Reviewerly/exHarmony huggingface dataset:
Efficient Asynchronous GCN Training on a GPU Cluster

Research on Graph Convolutional Networks (GCNs) has increasingly gained popularity in recent years due to the powerful representational capacity of graphs. A common assumption in traditional synchronous parallel training of GCNs using multiple GPUs is that load is perfectly balanced. However, this assumption may not hold in a real-world scenario where there can be imbalances in workloads among GPUs for various reasons. In a synchronous parallel implementation, a straggler in the system can limit the overall speed up of parallel training. To address these performance issues, this research investigates approaches for asynchronous decentralized parallel training of GCNs on a GPU cluster. The techniques investigated are based on graph clustering and the Gossip protocol. The research specifically adapts the approach of Cluster GCN, which uses graph partitioning for SGD based training, and combines with a gossip algorithm specifically designed for a GPU cluster to periodically exchange gradients among randomly chosen partners (GPUs). In addition, it incorporates a work pool mechanism for load balancing among GPUs. The gossip algorithm is proven to be deadlock free. The implementation is performed on a deep learning cluster with 8 Tesla V100 GPUs per compute node, and PyTorch and DGL as the software platforms. Experiments are conducted on different benchmark datasets. The results demonstrate superior performance with similar accuracy scores, as compared to traditional synchronous training which uses “all reduce” to synchronously accumulate parallel training results.
Investigating the “Fair Use” in Contemporary Art in the Cases of Jeff Koons and Richard Prince

This article explores how copyright-based genres in contemporary art conform to intellectual property law. By examining the “appropriation” cases and the criteria of the doctrine of fair-use in them, the limits and borders of legal and creative copying (reduplication?) are determined. This study’s main question is how fair-use as a legal solution helps artists to be more creative? The research hypothesizes that fair-use is the mediating solution of law and art to balance copyright laws strictness. The cases of Jeff Koons and Richard Prince, as examples of influential figures in the history of art and law, have been selected, and the criteria for their fair-use will be analyzed. The Research Method is a doctrinal research method in which the evolution of copyright laws following contemporary art characteristics is expressed through court decrees and reviewing cases. The present analysis results show that fair-use, by considering parody and Transformative use, defends the expression and appearance of the artist’s creativity. Further, the study of court’s performance through doctrinal research method shows that the emergence of creativity is a common goal of intellectual property law and contemporary art.
Quick Response Code Application for Virtual Model

Quick response code (QR code) is a cutting technology which can contain much information. In this paper, a convenient app for virtual model showing is proposed through scanning a QR code. With the help of the app, videos can be obtained, virtual models can be rotated and scaled by touching the screen. The interactive environment enhances learners' spatial thinking and cultivates their sense of innovation.
A Study on the Application of Video Assistant Referee (VAR) Technology in Chinese Football Professional League

By using the methods of literature review, video, video analysis and mathematical statistics, this paper studied the application of VAR technology in 30 rounds and 240 matches in Chinese super league in 2019, and compared it with Super League and 2018 FIFA world cup in 2018. It was found that the accuracy of key penalty was improved, the fairness and justice of the match were guaranteed, and the sound development of Super League was promoted. The application of VAR technology improved the trust between referees and players, promoted the self-confidence of referees in law enforcement and promoted the normal play of players' level. Shortcomings include unreasonable limits and timing of VAR intervention; the understanding and scale of foul penalty are not uniform; the review time is too long and the operation procedure is not standardized, etc. need to be improved.
THE REAL SHERLOCK HOLMES AND DR. WATSON

This article discovers the real identities behind the fictional characters of Sherlock Holmes and Dr. John H. Watson by Arthur Conan Doyle by making an extensive analytical analysis of people, names, stories, and places involved.
Applying Soft Attention in Deep Reinforcement Learning for Robot Collision Avoidance

research-article Share on Applying Soft Attention in Deep Reinforcement Learning for Robot Collision Avoidance Authors: Pei Xu University of Science and Technology of China, China University of Science and Technology of China, ChinaView Profile , GuangDa Chen University of Science and Technology of China, China University of Science and Technology of China, ChinaView Profile , Jianmin Ji University of Science and Technology of China, China University of Science and Technology of China, ChinaView Profile Authors Info & Claims ICMAI 2021: 2021 6th International Conference on Mathematics and Artificial IntelligenceMarch 2021 Pages 34–38https://doi.org/10.1145/3460569.3460573Online:31 August 2021Publication History 0citation70DownloadsMetricsTotal Citations0Total Downloads70Last 12 Months70Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Paper Dating Analysis Based on Paper Texture Image Feature

