Reviewerly/exHarmony huggingface dataset:
Semi-supervised Multi-Source Transfer Learning for Motor Imagery Recognition

In the field of motor imagery (MI) recognition, poor generalization and low recognition performance are major challenges. An MI recognition method based on semi-supervised learning and multi-source transfer learning is proposed. In this approach, samples are transferred from some source domains to the target domain using the multi-source transfer learning method. The source domains selection method based on distribution similarity is designed to select source domains with similar distribution to the target domain, and samples with high information entropy are selected from these source domains for transfer. In this regard, we propose a semi-supervised learning labeling method for labeling the unlabeled samples of the target domain, which utilizes the labeling information from a few labeled samples without increasing the labeling cost. The sample confidence measurement method and the dynamic adjustment mechanism are proposed to ensure labeling accuracy and minimize the influence of mislabeled samples. A fusion classification model can identify the new sample in the target domain. As a measure of the effectiveness of the proposed method, four types of MI from the BCI Competition IV dataset 2A were used to evaluate the recognition ability, and the outcomes confirmed an excellent recognition performance as well as a superior training efficiency when compared with the currently used methods.
Image Classification Based on Dual-attention Mechanism and Multi-convolution Layer

Image classification has always been the core of many people's research, and it is an indispensable and important technology in daily life. In the classification process, the local information of the image largely determines the global features of the image, so finding local pixel features is an indispensable step in the classification. To improve the accuracy of image classification problems, this paper proposes a new model on the premise of ensuring the model is simple and practical. Based on convolutional neural networks, it combines spatial and channel attention mechanisms, with multi-scale convolution operations to quickly and accurately extract key image information. The model is thus able to well capture the key features in the channel and space that determine the image category, thereby improving the classification accuracy. Through testing on multiple datasets and comparing with some mainstream methods, the accuracy of our model has been greatly improved, proving the effectiveness of our method. On the selected flower dataset, the accuracy rate reaches 77%, which is significantly better than other methods. On the rice dataset, the accuracy rate is almost 100%, which greatly improves the classification accuracy.
Efforts to Imporove Students Reading Comprehension Skills During the Face to Face Learning Period are Limited Through the Speed Reading Method in Elementari School

Lack of interest in reading in students can affect reading comprehension ability. Meanwhile, during the limited face-to-face learning period like today, students are required to be able to understand a reading quickly and precisely. The use of the speed reading method can help students find the main sentence in the reading they are reading. This study aims to: Knowing whether or not there is an influence on students in elementary schools. This study uses a quantitative experimental approach, with a research design using One Group Pretest – Posttest. The sample in this study was the fourth grade students of SDN Kalitengah II, totaling 21 students. Collecting data in this study used pre-test and post-test sheets in the form of description questions. In this study found the results of the analysis as follows. Before using the speed reading method (pre-test) the learning outcomes of students were categorized as very low with the percentage of results less than 60 80.96%, low 19.04%, medium 0% high 0%, very high 0%. After using the speed reading (post-test) method, very low 5.55%, low 0%, moderate 0%, high 77.78%, very high 16.67%. Based on the hypothesis test t Count 4.35 and t Table 1.725, then obtained tCount &gt; tTable 4.35 &gt; 1.725 then H0 is rejected and H1 is accepted. So that the speed reading method affects the reading ability of students.
Learning Multiple Granularity Features for Unsupervised Person Re-Identification

Local features have been shown effective in supervised person re-identification (Re-ID). However, it is hard to obtain workable local features in an unsupervised situation. In this paper, we propose a novel method to learn discriminative multiple granularity features in unsupervised domain adaptation (UDA) Re-ID. Firstly, we propose Multiple Granularity Memory Dictionary Group (MGMDG) to learn discriminative local features and global features respectively by contrastive learning for features of each granularity with the corresponding memory dictionary. Secondly, we design a teacher-student framework in which the teacher model supervises the student model to learn the multiple granularity features with a soft triplet loss. Finally, we use the concatenation of multiple granularity features to generate better pseudo labels for local features. Extensive experimental results show that the performance of the proposed method on Market1501, DukeMTMC, MSMT17 datasets significantly outperforms the state-of-the-art UDA Re-ID methods by a large margin.
Paper Prototyping

Try to create a paper prototype of your product that is interactive (e.g., ability to navigate between screens, open dropdown lists, etc.) without over-engineering it.
Retraction notice to ``The role of innovative approaches in teaching the flute: A path to creative realization of students'' [Thinking Skills and Creativity Volume 42, December 2021, 100959]

