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Pattern Recognition and Tracking XXXIV 2023

作者： Chen, Yan Gong, Wenhui Alam, Mohammad S. Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China Department of Electrical Engineering and Computer Science Texas A&M University - Kingsville KingsvilleTX78363-8202 United States

ISBN: (数字)9781510661691

ISBN: (纸本)9781510661684

Makeup transfer aims to extract a specific makeup from a face and transfer it to another face, which can be widely used in portrait beauty, and cosmetics marketing. At present, existing methods can achieve the transfer of the entire facial makeup, but the quality of makeup transfer is not excellent because there may be a mismatch between the two images. In this paper, we propose a facial makeup transfer network based on the Laplacian pyramid, which can better preserve the facial structure from the source image and achieve high-quality transfer results. The model consists of three parts: makeup feature extraction, facial structure feature extraction, and makeup fusion. The makeup extraction part is used to extract the facial makeup from the reference image. And the facial structure feature extraction part is used to extract facial structure from the source image, in order to solve the loss of facial details when extracting facial structural features, we used the method based on Laplacian Pyramid. The makeup fusion part will fuse the facial makeup with facial structure features. Many experiments on the MT dataset have shown that this method can transfer makeup successfully without changing the original facial structure, and achieve advanced performance in various makeup styles. © 2023 SPIE.

关键词： Feature extraction

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RSP-gcForest: A Distributed Deep Forest via Random Sample Partition

RSP-gcForest: A Distributed Deep Forest via Random Sample Pa...

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2023 IEEE International Conference on big data, bigdata 2023

作者： Li, Mark Junjie Cai, Wenzhu Lin, Yigang Huang, Sunjie Huang, Joshua Zhexue Peng, Patrick Xiaogang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen Academy of Inspection and Quarantine China

ISBN: (纸本)9798350324457

Deep Forest, a powerful alternative to deep neural networks, has gained much attention due to its advantages, such as low complexity, minimal hyperparameter requirements, and strong application performance. In the current big data environment, where data volumes and model complexities are growing rapidly, distributed computing is needed to increase computational efficiency. Recently, a distributed deep forest approach, called BLB-gcForest (Bag of Little Bootstraps-gcForest), has been successful in reducing training instances within cascade forests, combining BLB and granularity segmentation, thus improving the computational efficiency and scalability of distributed deep forests. However, it still transmits the entire data set between layers and requires double sampling with BLB, limiting the amount of data and the scalability of resource utilization. This paper introduces a novel algorithm, RSP-gcForest, based on Random Sample Partition (RSP) to improve distributed deep forests computational efficiency and scalability. RSP-gcForest uses block-level samples that replace the full dataset, significantly reducing interlayer instance transmission and prediction within cascade forests. Additionally, RSP blocks are integrated with the segmentation granularity of cascade forests for ensemble learning, effectively addressing computational efficiency and resource constraints. We conducted experiments on four extensive datasets using Spark and evaluated performance across five key metrics. The results clearly show that RSP-gcForest, while maintaining high classification quality, surpasses state-of-the-art methods in terms of computational efficiency and resource utilization. Furthermore, it achieves superior load balancing, demonstrating its potential as a powerful tool in big data and distributed computing. © 2023 IEEE.

关键词： Deep forest Distributed Computing Ensemble Learning Random Sample Partition

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Batch Normalization Damages Federated Learning on NON-IID data: Analysis and Remedy 48

Batch Normalization Damages Federated Learning on NON-IID Da...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Yanmeng Shi, Qingjiang Chang, Tsung-Hui The Chinese University of Hong Kong School of Science and Engineering Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Tongji University School of Software Engineering Shanghai201804 China

ISBN: (纸本)9781728163277

Batch normalization (BN) has been widely used for accelerating the training of deep neural networks. However, recent findings show that, in the federated learning (FL) scenarios, BN can damage the learning performance when the clients have non-i.i.d. data. While several FL schemes have been proposed to address this issue, they still suffer a significant performance loss compared to the centralized scheme. In addition, none of them have explained how the BN impacts the FL convergence analytically. In this paper, we present the first convergence analysis to show that the mismatched local and global statistical parameters due to non-i.i.d data cause gradient deviation and it leads the algorithm to converge to a biased solution with a slower rate. To remedy this, we further present a new FL algorithm, called FedTAN, based on an iterative layer-wise parameter aggregation procedure. Experiment results are presented to show the superiority of FedTAN. © 2023 IEEE.

关键词： batch normalization convergence analysis Federated learning non-i.i.d. data

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Real-time crowd counting via mobile-friendly Vision Transformer network 34

Real-time crowd counting via mobile-friendly Vision Transfor...