Paper dating analysis is an important research direction of document inspection, which is widely used in the inspection and identification of cultural relics and ancient books. This article focuses on the analysis of paper dating in ancient books, and proposes a non-destructive inspection and analysis method based on paper fiber texture images. Aiming at the global stacking morphology of paper fibers and the local features of specific types of fiber morphology, we propose a neural network-based hybrid texture feature extraction and representation method: On the one hand, we use convolutional networks to obtain global features of fiber texture; at the same time, we designed a method of extracting and representing local fiber morphological features based on the attention mechanism. By mixing the above two types of features, we realize the extraction and representation of paper fiber features. Furthermore, we use the GRU(Gate Recurrent Unit) model to establish a paper dating time series model and design a new loss function. In order to verify the method, this paper selects 36 domestic books published from 1950 to 2000, and uses the document checker VSC 6000 to collect paper texture images as a dataset, and verifies the effectiveness of the proposed method on this dataset. Experiments prove that the method proposed in this paper has achieved ideal results in paper dating analysis.
SteelChain—Blockchain-Based Transparent Supply Chain Framework for the Steel Industry

Steel Industry supply chain encompasses a distributed network of stakeholders diversified across countries, functionalities, production processes, and lots of manpower. Efficient logistics is a critical component of the industry’s supply chain carrying out the handling and movement of raw materials and products from the source locations to their final destination. Fragmentation in the process results in economic loss, opportunity losses, and rise in operating expenses. The pertinent setup is constrained by inefficient communication, lack of transparency, and standardized pricing resulting in higher expenses, rising delivery times, and human errors. These problems get multiplied owing to localized data frameworks which are non-standardized. This further necessitates redundancy in processing, increased paperwork, and increased manual interventions leading to frequent errors and incurring huge costs on companies due to deviations from delivery schedules, fraudulent intrusions, and damages in the consignment during transit. The SteelChain Protocol Suite aims to resolve these issues by scaling up blockchain technology to create a simple, transparent, and standardized framework usable throughout the steel manufacturing and delivery value chain. SteelChain efficiently utilizes the immutability offered by Blockchain Technology to map physical steel coils/rods to cryptographic transaction hashes. This ensures end-to-end constancy of identity of a particular coil/rod at each stage during the life cycle from order placement by customer to eventual reception of the consignment.
Identifying Inter-Project Relationships with Recurrent Neural Networks: Towards an AI Framework of Project Success Prediction

A growing number of emerging studies have been undertaken to examine the mediating dynamics between intelligent agents, activities, and cost within allocated budgets to predict the outcomes of complex projects in dint of their significant uncertain nature in achieving a successful outcome. Emerging studies have used machine learning models to perform predictions, and artificial neural networks are the most frequently used machine learning model. However, most machine learning algorithms used in prior studies generally assume that input features, such as project complexity, team size and strategic importance, and prediction outputs, are independent. That is, a projectâ€™s success is assumed to be independent of other projects. As the datasets used to train in prior studies often contain projects from different clients across industries, this theoretical assumption remains tenable. However, in practice, projects are often interrelated across several dimensions, such as distributed overlapping teams. Therefore, we argue that the inter-project relationships should be taken into consideration to improve prediction performance. Furthermore, an ongoing ethnographic study at a leading project management artificial intelligence consultancy, referred to in this research as Company Alpha, suggests that projects within the same portfolio frequently share overlapping characteristics. To capture the emergent inter-project relationships, this study aims to compare two specific types of artificial neural network prediction performances; (i) multilayer perceptron and; (ii) recurrent neural networks. The multilayer perceptron is one of the most widely used artificial neural networks in the project management literature, and recurrent networks are distinguished by the memory they take from prior inputs to influence input and output. Through this comparison, this research will examine whether recurrent neural networks can capture the potential inter-project relationship towards achieving improved performance in contrast to multilayer perceptron. Our empirical investigation using ethnographic practice-based exploration at Company Alpha will contribute to project management knowledge and support developing an intelligent project prediction AI framework with future applications for project practice.
Probability Density Machine: A New Solution of Class Imbalance Learning