This article has been retracted: please see Elsevier Policy on Article Withdrawal ( https://www.elsevier.com/about/our-business/policies/article-withdrawal ). <This article has been retracted at the request of the Editors-in-Chief. After a thorough investigation, the Editors have concluded that the acceptance of this article was based upon the positive advice of one illegitimate reviewer report. The report was submitted from an email account which was provided to the journal as a suggested reviewer during the submission of the article. Although purportedly a real reviewer account, the Editors have concluded that this was not of an appropriate, independent reviewer. This manipulation of the peer-review process represents a clear violation of the fundamentals of peer review, our publishing policies, and publishing ethics standards. Apologies are offered to the reviewer whose identity was assumed and to the readers of the journal that this deception was not detected during the submission process.
History of the Journal of Theoretical and Computational Acoustics

A brief history is given for the journal currently titled Journal of Theoretical and Computational Acoustics.
Cascaded Residual Densely Connected Network for Image Super-Resolution

Image super-resolution (SR) processing is of great value in the fields of digital image processing, intelligent security, film and television production and so on.This paper proposed a densely connected deep learning network based on cascade architecture, which can be used to solve the problem of super-resolution in the field of image quality enhancement.We proposed a more efficient residual scaling dense block (RSDB) and the multi-channel cascade architecture to realize more efficient feature reuse.Also we proposed a hybrid loss function based on L1 error and L∞ error to achieve better L∞ error performance.The experimental results show that the overall performance of the network is effectively improved on cascade architecture and residual scaling.Compared with the residual dense net (RDN), the PSNR / SSIM of the new method is improved by 2.24% / 1.44% respectively, and the L∞ performance is improved by 3.64%.It shows that the cascade connection and residual scaling method can effectively realize feature reuse, improving the residual convergence speed and learning efficiency of our network.The L∞ performance is improved by 11.09% with only a minimal loses of 1.14% / 0.60% on PSNR / SSIM performance after adopting the new loss function.That is to say, the L∞ performance can be improved greatly on the new loss function with a minor loss of PSNR / SSIM performance, which is of great value in L∞ error sensitive tasks.
A Study on the Realization of Virtual History in Korean Web Novels

This paper discussed the realization of virtual history in romance web novels, focusing on &lt;Midnight Mirage&gt;. &lt;Midnight Mirage&gt; depicted the love story with King Munjong and Queen Hyeondeok as motifs. &lt;Midnight Mirage&gt; used the story of Tumun-dong 72 Loyalists and the achievements of various national institutions in King Sejong to create various central event. The fictional elements of the story were used to form a inter-class romance, emphasize the personal feelings of the opposing forces and highlight the power of the crown prince. The characteristics of a virtual history are as follows. First, it was actively borrowing modern cultural customs. Second, it takes the form of a short episode when a historical figure appears. These characteristics have epic effects such as inducing rapid immersion of readers and relaxing the tension of the narrative.
A minimal model of the bar-resonator vibroacoustic interaction in mallet percussion instruments

In some mallet percussion instruments, such as vibraphones and marimbas, tubular acoustic resonators are placed beneath the tuned bars to enhance acoustic radiation at their fundamental frequency. This work deals with the formulation of a minimal physical model describing the vibro-acoustic interaction occurring in these instruments. The fundamental bar mode is modelled as a disk-shaped oscillator and the resonator as a cylindrical acoustic waveguide, described in terms of its acoustic modes. The three-dimensional effects associated with sound radiation and the interaction between the two elements are calculated using a 2-D axisymmetric finite element (FE) model, whose numerical results are then fitted to dimensionless analytical expressions, which encapsulate the complex physics of the vibro-acoustic interaction in a simplified manner. This can pragmatically be used to formulate a lumped-parameter model of the coupled system. Illustrative numerical results are presented and qualitatively validated, demonstrating the ability of the proposed modelling approach to capture the essential features of the coupled dynamics occurring in real instruments.
Security, privacy and compliance

This chapter evaluates blockchain's roles in strengthening cybersecurity, protecting privacy and improving compliance. Using practical applications and real-world examples, it argues that blockchain's decentralized feature is likely to result in a low susceptibility to manipulation and forgery by malicious participants. Since most of the data is currently stored in cloud data centers, it also compares how blockchain performs vis-vis the cloud in various aspects of security and privacy. Key underlying mechanisms related to the blockchain's impacts on the Internet of Things (IoT) security are also covered. From the security and privacy considerations, it highlights how blockchain-based solutions could possibly be, in many aspects, superior to the current IoT ecosystem, which mainly relies on centralized cloud servers through service providers. The chapter also delves into how blockchain can make it possible to contain an IoT security breach in a targeted way after it is discovered. It will discuss how blockchain can address compliance issues such as Know Your Customer (KYC) Anti-Money Laundering (AML) and Counter-Terrorism Financing (CTF). Special consideration is also given to how blockchain-based identity and access management systems can address some of the key challenges associated security, privacy and compliance.
Genetically modified games

