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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Kan, Shengqi Fang, Wei Wu, Jiayi Sheng, Victor S. School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China Department of Computer Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783031067662

With the substantial improvement of people’s living standards, the amount of domestic garbage is increasing rapidly, and intelligent waste classification has become an urgent need in modern society. In this paper, we propose a real-time garbage detection model based on the improved YOLOv5 (you only look once version 5) algorithm. Firstly, mosaic data enhancement is introduced to enrich the background of the detection object and improve the robustness of the network. Secondly, Distance-IOU Non-Maximum Suppression is used to replace the traditional Non-Maximum Suppression to improve the suppression effect of prediction boxes. Finally, the network is further optimized from the aspect of activation function. The experimental results show that among the four versions of YOLOv5, their mean average precision(mAP) all reach more than 84%, The improved YOLOv5x has the best recognition effect, whose mAP reaches 89.4%, which is 2.1% higher than that of YOLOv5x and 5.3% higher than that of YOLOv5s. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards

arXiv

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arXiv 2023年

作者： Xue, Bo Wang, Yimu Wan, Yuanyu Yi, Jinfeng Zhang, Lijun Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen China Cheriton School of Computer Science University of Waterloo Waterloo Canada School of Software Technology Zhejiang University Ningbo China JD AI Research Beijing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Peng Cheng Laboratory Shenzhen China

This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose (1 + ϵ)-th moment is bounded for some ϵ ∈ (0, 1]. Although there exist methods for generalized linear bandits, most of them focus on bounded or sub-Gaussian rewards and are not well-suited for many real-world scenarios, such as financial markets and web-advertising. To address this issue, we propose two novel algorithms based on truncation and mean of medians. These algorithms achieve an almost optimal regret bound of Oe(dT 1+ 1 ϵ ), where d is the dimension of contextual information and T is the time horizon. Our truncation-based algorithm supports online learning, distinguishing it from existing truncation-based approaches. Additionally, our mean-of-medians-based algorithm requires only O(log T) rewards and one estimator per epoch, making it more practical. Moreover, our algorithms improve the regret bounds by a logarithmic factor compared to existing algorithms when ϵ = 1. Numerical experimental results confirm the merits of our algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Ethereum Smart Contract Representation Learning for Robust Bytecode-Level Similarity Detection 34

Ethereum Smart Contract Representation Learning for Robust B...

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34th International Conference on software Engineering and Knowledge Engineering, SEKE 2022

作者： Tian, Zhenzhou Huang, Yaqian Tian, Jie Wang, Zhongmin Chen, Yanping Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Department of Computer Science and Engineering Wright State University DaytonOH United States

ISBN: (纸本)1891706543

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed. However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust. Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code. This leads to a large number of similar contracts with potential security risks. Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum. In this paper, we design a learning-effective and cost-efficient model, called SmartSD, for Ethereum smart contract similarity detection. Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks. The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Smart contract

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Unsupervised Detailed Human Shape Estimation from Multi-view Color Images 11th

Unsupervised Detailed Human Shape Estimation from Multi-view...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Zheng, Huayu Wang, Kangkan Li, Wei Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Lab for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783030873578

This paper presents a novel framework to estimate detailed human body shape from color images in an unsupervised manner. It is a challenging task due to factors such as variations in human shapes, occlusion, and cloth details. The existing methods are mainly supervised and require a large number of ground truth real training data which is usually hard to obtain. To solve this problem, we propose an unsupervised detailed human shape estimation method from multi-view color images. Specifically, we first predict the depth map for the source view through robust photometric consistency with different views. Then, we predict the initial SMPL model from the color image and refine it by an iterative error feedback regressor based on point clouds of the predicted depth map. Finally, the refined SMPL model is deformed to fit the details (i.e., clothes and faces) on the point clouds to recover the detailed human shapes which are represented by adding a set of offsets to the SMPL model. The experimental results on different dataset demonstrate that our method outperforms the state-of-the-art methods and achieves higher reconstruction accuracy. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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Addressing Intermediate Verification Latency in Online Learning Through Immediate Pseudo-labeling and Oriented Synthetic Correction

