Reviewerly/exHarmony huggingface dataset:
Towards Tacit Knowledge Mining within Context: Visual Cognitive Graph Model and Eye Movement Image Interpretation

Visual attention is one of the most important brain cognitive functions, which filters the rich information of the outside world to ensure the efficient operation of limited cognitive resources. The underlying knowledge, i.e., tacit knowledge, hidden in the human attention allocation performances, is context-related and is hard to be expressed by experts, but it is essential for the novice operator training and interaction system design. Traditional model of visual attention allocation and corresponding analysis methods seldom involve task contextual information or present the tacit knowledge in explicit and in quantified way. Thus, it is challenging to pass on the expertâ€™s tacit knowledge to the novice or utilize it to construct interaction system by employing traditional methods. Therefore, this paper firstly proposes a new model called the visual cognitive graph model based on graph theory to model the visual attention allocation associating with the scenario context. Then, based on this graph model, utilize data mining method to reveal attention patterns within scene context to quantitatively analyze the operator's tacit knowledge during operation tasks. We introduce three physical quantities derived from graph theory to describe the tacit knowledge, which can be used directly to construct an interaction system or operator training. For example, discover the essential information within task context, the relevant information affecting critical information and the bridge information revealing the decision-making process. We test the proposed method in the example of flight operation, the comparison results with traditional eye movement graph model demonstrates that the proposed visual cognitive model can compromise the task scenario context. The comparison results with statistical analysis method demonstrates that our tacit knowledge mining method can reveal the underling knowledge hidden in the visual information. Finally, we give the practical applications in the examples of operator training guidance and adaptive interaction system. Our proposed method can explore more in-depth knowledge of visual information, such as the correlations of different obtained information and the way operator obtains information, most of which are even not noticed by operators themselves.
Simulation of the Gaussian Distribution of a Random Variable Using a Standard Programm Rnd()

The paper considers three algorithms for modeling the normal distribution of a continuous random variable using a standard program Rnd() that generates random numbers: the integral equation method, the von Neumann selection-rejection method, and the Metropolis algorithm. Listings of programs written in the Visual Basic for Application (VBA) language, in which the indicated algorithms are implemented, are presented. The results of calculations obtained with the help of these programs are presented. The efficiency of using various algorithms is analyzed and conclusions are drawn about the expediency of their use in solving various problems using the Monte Carlo method #CSOC1120.
Reimagining “Space”: An Approach to Increase Participants’ Social Aspects During Online Conferencing

Online conferencing has become a new normal after the COVID-19 pandemic. However, existing systems like ZOOM fall short of facilitating informal social aspects like chats, talks, discussions, dialogues, gatherings, secrets, or even gossip or quarrel, which often occur spontaneously during physical meetings. In this study, we design and prototype a novel system considering key spatial features that influence social interactions offline. The proposed system consists of three typical meeting modes: square mode for free social, room mode for split group discussion, stage mode for speech and presentation. Through Wizard-of-Oz testing with 10 participants, we summarize the design features that contribute to the richness of ambiance, the flexibility of distance, the serendipity of interaction of online conferences, and the effect of these aspects on social interaction. Together with the limitations and suggestions for future work, we hope this paper can inspire the design of spatial interaction on screen, with the aim to improve informal social aspects of online conferencing.
Design and Implementation of Real Estate Market Forecasting Model Based on Multiple Regression Analysis

In recent years, with the rising of China’s GDP index, the problem of real estate market has become increasingly prominent. It is necessary to prevent the real estate market from developing too hard, resulting in the hard landing of the real estate bubble, and to ensure that the real estate market can meet the real needs of the masses. This requires a good real estate market prediction model to accurately predict the sales area of the real estate market and guide the real estate to make reasonable planning. This paper uses multiple linear regression analysis to design a real estate market forecasting model, explores and designs a real estate market forecasting algorithm based on multiple linear regression.
Towards Supporting Attribute-Based Access Control in Hyperledger Fabric Blockchain

Hyperledger Fabric (HLF) is an open-source platform for deploying enterprise-level permissioned blockchains where users from multiple organizations can participate. Preventing unauthorized access to resources in such blockchains is of critical importance. Towards addressing this requirement, HLF supports different access control models. However, support for Attribute-Based Access Control (ABAC) in the current version of HLF is not comprehensive enough to address various requirements that arise when multiple organizations interact in an enterprise setting. To address those shortcomings, in this paper, we develop and present methods for providing full ABAC functionality in Hyperledger Fabric. Performance evaluation under different network configurations using the Hyperledger Caliper benchmarking tool shows that the proposed approach is quite efficient in practice.
A New Weighted Multi-Scale Descriptor for Hand Gesture Recognition

