Reviewerly/exHarmony huggingface dataset:
Dynamic gesture recognition based on YOLOv4 and deep-sort methodological research

The dynamic gesture trajectory recognition results are low accurate and poor real-time due to the problems of occlusion, complex background and fast gesture movement. In this paper, we take advantage of the advantages of machine vision to extract the video keyframes by the three-frame differential method and use the annotation software to produce the dataset. The you only look once 4 (YOLOv4) algorithm is improved to reduce the redundancy of the network structure and enhance the applicability of the feature map for hand gesture recognition. Combined with the Deep-sort real-time tracking feature, the hand motion trajectory is obtained by introducing the epiphenomenal features to effectively avoid the situation that the object is not tracked when it is obscured. To avoid the problem of gradient disappearance during deep network training, the DenseNet-BC-169 network is used to balance the recognition rate and training time for gesture trajectory classification. Compared with FLIXT, the winner of the dynamic gesture recognition challenge, the final results showed a 6.13% improvement in accuracy and video processing with the IsoGD dataset reached 31fps, validating the effectiveness of this method.
Effects of Career Decision Self-Efficacy in College Students Majoring in Cosmetology on Career Preparation Behavior and Major Satisfaction

Purpose: This study examines the effects of career decision-making and self-efficacy on the career preparation behaviors and major satisfactions of beauty majors.Methods: A total of 253 students were selected from two years of college cosmetology majors in Gyeonggi-do and Incheon. Data collected over two weeks from November 15 to November 30, 2021 was analyzed. Frequency analysis, as well as factor, reliability, correlation, and multiple regression analyses, were performed using SPSS program 26.0.Results: First, the study found a positive correlation between career decision self-efficacy, career preparation behaviors, and major satisfaction. Secondly, the study found that career decision self-efficacy bears a statistically significant (p&lt;0.05) effect on job preparation and collection, career counseling, and career exploration. Third, the study found that career decisionmaking self-efficacy bears a statistically significant (p&lt;0.05) effect on class and perception satisfaction. Fourth, the study found that career preparation behaviors carry a statistically significant effect on class satisfaction (p&lt;0.05).Conclusion: This study was conducted to investigate the effects of self-efficacy on career decision-making in the career preparation behaviors and major satisfactions of college students majoring in cosmetology. The results of career decision self-efficacy bore a statistically significant effect on career preparation behaviors and major satisfaction.
Athlos: A Framework for Developing Scalable MMOG Backends on Commodity Clouds

The development of resource-intensive, distributed, real-time applications like Massively Multiplayer Online Game (MMOG) backends entails a variety of challenges, some of which have been extensively studied. Despite some advancements, the development and deployment of MMOG backends on commodity clouds and high-level computing layers continues to face several obstacles, including a non-standardized development methodology, lack of provisions for scalability, and the need for abstractions and tools to support the development process. In this paper, we describe a set of models, methods, and tools for developing scalable MMOG backends and hosting them on commodity cloud platforms. We present Athlos, a framework that allows game developers to leverage our methodology to rapidly prototype MMOG backends that can run on any type of cloud environment. We evaluate this framework by conducting simulations based on several case-study MMOGs to benchmark its performance and scalability, and compare the development effort needed, and quality of the code produced with other approaches. We find that MMOGs developed using this framework: (a) can support a very high number of simultaneous players under a given latency threshold, (b) elastically scale both in terms of runtime and state, and (c) significantly reduce the amount of effort required to develop them. Coupled with the advantages of high-level computing layers such as Platform, Backend, and Function-as-a-Service, we argue that our framework accelerates the development of high-performance, scalable MMOGs, that leverage the resources of commodity cloud platforms.
Spatial label smoothing via aleatoric uncertainty for bleeding region segmentation from laparoscopic videos

Automatically segmenting bleeding regions is an essential task for computer-assisted surgery systems. Recent development in deep learning has boosted the performance of medical image segmentation. However, training deep neural networks, in general, requires high-quality pixel-wise annotations, in which such an annotating process is expensive and easy to introduce annotation noise. To address this issue, motivated by the observation that the noise should be data-dependent, we propose an uncertainty-guided label smoothing method instead of using a fixed label smoothing strategy. Aleatoric uncertainty, which accounts for inherent noise such as annotation error, is estimated via an additional branch of deep neural networks. With the help of estimated aleatoric uncertainty, we could guide the spatial label smoothing in a self-adaptive manner. We demonstrated the effectiveness of our proposal by evaluating the Dice coefficient in a private bleeding segmentation dataset. An improvement over the baseline was observed for our proposal.
Exploiting the Nature of Repetitive Actions for Their Effective and Efficient Recognition