Genetic programming is the practice of evolving formulas using crossover and mutation of genes representing functional operations. Motivated by genetic evolution, we introduce and solve two combinatorial games, and we demonstrate some advantages and pitfalls of using genetic programming to investigate Grundy values. We conclude by investigating a combinatorial game whose ruleset and starting positions are inspired by genetic structures.
Human-AI Protocols for Cloud Data Management

AbstractIn this paper a hybrid i.e. human-AI procedures will be defined for intelligent and secure data management in the Cloud. Such procedures will be useful in creation a new solution oriented on application of personal features in security technologies. User oriented data management can facilitate the secure information distribution over hierarchical structures and distributed networks. This also allow to increase efficiency and security of strategic data management procedures.KeywordsHybrid proceduresHuman-AI protocolsData securityPersonal features
Towards a Formal Language in Law

In today's globalized society, the generation of digital information grows exponentially along with the technological innovations in all aspects of everyday life. In order to cope with gathering, administering, sharing information, planning, and automatization of many human activities including some human reasoning tasks, multi-agent systems in the form of language programming scripting have become an important aspect in dealing with those problems. Thus, the agent-based programming language has become an important line of research in artificial intelligence from a theoretical and practical point of view. In this chapter, the authors review some already implemented languages and theoretical aspects toward a formal proposal of a language that deals with human reasoning in law. In particular, the authors present as an example a language, based on topoi theory, that deals with the problem of defining ontology as specified by FIPA as a requirement for agent-based programming language.
Image sensing with nonlinear optical neural networks (Conference Presentation)

In conventional approaches to computer-vision tasks such as object recognition, a camera digitally records a high-resolution image and an algorithm is run to extract information from the image. Alternative image-sensing schemes have been proposed that extract high-level features from a scene using optical components, filtering out irrelevant information ahead of conversion from the optical to electronic domains by an array of detectors (e.g., a CMOS image sensor). In this way, images are compressed into a low-dimensional latent space, allowing computer-vision applications to be realized with fewer detectors, fewer photons, and reduced digital post-processing, which enables low-latency processing. Optical neural networks (ONNs) offer a powerful platform for such image compression/feature extraction in the analog, optical domain. While ONNs have been successfully implemented using only linear operations, which can still be leveraged for computer-vision applications, it is well known that adding nonlinearity (a prerequisite for depth) enables neural networks to perform more complex processing. Our work realizes a multilayer ONN preprocessor for image sensing, using a commercial image intensifier as an optoelectronic, optical-to-optical nonlinear activation function. The nonlinear ONN preprocessor achieves compression ratios up to 800:1. At high compression ratios, the nonlinear ONN outperforms any linear preprocessor in terms of classification accuracy on a variety of tasks. Our experiments demonstrate ONN image sensors with incoherent light, but emerging technologies such as metasurfaces, large-scale laser arrays, and novel optoelectronic materials, will provide the means to realize a variety of multilayer ONN preprocessors that act on coherent and/or quantum light.
Text-to-Face Generation via Multi-Modal Attention Memory Network with Fine-Grained Feedback

Over the past few years, a lot of works have explored on Text-to-Image (TTI), which aims to generate images from text descriptions. Compared with Text-to-Image (TTI), Text-to-Face (TTF) is a subtopic with greater challenges but meaningful, and less of the related studies. The text description of the face is more abstract and complex, which leads to poor quality generated face images and a low level of semantic consistency between text description and face images. As a solution to this problem, we propose a Multi-Modal Attention Memory Network with Fine-Grained Feedback. The proposed method uses a Multi-Modal Attention Memory Network to refine the face image features continuously and to improve the quality of the resulting face image. And the word-level discriminator is designed to establish the correlation between words and facial attributes, providing the generator with fine-grained training feedback. In order to verify the effectiveness of our method, we have performed a verification on the Multi-Modal CelebA-HQ dataset, and the experimental results confirm its effectiveness.
Structure-based Large-scale Dynamic Heterogeneous Graphs Processing: Applications, Challenges and Solutions

Wenjie Zhang has given a Keynote Talk at the First Workshop on Graph Learning, associated with The ACM Web Conference 2022, on Monday 25th April 2022. This paper provides a summary of the topics she addressed during her talk.
A Comparative Study on Career-specific Teacher Training System in South Korea and Japan: Focusing on Seoul and Tokyo