Image-based hand gesture recognition is a very challenging problem as the hand is a smaller object with much complex articulations and hence more easily affected by segmentation errors compared to the entire human body. This paper suggests a new weighted multi-scale feature descriptor (WMD) along the contour of the hand for robust hand gesture recognition system using depth images. Firstly, the weight factor is estimated for each contour point by 2D Gaussian smoothing function and Prewitt operator to relate it with its neighbors and highlight its importance. Then the feature descriptor is constructed via 1D Gaussian smoothing considering the contour points in the hand should not be independent from each other when used to recognize the gestures. Granularity of the descriptor is characterized by multiple scales with different standard derivations of the Gaussian function. And its invariants to translation, rotation and scaling transformations are proved theoretically and validated experimentally. Finally, extensive experiments on our ten-gesture dataset and public NTU dataset have been carried out comparing the proposed algorithm with two other hand gesture recognition methods. The results show that our method outperforms the other methods and provides a good combination of accuracy and computational efficiency for real-time applications.
A GAN Based Approach to Compare Identical Images with Generative Noise

Generative Adversarial Networks (GAN) have shown impressive results in the generation and translation of images, for instance to generate a painting image with a specific style from a realistic photo. This ability to change of style makes it usable to denoise an image and to the best of our knowledge, GAN has not been used for such applications. This paper presents a generic approach to compare identical images but of which one has been modified by an external process which makes it very noisy to be directly comparable to the original image. This noise results from the process of creating a document and is called generative noise. First, the noisy image is transformed with the generator to get a denoised image more similar to the original one. However, the denoising with the GAN is not perfect due to strong noise processes which requires to transform the original and denoised images into a comparable space, and to check that the quality is good enough to make the comparison. This approach has been applied on the Romanian identity card to compare the identity photo to the ghost image and shows significantly better results than standard comparison approaches.
Multi-reservoir Echo State Networks with Encoders

Echo state networks (ESNs) as a specific type of recurrent neural networks (RNNs) have gained o lot of attention within research community. Training of ESNs is much less computationally demanding since unlike more common fully trained RNNs only small part of ESN parameters is trained. Recently more and more research is aimed to study methods how to scale ESNs and multiple approaches to multi-reservoir ESNs have emerged such as Deep ESNs and Deep Projection-encoding ESNs. In this contribution we study several approaches to interconnect multiple reservoir layers to achieve rich timescale dynamics necessary for some task. We perform experiments on two widely used datasets on the prediction task and compare achieved results.
Unsupervised Generation of Tradable Topic Indices Through Textual Analysis

Stocks' returns are moved by many risk factors. Thematic stock indices try to represent these factors but are limited by the fact that risk factors are not directly observable. This paper shows a method to uncover hidden risk factors through text analysis. It applies the dynamic variant of the Latent Dirichlet Allocation (LDA) model to annual and quarterly reports to find a topic distribution for each stock. This is then interpreted as the risk factor partition and transformed into a standard normal basis which corresponds to pure risk factors. The weights indicate the proportions necessary to combine the equities into tradable topic indices. The need for human intervention is minimized by determining the optimal parameters automatically.
Experimental investigation of quantum discord in DQC1

Quantum discord has been proposed as a resource responsible for the exponential speedup in deterministic quantum computation with one pure qubit (DQC1). Investigation of the quantum discord generated in DQC1 is of significant importance from a fundamental perspective. However, in practical applications of DQC1, qubits generally interact with the environment. Thus, it is also important to investigate the discord when DQC1 is implemented in a noisy environment. We implement DQC1 on an electron spin resonance (ESR) architecture in such an environment and nonzero quantum discord is observed. Furthermore, we find that the values of discord correspond to the values of purity &lt;i&gt;α&lt;/i&gt; and quantum Fisher information, which reflect the power of the algorithm. Our results provide further evidence for the role of discord as a resource in DQC1 and are beneficial for understanding the origin of the power of quantum algorithms.
Price Discovery in Bitcoin: The Role of Limit Orders