In the field of human action recognition (HAR), the recognition of actions with large duration is hindered by the memorization capacity limitations of the standard probabilistic and recurrent neural network (R-NN) approaches that are used for temporal sequence modeling. The simplest remedy is to employ methods that reduce the input sequence length, by performing window sampling, pooling, or key-frame extraction. However, due to the nature of the frame selection criteria or the employed pooling operations, the majority of these approaches do not guarantee that the useful, discriminative information is preserved. In this work, we focus on the case of repetitive actions. In such actions, a discriminative, core execution motif is maintained throughout each repetition, with slight variations in execution style and duration. Additionally, scene appearance may change as a consequence of the action. We exploit those two key observations on the nature of repetitive actions to build a compact and efficient representation of long actions by maintaining the discriminative sample information and removing redundant information which is due to task repetitiveness. We show that by partitioning an input sequence based on repetition and by treating each repetition as a discrete sample, HAR models can achieve an increase of up to 4% in action recognition accuracy. Additionally, we investigate the relation between the dataset and action set attributes with this strategy and explore the conditions under which the utilization of repetitiveness for input sequence sampling, is a useful preprocessing step in HAR. Finally, we suggest deep NN design directions that enable the effective exploitation of the distinctive action-related information found in repetitiveness, and evaluate them with a simple deep architecture that follows these principles.
Linear Programming Bounds for Approximate Quantum Error Correction Over Arbitrary Quantum Channels

While quantum weight enumerators establish some of the best upper bounds on the minimum distance of quantum error-correcting codes, these bounds are not optimized to quantify the performance of quantum codes under the effect of arbitrary quantum channels that describe bespoke noise models. Herein, for any Kraus decomposition of any given quantum channel, we introduce corresponding quantum weight enumerators that naturally generalize the Shor-Laflamme quantum weight enumerators. We establish an indirect linear relationship between these generalized quantum weight enumerators by introducing an auxiliary exact weight enumerator that completely quantifies the quantum code's projector, and is independent of the underlying noise process. By additionally working within the framework of approximate quantum error correction, we establish a general framework for constructing a linear program that is infeasible whenever approximate quantum error correcting codes with corresponding parameters do not exist. Our linear programming framework allows us to establish the non-existence of certain quantum codes that approximately correct amplitude damping errors, and obtain non-trivial upper bounds on the maximum dimension of a broad family of permutation-invariant quantum codes.
A class of 1-generator right Quasi-polycyclic Codes

In the present paper, we interest in right <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell$</tex> -quasi-polycyclic <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$((\vec{a}, \ell)$</tex> - QP) codes of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n= ml$</tex> induced by the vector <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vec{a}=(a_{0}, a_{1},\ldots, a_{m-1})\in \mathbb{F}_{q}^{m}$</tex> , over the finite field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{q}$</tex> , which forms a generalization of quasi-cyclic, quasi-negacyclic and quasi-twisted codes. After recalling the necessary background on polycyclic and QP codes, we discuss the structure and the enumeration of the 1-generator <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell$</tex> -QP codes. Finally, we give some examples as applications of some of the results using the SageMath software.
PRAM: Penalized Resource Allocation Method for Video Services

Abstract The human visual system response to picture quality degradation due to packet loss is very different from the responses of objective quality measures. While video quality due to packet loss may be impaired by at most for one Group of Pictures (GOP), its subjective quality degradation may last for several GOPs. This has a great impact on resource allocation strategies, which normally make decisions on instantaneous conditions of multiplexing buffer. This is because, when the perceptual impact of degraded video quality is much longer than its objective degradation period, any assigned resources to the degraded flow is wasted. This paper, through both simulations and analysis shows that, during resource allocation, if the quality of a video stream is significantly degraded, it is better to penalize this degraded flow from getting its full bandwidth share and instead assign the remaining share to other flows preventing them from undergoing quality degradation.
Next generation internet oriented online English teaching system