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Pattern Recognition and Tracking XXXIV 2023

作者： Ji, Peirong Wu, Zhiwei Chen, Yan Alam, Mohammad S. Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China Department of Electrical Engineering and Computer Science Texas A&M University - Kingsville KingsvilleTX78363-8202 United States

ISBN: (数字)9781510661691

ISBN: (纸本)9781510661684

Accurate crowd counting in congested scenes remain challengeable in the trade-off of efficiency and generalization. For solving this issue, we propose a mobile-friendly solution for the network deployment in high response speed demand scenarios. In order to introduce the profound potential of global crowd representations to lightweight counting model, this work suggests a novel crowd counting aimed mobile vision transformers architecture (CCMTNet), which strives for enhancing the efficiency of the model universality in real-time crowd counting tasks on resource constrained computing devices. The framework of linear CNN network interpolation structure with self-attention blocks endows the model with the ability of local feature extraction and global high-dimensional crowd information processing with low computational cost. In addition, several experimental networks with different scales based on the proposed architecture are comprehensively verified to balance the accuracy loss as compressing the computing costs. Extensive experiments on three mainstream datasets for crowd counting tasks well demonstrate the effectiveness of this proposed network. Particularly, CCMTNet achieves the feasibility of reconciling the counting accuracy and efficiency in comparisons with traditional lightweight CNN networks. © 2023 SPIE.

关键词： Efficiency

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MSAGNet: Crowd Counting Network based on Multi-Scale Attention Grading 34

MSAGNet: Crowd Counting Network based on Multi-Scale Attenti...

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Pattern Recognition and Tracking XXXIV 2023

作者： Wu, Zhiwei Ji, Peirong Chen, Yan Alam, Mohammad S. Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China Department of Electrical Engineering and Computer Science Texas A&M University-Kingsville KingsvilleTX78363-8202 United States

ISBN: (数字)9781510661691

ISBN: (纸本)9781510661684

In recent years, great progress has been made in the study of crowd counting. Although the crowd counting networks being proposed to solve different problems have achieved satisfactory counting results, the differences of crowd density and scale in the same scene still degrade the overall counting performance. In order to deal with this problem, we propose a Multi-Scale Attention Grading Crowd Counting Network (MSAGNet), which focuses on different crowd densities in the scene by attention mechanism and fuses multi-scale information to reduce scale differences. Specifically, the grading attention feature obtaining module focuses on different densities of people in the scene by attention mechanism, and adaptively assigns corresponding weights to different density regions. Dense regions are given more weights, allowing the model to focus more on that part making the training of that region more accurate and effective. In addition, the multi-scale density feature fusion module fuses the feature maps containing density information to generate the final feature maps. The obtained feature maps contain attention information at different scales, which are density mapped to obtain the estimated density maps. This method can focus on different density regions in the same scene, and simultaneously fuse multi-scale information and attention weight, which can effectively solve the problem of counting dense regions that is difficult to calculate. Extensive experiments on existing crowd counting datasets (UCF_CC_50, ShanghaiTech, UCF-QNRF) show that our method can effectively improve the counting performance. © 2023 SPIE.

关键词： Grading

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One-Handed Gesture Recognition Based on Improved ST-GCN and CA Attention Mechanism

One-Handed Gesture Recognition Based on Improved ST-GCN and ...

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Automation, Robotics and Computer engineering (ICARCE), International Conference on

作者： Guixin Wang Department of Software Engineering School of Big Data and Computer GuangDong BaiYun University Guangzhou China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

The article takes one-handed gesture video as the research object, and mainly focuses on the recognition process and method of “isolated words” and static gesture mixtures. Firstly, the algorithm of extracting the key point positions of frame pictures is proposed by grouping rules of frame sequences of different gestures. This algorithm solves the problem of invalid gesture images affecting the recognition due to the redundancy of gesture images when extracting gestures from traditional gesture videos. Finally, a new network (ST-GCN-CA) is proposed, which improves the traditional spatio-temporal graph convolutional network (ST-GCN) and adds the CA attention mechanism. The method and network in this paper are applied to the Chinese isolated word sign language dataset (CSL-500) single-handed video, and the experimental results show that the recognition accuracies in the isolated word gesture video and the hybrid video reach 94.3% and 94.1%, respectively, which improves the recognition accuracies in comparison with other methods in the public domain.

关键词： Sign language Attention mechanisms Accuracy Image recognition Automation Graph convolutional networks Convolution Redundancy Robots

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Score-based causal feature selection for cancer risk prediction

Score-based causal feature selection for cancer risk predict...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Shanshan Huang Qingsong Li Lei Wang Yuanhao Wang Li Liu School of Big Data & Software Engineering Chongqing University Chongqing China

The primary goal of cancer risk prediction is to find dominant features, with each being responsible for cancer diagnosis. As a result, selected feature sets often converge to inexplicable and implausible results. Existing score-based causal models adopt a score function to learn a local causal structure to explicitly characterize a unique causal configuration as a variable number of nodes and links, which however suffer from nonconvex optimization and global incompleteness. This leads us to present a score-based approach to construct a causal network by optimizing a score function with a convex solution under the constraint of causal Markov property. It can be analytically shown that the resulting causal network satisfies the causal Markov property, and as a result, all cause-effect dependencies can be retained and are globally consistent. An additional node selector is introduced to choose the most dominant causal features. Empirical evaluations on three benchmarks and one in-house cancer risk datasets suggest our approach significantly outperforms the state-of-the-arts.

