipher algorithm (modulo 64), where we use the invertible matrix as key, and this produces the ciphertext. The proposed technique reduces the size of ciphertext data after encryption. The security analysis of the modified algorithm using avalanche effect shows three-fold increase than comparing to tradition Hill cipher algorithm. Further, the data size dependence on the performance of the modified Hill cipher algorithm is also addressed and found that the encryption time is increased with increase of data size.
An Ensemble for Attendance Management with Face Visualization and Recognition Using Local Binary Pattern Histogram

Identifying a person and recognizing their individuality has been the most important necessity in any community or organization. So, attendance management is a necessary tool in any environment where maintaining attendance is required. Automation is always an easier way to upgrade the existing approach, in performance, efficiency, and robustness. This project is aimed at developing a less intrusive, cost-effective, and more efficient automated attendance management system. The system is based on face detection and recognition algorithms, which automatically detects a person through a walk-in or an image of an individual or a group. The system comports the given image with the pre-registered data and confronts the lateral procedure. The system uses Local Binary Pattern Histogram Algorithm with Haar cascade classifier to identify the image through its nodal preference to downscale, detect and differentiate the image. The produced data of the attendance is managed and automatically updated in the database and is stored in an excel sheet. Different real-time scenarios were taken into account to evaluate the performance of the system. The project also has measures implemented to handle the threats like spoofing.
A Digital Steganography Technique Using Hybrid Encryption Methods for Secure Communication

As the era moves forward, so does the advancement and progress. The advancement of medical science has been accompanied by some challenges. Due to this, another challenge arises: Sharing data over public networks. This requires security measures for the data to be sent securely. One way to do this is with steganography. Steganography is the process of hiding secret information from hackers by placing it in other publicly available data. A second approach to adding security is hybridizing steganography with encryption techniques. In this implementation the 4 main pillars of steganography are considered payload capacity, imperceptibility, robustness, and security. For maintaining all these pillars different algorithms have been hybridised and one novel algorithm has been constructed. First of all, DWT (Discrete Wavelet Transformation) technique and BES (Bald Eagle Search) algorithm have been used together for embedding and pixel selection process. After that Huffman compression technique and logistic chaotic technique have been used together on the secret image before embedding it into cover image. In this the database of Covid-19 patients is used as the confidential data and the results that this model has performed better than the existing ones.
WaterCrypt: Joint Watermarking and Encryption Scheme for Secure Privacy-Preserving Data Aggregation in Smart Metering Systems

As the world is embracing the new era of smart technologies and the development of IoT equipment, such as smart grid and metering systems, a significant concern related to the privacy and security of user’s confidential information is forming rapidly. Many of the existing solutions are still suffering from plenty of time and power consumption, and security vulnerabilities. Secure data aggregation in smart metering systems is still a challenging task due to a plethora of attainable cyber and physical attacks. This paper presents the novel WaterCrypt technique based on reversible watermarking and Paillier encryption that significantly reduces the battery consumption of resource constrained smart meters by introducing a unique Encryption Server as well as making use of the homomorphic encryption properties to secure real time data transmission. In addition, the reversible watermarking technique used in the protocol guarantees the integrity and authenticity of data. The experimental results show that the proposed scheme offers a privacy friendly, secure data transmission and aggregation solution in smart metering systems in a cost effective manner.