With the continuous development of information technology all around this world, great changes have taken place in the teaching methods of English courses in colleges and universities. The implementation of online English teaching through networking and information technology has become an inevitable development trend in the future. Therefore, this paper proposes a multimedia English teaching system based on Internet virtual technology, which aims to further expand the implementation path of English teaching in colleges and universities and improve the information level of English teaching. First of all, this paper analyzes the working principle of the English teaching classroom distance teaching system, and constructs the three‐tier architecture of the English teaching classroom distance teaching system. Then, the system is designed based on Internet technology, and the output control of long‐distance multimedia teaching information is realized by using the core control module of Internet. Finally, the virtual teaching environment is created by using virtualization technology to realize the interaction between scene objects and users, and show the teaching effect smoothly. The final experimental results show that the designed system can interact in real time. It improves the fun of students' English practice and teaching effect, and the performance of the system is stable.
Robust projection twin support vector machine via DC programming

In this paper, we propose a robust projection twin support vector machine (RPTSVM), where a new truncated L2-norm distance measure is applied to the with-class scatter to boost the robustness of the classifier when encountering a large number of outliers. In order to further improve the robustness of the model, chance constraints are employed to specify the lower bound of the probability that the distance from the projected samples to the projection of the other class centre is at least one. RPTSVM considers a pair of non-convex non-smooth problems with chance constraints. To solve these difficult problems, a newly designed method based on the difference of convex functions (DC) programs approach is presented. Extensive experiments on artificial datasets and benchmark datasets demonstrate the robustness and feasibility of our proposed RPTSVM.
Introduction by Judea Pearl

chapter Share on Introduction by Judea PearlProbabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 215–220https://doi.org/10.1145/3501714.3501731Online:04 March 2022Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Touch-behavioral Authentication on Smartphones using Machine Learning

The traditional authentication approaches for smartphones, such as PIN code or pattern-based password, are vulnerable of password hacking in public by shoulder-suffering or smudge attack. On the other side, advanced authentication approaches, such as fingerprints or retina-based recognition, required specific hardware or high computational power. In this work, we propose using the advancements in machine learning (ML) for providing the authentication mechanism without the requirement of any additional hardware. We propose the usage of users’ touch interaction behavior on smartphone screen to provide the required authentication mechanism. We propose the solution in two modes, i.e., using the supervised ML technique where the system is trained and then authorized the legitimate user using a set of simple shapes, and using the unsupervised ML technique where the system is trained on a user’s free touch interaction with the underlying device. Moreover, we conducted a preliminary user study with our supervised learning based system.
Deep Autoencoder Multi-Exposure HDR Imaging

Recently, in the era of photography, due to capturing images with limited dynamic range by cameras, High Dynamic Range (HDR) imaging has engrossed people’s attention because HDR pictures present more details and better luminance than images with Low Dynamic Range (LDR). Moreover, produced HDR images by a single LDR image cannot reconstruct details appropriately, and therefore, in this research, a deep learning method is proposed to generate an HDR picture by multiple LDR pictures with different exposures. The experiments and results illustrate that the proposed algorithm performs better than the other methods in quantitative and visual comparison.
Dictionary-based transfer learning with Universum data

Recently, transfer learning is a popular method in machine learning, which transfers the knowledge learned from source task into target task. In practice, we can obtain the third-class examples except for the positive samples or negative samples, which are called Universum data, and Universum data can improve the performance of the classifier. In this paper, we propose a dictionarybased transfer learning with Universum data method, named U-DTL. In the proposed method, we first introduce the Universum data into the proposed model by the ∊-insensitive loss. We then embed two dictionaries for the source and target domains into a new model, and put forward the similarity constraint for dictionaries between both domains to determine the relationship among samples of source and target domains. Further, we use the gradient-based optimization and SVD algorithm to alternately optimize and update the dictionaries, and utilize Lagrangian function to iteratively optimize the proposed U-DTL model to obtain the classifier. Finally, the statistic result of Wilcoxon-test has shown that the proposed U-DTL method has the better performance than previous methods. And we have conducted extensive experiments on the benchmark datasets to evaluate the performance of the proposed U-DTL method and baselines. The results show that the proposed U-DTL method makes the better performance than previous methods.
Digital Party Building Platform Based on Hybrid Cloud Technology and Multi-Center Data