[Purpose]The purpose of this study was to provide meaningful implications to South Korea by analyzing career-specific teacher training system in South Korea and Japan.&#x0D; [Methods]To this end, Seoul and Tokyo were selected as representatives of each country and related documents were analyzed. An analysis framework that set background of introduction, operating system, and program as comparative standards was used.&#x0D; [Results]The main findings were as follows. First, Both South Korea and Japan introduced career-specific teacher training to develop teachers’professionalism and improve their competencies. Second, South Korea focused on out-school training, but Japan focused on in-school training as the legal training. Third, in terms of program, there were differences for period, participants, evaluation and so on. South Korea focused on short-term and lecture/participation-centered training, but Japan focused on long-term and case sharing/practice-centered training. Participants were limited to a narrower range in Japan. Also, unlike South Korea, Japan conducted separate evaluation and it was used for decision on formal hiring and future training plan.&#x0D; [Conclusion]This study suggests that school principals’ role performance for operating in-school training, sophisticated demand analysis and reflection of on-site opinions, and solutions to support teachers’continuous learning are needed for career-specific teacher training system in South Korea.
Quantum communication with semiconductor quantum dots (Conference Presentation)

Semiconductor quantum dots (QDs) are able to confine single charges on the nanoscale in all three dimensions of space, making them excellent systems for exploring quantum phenomena. In particular, QDs have demonstrated outstanding performance as sources of entangled and indistinguishable photon pairs, properties highly desired in the fields of quantum communication and -information processing. Here I report on the advances of QDs as potential resources for photonic quantum networks, which allow to overcome the fundamental range limitations of single photon-based applications. After an introduction to the underlying mechanisms of entangled photon pair generation, I demonstrate several building blocks of quantum networks, with quantum key distribution as a prime application.
A Fine-Grained Recognition Model Based On Hippocampal Neural Circuit

Fine-grained image recognition tasks have received much attention in recent years. Works in this fields generally suffers from the following drawbacks: firstly, models are designed to be overly bloated due to the incorporation of multiple less relevant solutions, secondly, the feature map-based design methods ignore the characteristics of the feature vectors themselves, and thirdly, they lack a comprehensive consideration of the semantic relationships of the categories. In this paper, a brain-inspired modelling framework is proposed to model the neural circuits of hippocampal. The TSP module simulates situational memory, uncovering image properties, by interacting with pairs of sample features, while the MSP module simulates statistical learning, forming overlapping representations, by optimizing the spatial distribution of the samples&#x0027; feature states. The work in this paper integrates the semantic information embedded in feature vectors to generate discriminative representations in terms of their universal nature. Experimental results show that the model in this paper is effective in public benchmarks and can be a complementary alternative to other state-of-the-art methods.
Review of Feature Representation Techniques in Web APIs

In the current scenario, each task can be handled using Web APIs. Due to this, companies are providing services in the forms of Web APIs and Mashup APIs, which has generated a pool of services on the internet. It isn&#x2019;t easy to search for the required services on the internet. It has resulted in an increase in the need for service management. If the services are properly grouped according to their actions and working, it will be easy to search for them. To group services, it is required that functionality of web APIs should be represented in vector space effectively. This service representation can be based on the frequency of features or statistical or semantic analysis of features. This article provides an overview of the many ways for representing services in vector space that are utilised in web API domains. This review paper will assist the researchers understand the existing techniques for service representation so that service management and grouping can be performed effectively.
An adversarial attack method for Yolov4 based on changeable-perturbation-step PGD

With the development of artificial intelligence, the object detection model based on deep learning has also achieved great results. The detection model has also developed from the traditional manual extraction of features to the current neural network extraction. The classic single-stage detection model is based on YOLO series is representative. However, with constant research, it is discovered that the detection model based on deep neural network also inherits the shortcomings of neural network and is vulnerable to adversarial attacks. This paper proposes an optimized attack algorithm based on PGD, which realizes the adversarial attack on the YOLOv4 object detection model. Experiments have proved that this attack method in this paper reduces the mAP indicator from 87.61% to 0.12% on the VOC data set, and from 69.17% to 0.37% on the COCO data set. It has a certain improvement in the evaluation indicators PSNR and SSIM, and the attack effect Compared with the original PGD, the quality of the generated adversarial example is better.
A Tale of Two Forms of Protest

The COVID-19 pandemic triggered extraordinary legislative and executive responses across the globe. In 2020, the Victorian Government used, for the first time, powers in the Public Health and Wellbeing Act 2008 (Vic) to impose significant restrictions on the freedoms of the public. This article considers the powers to make directions under the Victorian regime and the potential relevance of the implied freedom of political communication in the Australian Constitution and the human rights protected by the Charter of Human Rights and Responsibilities Act 2006 (Vic). We conclude that some forms of protest activity may have remained lawful during the Victorian lockdowns, although the purpose of the protest activity and the manner of protest may be relevant considerations when adopting a Charter-consistent interpretation of the statutory provisions.