Class imbalance learning (CIL) is an important branch of machine learning as, in general, it is difficult for classification models to learn from imbalanced data; meanwhile, skewed data distribution frequently exists in various real-world applications. In this paper, we introduce a novel solution of CIL called Probability Density Machine (PDM). First, in the context of Gaussian Naive Bayes (GNB) predictive model, we analyze the reason why imbalanced data distribution makes the performance of predictive model decline in theory and draw a conclusion regarding the impact of class imbalance that is only associated with the prior probability, but does not relate to the conditional probability of training data. Then, in such context, we show the rationality of several traditional CIL techniques. Furthermore, we indicate the drawback of combining GNB with these traditional CIL techniques. Next, profiting from the idea of K-nearest neighbors probability density estimation (KNN-PDE), we propose the PDM which is an improved GNB-based CIL algorithm. Finally, we conduct experiments on lots of class imbalance data sets, and the proposed PDM algorithm shows the promising results.
An Efficient Local Word Augment Approach for Mongolian Handwritten Script Recognition

The scarcity problem of Mongolian handwritten data greatly limits the accuracy of Mongolian handwritten script recognition. Most existing augmentation methods generate new samples by making holistic transformation on the existing samples, which cannot fully reflect the variation of the Mongolian handwritten words. According to the characteristics of Mongolian words, this paper proposes a local word augment approach for Mongolian handwriting data, effectively improving the diversity of the augmented samples. We make local variation on the strokes by moving the endpoints and the out-stroke control point of the strokes and reconstructing the strokes with Bezier splines. The overall generation process is flexible and controllable. Experiment on few-shot Mongolian handwritten OCR demonstrates that our approach significantly improves the recognition accuracy and outperforms the holistic augmentation methods.
The Laboratory as a Site for Research on Acting: relationships between art and science

ABSTRACT This article focuses on new knowledge perspectives proposed by research on onstage practice within the performing arts. It is based on the notion of the laboratory as a backbone, methodological framework, and place for exploring research-creation projects. Our study offers critical reflections on research procedures, taking the actor's creative process as our object of study. We go on to establish a connection between theoretical dimensions and practical expressions, through analysis of two research projects that confront performance practice with its underlying scientific premises. Lastly, different modes for publishing and disseminating the results of research on onstage practice are discussed.
Discovering HOI Semantics from Massive Image Data

Human-Object Interaction (HOI) plays an important role in human-centric scene understanding. However, the commonly used two-stage methods have large computational costs and a slow inferring speed. The existing one-stage methods detect HOIs by detecting the central points or the union boxes of human and objects, which need to process a large scale of regions and many unnecessary features. In this paper, we propose a novel one-stage method for discovering HOI semantics from massive image data. In particular, we present two new designs in our method, namely action classification and displacement prediction. Further, we design a special HOI score calculation strategy, which can decay the HOI score of the results that have bad matching result. We evaluate our method on the popular HICO-DET benchmark and compare our proposal with a number of existing approaches. The results show that our method outperforms existing methods in discovering HOI semantics. abstract environment.
Social Security Reform in Brazil: A Twitter Sentiment Analysis

Brazil’s social security reform, approved in 2019, moved several social networks, revealing the high polarity of this theme. In this paper, we perform an automatic investigation of this polarity using sentiment analysis techniques. We collected 980,577 tweets published between January and November of that year and compared four supervised machine learning algorithms: Multinomial Naïve Bayes, Logistic Regression, Support Vector Machines, and Random Forest. Due to the large volume of data collected, we employed a transfer learning approach to train these algorithms. As a result, the four algorithms predominantly classified the tweets as “neutral”, that is, these posts do not explicitly expose their users’ opinion about the topic. Of the tweets that showed polarity, there was a dominance of posts classified as “positive”, i.e., their authors have positive feelings (support, affinity) concerning the Social Security Reform proposal.
Research on real time network homomorphic encryption algorithm based on chaotic sequence

In order to improve the network security, a realtime network full homomorphic encryption algorithm based on chaotic sequence is designed. In order to improve the security of network data and reduce the cost of coding, chaotic sequences are used for network security coding. According to the results of network coding, a fully homomorphic encryption scheme is designed and applied to the design of real-time network full homomorphic encryption algorithm, and the correctness of encryption and decryption is tested to further improve the network security. The experimental results show that the algorithm has high computational efficiency, wide encryption coverage and good encryption effect, which can effectively reduce the network security risk and ensure the network information security.
Taking over the Stock Market: Adversarial Perturbations Against Algorithmic Traders