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Just-In-Time Method Name Updating With Heuristics and Neural Model

Just-In-Time Method Name Updating With Heuristics and Neural...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Zhenting Guo Meng Yan Hongyan Li Zhezhe Chen Weifeng Sun School of Big Data and Software Engineering Chongqing University Chongqing China

Ensuring the quality and conciseness of method names is pivotal for the readability and maintainability of source code. However, for developers, it often presents challenges, particularly during the course of code evolution. Throughout this process, developers sometimes may neglect to update the method name, resulting in inconsistency which could potentially mislead developers and introduce future bugs. In this paper, we propose the task of “Just-In-Time (JIT) Method Name Updating” which automatically performs method name updates to avoid inconsistent names and fix them before being introduced into code bases. Specifically, we propose an approach that combines heuristic rules and a neural model. The heuristic rule-based component mainly focuses on the single-token changes for our empirical findings that the proportion of single-token modifications is extensive, and often corresponds to code-indicative updates. The neural model-based component is a customized Seq2seq model considering code changes and the new method body’s token type. To evaluate our approach, we conduct extensive experiments on the collected dataset with over 108K method name-body co-change samples from popular Java projects. The results show that our method outperforms the three baselines on all metrics. In particular, our approach achieves a significant improvement in Accuracy and improves method generation baseline by 23.5%.

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GRAFRAUD: Fraud Detection using Graph databases and Neural Networks 4

GRAFRAUD: Fraud Detection using Graph Databases and Neural N...

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4th International Informatics and software engineering Conference, IISEC 2023

作者： Sayar, Alperen Arslan, Suayip Raina, Ajeet Singh Ertugrul, Seyit Cakar, Tuna Tam Finans R and D Data Science Istanbul Turkey Massachusetts Institute of Technology Big Data and Data Governance Massachusetts United States Docker Containerized Software Docker Inc. CA United States MEF University Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350318036

The issue of fraud has become a significant concern for many companies, particularly in the finance sector, but the traditional methods of detecting fraud are no longer adequate. Innovative technologies are necessary to identify complex fraudulent activities, and RedisGraph, a high-performance graph database, may offer a solution. With the assistance of neural networks, RedisGraph can accurately and efficiently detect fraudulent transactions in vast and intricate environments. Companies typically use a combination of Python and Oracle databases to design fraud detection systems. which provide robust data management and real time AI processing capabilities. These technologies allow to create fraud detection systems that can determine fraudulent activities in real-Time. But according to advancements of fraud methods only using of these systems not efficient nowadays. This article presents a proof of concept based on an essential use case of RedisGraph-powered neural networks in detecting financial fraud. It demonstrates the value of carefully employing Python and Oracle database to construct and deploy real-Time systems that can efficiently detect fraudulent activities. © 2023 IEEE.

关键词： Decision making

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Revisiting and Improving Retrieval-Augmented Deep Assertion Generation

arXiv

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arXiv 2023年

作者： Sun, Weifeng Li, Hongyan Yan, Meng Lei, Yan Zhang, Hongyu School of Big Data and Software Engineering Chongqing University Chongqing China

Unit testing validates the correctness of the unit under test and has become an essential activity in software development process. A unit test consists of a test prefix that drives the unit under test into a particular state, and a test oracle (e.g., assertion), which specifies the behavior in that state. To reduce manual efforts in conducting unit testing, Yu et al. proposed an integrated approach (integration for short), combining information retrieval with a deep learning-based approach, to generate assertions for a unit test. Despite being promising, there is still a knowledge gap as to why or where integration works or does not work. In this paper, we describe an in-depth analysis of the effectiveness of integration. Our analysis shows that: ① The overall performance of integration is mainly due to its success in retrieving assertions. ② integration struggles to understand the semantic differences between the retrieved focal-test (focal-test includes a test prefix and a unit under test) and the input focal-test, resulting in many tokens being incorrectly modified;③ integration is limited to specific types of edit operations (i.e., replacement) and cannot handle token addition or deletion. To improve the effectiveness of assertion generation, this paper proposes a novel retrieve-and-edit approach named EDITAS. Specifically, EDITAS first retrieves a similar focal-test from a pre-defined corpus and treats its assertion as a prototype. Then, EDITAS reuses the information in the prototype and edits the prototype automatically. EDITAS is more generalizable than integration because it can ① comprehensively understand the semantic differences between input and similar focal-tests;② apply appropriate assertion edit patterns with greater flexibility;and ③ generate more diverse edit actions than just replacement operations. We conduct experiments on two large-scale datasets and the experimental results demonstrate that EDITAS outperforms the state-of-the-art approaches,

关键词： Integration

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