We analyse the price discovery process in bitcoin-dollar trading on Coinbase, the most established cryptocurrency spot exchange. Using a modified reinforcement learning framework, we find that market orders individually carry more information than limit orders, but due to their abundance, limit orders are the main driver of price discovery. Moreover, we observe a significant asymmetry in permanent price impact, with sell market orders carrying more information than buy market orders. We relate this asymmetry to unidirectional retail order flows. Owing to a negligible relative tick size, most of the price discovery contribution of limit orders originates from orders undercutting the best quotes. Consistent with theoretical predictions, the state of the order book has a significant impact on price discovery, with orders submitted along the order book imbalance showing an increased contribution and the importance of market orders being more pronounced in case of a binding bid-ask spread. Finally, the price discovery process on Coinbase is strongly influenced by information flows from other exchanges.
Meta-Learning

Although current deep reinforcement learning methods have obtained great successes, training times for most interesting problems are high; they are often measured in weeks or months, consuming time and resources—as you may have noticed while doing some of the exercises at the end of the chapters.
Higher School of Russia in the Conditions of Digitalization of Education

The main goal of this research is to analyze the ongoing transformations in the higher school of Russia in the conditions of digitalization. The Decree of the Government of the Russian Federation adopted in 2019 contained a decision on the creation of digital platforms in education. The government’s report emphasizes that by 2024, 20% of students will be able to master individual courses and disciplines online using the resources of educational organizations, and a targeted model of the digital educational environment will be introduced in all constituent entities of the Russian Federation. In addition, it should be noted that 2020 is a turning point for all spheres of society, including higher education. In connection with the pandemic of coronavirus infection, the Ministry of Science and Higher Education of the Russian Federation recommended that universities switch to distance learning. Thus, today, one of the most important tasks for the higher school of Russia is to reach a fundamentally new level of development using distance learning. In order to do this, it is necessary to solve the following problems: the introduction of modern technologies, the formation of new standards, the training of competent specialists, etc. The task of this study is to find out how successfully the process of forced digitalization is going and to give practical recommendations for the further development of the digital educational environment. In this article, the authors use the methods of descriptive statistics identified during a sociological survey among students and teachers of Ufa State Petroleum Technical University.
Multiple Input Branches Shift Graph Convolutional Network with DropEdge for Skeleton-Based Action Recognition

Graph Convolutional Networks (GCNs) achieve remarkable success in the skeleton-based action recognition tasks. However, the recent state-of-the-art (SOTA) methods for this task usually have a large model size and too heavy computational complexity. In this work, we propose an early fused model, Multiple Input Branches Shift Graph Convolutional Network with DropEdge (MIBSD-GCN). First, to reduce the complexity of the multi-stream model, we introduce a lightweight Shift Graph Convolutional Network (Shift-GCN) block. It is embedded into an early fused architecture, Multiple Input Branches (MIB), which can enrich input features and suppresses the model redundancy. Then, a novel spherical coordinate representation is added as one of the input branches to enhance the recognition effect. Finally, we design the Shift Graph Convolutional Network with DropEdge (SD-GCN) to prevent over-fitting and over-smoothing, while maintain the model accuracy. Extensive experiments on two large-scale datasets, NTU RGB+D 60 and NTU RGB+D 120, show that the proposed model outperforms previous SOTA methods. We achieve 96.6% accuracy on the Cross-view benchmark of the NTU RGB+D 60, while being 3.4–16.5 times fewer FLOPs than other SOTA models.
Studying the User Experience of an Educational AR-Based App for Smart Glasses

The steady evolution of wearable technology—along with augmented reality (AR)—has enabled important advances in teaching and learning. We present STEM Education, an interactive AR application for smart glasses. We evaluate the app’s user experience (UX) with undergraduate STEM (Science, Technology, Engineering, and Mathematics) students. More specifically, we focus on the students’ overall satisfaction, degree of technological acceptance, mental workload, and emotional response. Our observations suggest that the app improves the student’s learning quality, while promoting high levels of engagement and satisfaction. We discuss these findings and their implications in the design of similar technology for educational contexts.
Simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake

Consensus protocols constitute an important part in virtually any blockchain stack as they safeguard transaction validity and uniqueness. This task is achieved in a distributed manner by delegating it to certain nodes which, depending on the protocol, may further utilize the computational resources of other nodes. As a tangible incentive for nodes to verify transactions many protocols contain special reward mechanisms. They are typically inducement prizes aiming at increasing node engagement towards blockchain stability. This work presents the fundamentals of a probabilistic blockchain simulation tool for studying large transaction volumes over time. Two consensus protocols, the proof of work and the delegate proof of stake, are compared on the basis of the reward distribution and the probability bound of the reward exceeding its expected value. Also, the reward probability as a function of the network distance from the node initiating the transaction is studied.
On Operations of Restriction and Freezing on Monadic Fuzzy Quantifiers Over Fuzzy Domains