In recent years, machine learning has become prevalent in numerous tasks, including algorithmic trading. Stock market traders utilize machine learning models to predict the market’s behavior and execute an investment strategy accordingly. However, machine learning models have been shown to be susceptible to input manipulations called adversarial examples. Despite this risk, the trading domain remains largely unexplored in the context of adversarial learning. In this study, we present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques to manipulate the input data stream in real time. The attacker creates a universal adversarial perturbation that is agnostic to the target model and time of use, which remains imperceptible when added to the input stream. We evaluate our attack on a real-world market data stream and target three different trading algorithms. We show that when added to the input stream, our perturbation can fool the trading algorithms at future unseen data points, in both white-box and black-box settings. Finally, we present various mitigation methods and discuss their limitations, which stem from the algorithmic trading domain. We believe that these findings should serve as a warning to the finance community regarding the threats in this area and promote further research on the risks associated with using automated learning models in the trading domain.
Towards Assurance-Driven Architectural Decomposition of Software Systems

Computer systems are so complex, so they are usually designed and analyzed in terms of layers of abstraction. Complexity is still a challenge facing logical reasoning tools that are used to find software design flaws and implementation bugs. Abstraction is also a common technique for scaling those tools to more complex systems. However, the abstractions used in the design phase of systems are in many cases different from those used for assurance. In this paper we argue that different software quality assurance techniques operate on different aspects of software systems. To facilitate assurance, and for a smooth integration of assurance tools into the Software Development Lifecycle (SDLC), we present a 4-dimensional meta-architecture that separates computational, coordination, and stateful software artifacts early on in the design stage. We enumerate some of the design and assurance challenges that can be addressed by this meta-architecture, and demonstrate it on the high-level design of a simple file system.
On normality of a closure space generated from a tree by relation

Binary relation plays a prominent role in the study of mathematics in particular applied mathematics. Recently, some authors generated closure spaces through relation and made a comparative study of topological properties in the space by varying the property on the relation. In this paper, we have studied closure spaces generated from a tree through binary relation and observed that under certain situation the space generated from a tree is normal.
Unified and View-Specific Multiple Kernel K-Means Clustering

Multiple kernel clustering (MKC), as an important tool for handling multi-view non-linear data, has attracted notable attention among data mining and machine learning communities. The key issue of MKC is to obtain a more accurate and appropriate kernel similarity for clustering from the given multiple kernel library. However, existing MKC methods only capture the unified or consistent information while the view-specific individual structures have been ignored to degrade clustering performance. In this paper, we propose a novel multiple kernel k-means clustering method (UVSMKC), where the \(\textit{unified}\) and \(\textit{view-specific}\) information is seamlessly explored under multiple kernel setting. Different form traditional framework, we formulate the provided multi-view kernels with a \(\textit{unified}\) low-rank similarity matrix, multiple \(\textit{view-specific}\) matrices and the respective noise matrices. Furthermore, we design a four-step alternate algorithm to solve the proposed optimization problem. Comparing to the state-of-the-art multiple kernel clustering methods, the experimental results on six multiple kernel benchmark datasets validate the effectiveness of our proposed UVSMKC, showing promising ability to capture consensus and view-specific information.
Pedagogic assistance to self-realisation of students of the direction of training “Social Work” in the educational space of the university

The article is devoted to the study of the mechanisms of pedagogic assistance to self-realisation of students of the "Social Work" training direction in the educational space of the university. We considered and substantiated the theory of student-centred education in the conditions of a university. The mechanisms of students' realisation of their existential needs in the process of obtaining higher education are considered. The article defines the conditions conducive to student self-realisation in the educational space of the university. The content of the article substantiates an algorithm for the formation of significant personal qualities of a student in the educational space of the university. Particular attention is paid to the model of pedagogic assistance to self-realisation of students in the educational space of the university. The text provides effective forms of pedagogic assistance to self-realisation of students in the educational space of the university. The article analyses the results of an empirical study of the process of pedagogic assistance to self-realisation of students in the direction of training "Social Work" in the educational space of the university. During the analysis of the literature and the study, it was revealed that in the process of self-realisation there is always something that encourages the student to carry out self-realisation – a motive reflected in the goal of self-realisation, which affects the final result of its studies at the university. The necessity of timely determination of interests, abilities, inclinations, value orientations, students’ capabilities and ways of overcoming barriers that hinder self-realisation is shown. In this case, the teacher acts as a partner, coordinator, consultant, mentor, thereby providing more opportunities for the student's independence and responsibility.