In the research process of this subject, an advanced digital party building platform is designed based on Web network technology. The management scheme integrated with the platform and Internet will produce a more superior management mode than the traditional party construction work, so as to advance the propaganda work of party construction among college students. Digitization party building activity network exchange platform. Carry out party building activities mainly related activities, so that more party members and the masses to participate in them, and exchange and interaction. In the operation interface, the system provides a variety of functional sub-interface, such as query, report and macro, these functional modules can greatly speed up the operation efficiency. Using JSP technology, can make dynamic website development more simple and convenient. Because the JAVA language has the characteristics of cross-platform and high encapsulation, you can design the logic function and the website interface separately.
Counterfeit Regulation through Machine Learning Approach and Deployment in Dockers

Money is very essential in today&#x2019;s world to lead a good &#x0026; prosperous life. Money Mints are responsible for the printing of banknotes for a Nation. Dissimilitude here mentioned refers to unrestrained reproduction of currency with the help of advanced technology accessible to all. The act of manufacturing replicated currency without the authenticity of the State is called Counterfeiting. Fake Notes are being produced with many features that can&#x2019;t be easily recognized by us and are similar to original Bank Notes. Therefore, an efficient mechanism is to be engineered for regulating the Counterfeit of Money. This paper implements Wavelet Transformed images of banknotes. Supervised Learning Classifiers, Random Forest &#x0026; Naive Bayes are compared &#x0026; used to predict original one. Performance Evaluation is based on parameters such as Accuracy. A User-Friendly Interface is designed using Flask, Flagger to view the predictions made by the Model and Docker for environment standardization.
Model of Information System for Application of Blockchain Technologies

Abstract This article describes the current security problems of blockchain networks, identifying the least explored area. The concept of an information system for investigating blockchain incidents is presented. An example of stealing bitcoins from a cryptocurrency wallet was taken as input. This example contains a fairly large number of blocks and transactions, and everything needs to be studied. An overview of the existing vulnerabilities in the Ethereum blockchain platform is presented. A brief description of the basic principles of operation of two largest cryptocurrency platforms - Bitcoin and Ether - is given. Examples of possible incidents that are widespread among users of blockchain systems are considered. The architecture of the designed solutions is described. The first experimental results obtained on the basis of the implementation of the designed algorithms are presented. It is concluded that iterating over a large number of transactions takes a lot of time; in the future, it is planned to apply machine learning methods to analyze such a large data array.
E–H

To regulate →electronic money, the European Commission adopted the second e-money directive 2009/110/EC in 2009, which was incorporated into the →payment services supervision act in 2011. The directive defines the characteristics of e-money transactions as well as the type of e-money institutions, which have to meet lower capital requirements compared to a →banking license and thus should facilitate the market entry for new providers.
Recognition and Prediction of Badminton Attitude Based on Video Image Analysis

Among many sports, badminton is one of the most popular events and it is deeply loved by people. However, there is relatively little research on pose recognition and prediction of badminton, so this paper uses video image analysis to perform badminton action recognition prediction and action classification. In order to better realize the lower limb movement pattern recognition and motion posture prediction of badminton players, this paper chooses BP neural network algorithm to establish a badminton motion posture recognition and prediction model based on video image analysis. The simulation results of the models are compared, and it is found that the recognition of the motion pose and the prediction model established by the neural network are accurate, and they almost coincide with the actual motion trend. In this paper, a face detection and recognition framework is established. The algorithm is used to filter the pictures of the input recognition network. Through calculation, the average accuracy rate of the face detection algorithm realized in this paper can reach 92.6%. This shows that the face detection algorithm implemented in this paper basically meets the standards. In this paper, a single inertial sensor is used to classify and recognize badminton movements using sensors located on the right wrist, left wrist, waist, and right ankle. This shows that the right wrist is the best position and achieves different strokes.
Development of a software model for quantum computations and simulation of quantum algorithms operation based on Elbrus platform

A software model for quantum computations has been developed and described within the scope of the study. Using the developed model, an experiment was carried out on the Elbrus-801 computing system. During the experiment, the operation of different quantum circuits and algorithms was simulated. The developed model can be used for developing and debugging computationally complex algorithms that may be useful for computational tasks related to aerospace defence applications.