This article introduces important operations on the class of monadic fuzzy quantifiers, namely, restriction and freezing. These operations are introduced and investigated in the novel frame of monadic fuzzy quantifiers over fuzzy domains.
<i>Astrea:</i>Auto-Serverless Analytics Towards Cost-Efficiency and QoS-Awareness

With the ability to simplify the code deployment with one-click upload and lightweight execution, serverless computing has emerged as a promising paradigm with increasing popularity. However, there remain open challenges when adapting data-intensive analytics applications to the serverless context, in which users of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">serverless analytics</i> encounter the difficulty in coordinating computation across different stages and provisioning resources in a large configuration space. This paper presents our design and implementation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Astrea</i> , which configures and orchestrates serverless analytics jobs in an autonomous manner, while taking into account flexibly-specified user requirements. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Astrea</i> relies on the modeling of performance and cost which characterizes the intricate interplay among multi-dimensional factors (e.g., function memory size, degree of parallelism at each stage). We formulate an optimization problem based on user-specific requirements towards performance enhancement or cost reduction, and develop a set of algorithms based on graph theory to obtain the optimal job execution. We deploy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Astrea</i> in the AWS Lambda platform and conduct real-world experiments over representative benchmarks, including Big Data analytics and machine learning workloads, at different scales. Extensive results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Astrea</i> can achieve the optimal execution decision for serverless data analytics, in comparison with various provisioning and deployment baselines. For example, when compared with three provisioning baselines, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Astrea</i> manages to reduce the job completion time by 21% to 69% under a given budget constraint, while saving cost by 20% to 84% without violating performance requirements.
AutoThing: A Secure Transaction Framework for Self-Service Things

Self-Service Terminals (SSTs) are increasing their presence across multiple industries, from vending machines and self-service banking to automated national border crossing checkpoints. Due to the massive integration of SSTs into critical infrastructure, their security has raised major concerns. In this work, we develop a security model for the family of SST system protocols to formally prove that traditional SST systems are not resilient against cyber-attacks. We create a comprehensive inventory of attacking configurations against SSTs. We then use this inventory to verify that enhanced resilience against compromising any major component of an SST system can be achieved via three steps: a) replacing free-range APIs with multi-signature transaction tokens; b) switching from networking interfaces in SSTs into direct device-to-device channels; and c) adding a bootstrapping service. We introduce Offline Self-Service Things (OSST), which have high attack resilience by maintaining a distributed representation without the need to be online. To enable the real-world applicability of OSSTs, we develop <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoThing</small> , a transaction framework for OSSTs. We show the extensibility of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoThing</small> by building two applications upon the framework: <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VolgaPay</small> , a payment system for vending machines; and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VolgaGuard</small> , an access control system. We evaluate both systems to show the portability and scalability of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoThing</small> .
Mct-Net: A Co-Training Method Based on Meta-Learning with Contradictory Loss for Learning with Noisy Labels

With the rapid development of deep neural networks, the demand for large-scale accurately labeled datasets is growing fast. However, human-labeled datasets may lead to misleading information, which can directly degrade the generalization performance of deep neural networks due to overfitting to noisy labels. Therefore, as a kind of weakly supervised learning, the research on learning with noisy labels is a hot topic nowadays. Most previous works may be not robust to learning with noisy labels, because they cannot well control the overfitting of the trained model to noisy labels. Inspired by co-training and meta-learning, we propose in this paper a novel two-stage learning framework called Meta-Co-Training net (MCT-net) for learning with noisy labels, one of which is a pre-training stage and the other is the meta-co-training stage. We first pre-train two same backbone networks with a robust loss function called Contradictory Loss, which is first proposed and verified, obtaining excellent performance both on experimental results and theoretical foundation. Then we co-teach two pretrained backbone networks based on meta-learning, which we do not adopt any auxiliary clean subset as meta-data compared with other meta-learning methods and also introduce multilayer perceptrons to assist co-teaching. Compared with other state-of-the-art methods on corrupted datasets, such as Cifar10, Cifar100, Animal10N and Clothing1M, the experimental results demonstrate that MCT-net is superior to other methods on shallower layers.