Addressing Intermediate Verification Latency in Online Learn...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zixin Zhong Liyan Song Fengzhen Tang Bo Yuan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Faculty of Computing Harbin Institute of Technology Harbin China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In non-stationary data streams, the challenges of concept drift are further compounded by the issue of Intermediate Verification Latency (IVL), which can impede timely model adaptation. IVL refers to the finite delay between the arrival of data features and their corresponding labels. This delay could pose a significant challenge in adapting models to new concepts, ultimately hindering predictive performance. However, existing IVL approaches exhibit certain limitations. Some approaches passively wait for delayed labels, thereby overlooking temporarily unlabeled data. Other approaches employ pseudo-labeling for immediate model updates, but may risk losing valuable information when reverting model states to rectify previous pseudo-labeling mistakes. To overcome these limitations, we propose a novel approach called Micro-cluster based Immediate Pseudo-Labeling with Oriented Synthetic Correction (MIPLOSC). MIPLOSC leverages micro-cluster systems to effectively capture data distributions, thus facilitating its two core components: immediate pseudo-labeling and oriented synthetic correction. The immediate pseudo-labeling mechanism facilitates immediate utilization of temporarily unlabeled data, and the oriented synthetic correction mechanism enables finergrained rectification from previous erroneous pseudo-labels and concept drift, minimizing the loss of learned information. Experimental studies validated the effectiveness of MIPLOSC in addressing IVL, demonstrating its superiority over competing methods in both space consumption and predictive performance across varying degrees of label delay.

关键词： Adaptation models Costs Concept drift Noise Neural networks Predictive models Robustness

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Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chenhang Wang Yujian Jin Jiayi Liang YuHan Yang Shanling Nie Yang Hai Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Sydney Australia

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer.

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A Parameter-Independent Algorithm of Finding the Maximum Clique with Continuous-Time Quantum Walks Driven by Seidel Matrix

SSRN

引用

SSRN 2023年

作者： Li, Xi Chen, Xiao Hu, Shouwei Xu, Juan Liu, Zhihao School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China

The problem of searching the maximum clique(MC) in network analysis holds significant practical relevance. Quantum-based algorithms have recently emerged as promising approaches for addressing this problem. However, the performance of quantum-based algorithms heavily depends on the parameters of the quantum system and can vary significantly for different graph types. In order to tackle this issue, we propose an algorithm based on quantum walks. We initially demonstrate that Continuous-Time Quantum Walks (CTQW), driven by the Seidel matrix, offer valuable insights into the clique structure of a given graph, outperforming the conventional CTQW with the adjacency matrix Hamiltonian. Specifically, we conduct an in-depth analysis of CTQW applied to 4 special types of graphs, meticulously calculating the magnitudes of amplitude associated with different vertices. Our findings consistently reveal that the vertices belonging to the MC consistently exhibit the highest intensity at the frequency corresponding to the largest probability amplitude for these types of graphs. Considering the varying amplitudes of graph vertices, we propose an algorithm for determining the MC based on CTQW. We compare our algorithm with the simulated adiabatic bifurcation method, and the results indicate that our algorithm is parameter-independent and exhibits greater stability. © 2023, The Authors. All rights reserved.

关键词： Continuous time systems

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SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector

arXiv

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arXiv 2024年

作者： Mao, Qianchen Li, Qiang Wang, Bingshu Zhang, Yongjun Dai, Tao Chen, C.L. Philip The School of Software Northwestern Polytechnical University Taicang Campus Suzhou215400 China The School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 China

In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a pathway that preserves high-resolution features of sparse and tiny targets. However, it can result in redundant and expensive computations. To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets. Specifically, to cope with the computational redundancy issue, we employ a new dual-branch sparse decoder to restore the feature map. Firstly, the fast branch directly predicts a sparse map indicating potential small target locations (occupying only 0.5% area of the map). Secondly, the slow branch conducts fine-grained adjustments at the positions indicated by the sparse map. Additionally, we design an lightweight DO-RepEncoder based on reparameterization with the Downsampling Orthogonality, which can effectively reduce memory consumption and inference latency. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models while achieving faster inference speed and fewer parameters. For example, on the IRSTD-1K dataset, SpirDet improves MIoU by 4.7 and has a 7× FPS acceleration compared to the previous state-of-the-art model. The code will be open to the public. © 2024, CC BY.

关键词： Deep learning

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Onfocus detection: identifying individual-camera eye contact from unconstrained images

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science China(Information sciences) 2022年第6期65卷 5-16页

作者： Dingwen ZHANG Bo WANG Gerong WANG Qiang ZHANG Jiajia ZHANG Jungong HAN Zheng YOU School of Mechano-Electronic Engineering Xidian University State Key Laboratory of Precision Measurement Technology and Instruments Tsinghua University Beijing Jingzhen Medical Technology Ltd. Computer Science Department Aberystwyth University

Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not. Based on the behavioral research, the focus of an individual during face-to-camera communication leads to a special type of eye contact, i.e., the individual-camera eye contact, which is a powerful signal in social communication and plays a crucial role in recognizing irregular individual status(e.g., lying or suffering mental disease) and special purposes(e.g., seeking help or attracting fans). Thus,developing effective onfocus detection algorithms is of significance for assisting the criminal investigation,disease discovery, and social behavior analysis. However, the review of the literature shows that very few efforts have been made toward the development of onfocus detector owing to the lack of large-scale public available datasets as well as the challenging nature of this task. To this end, this paper engages in the onfocus detection research by addressing the above two issues. Firstly, we build a large-scale onfocus detection dataset, named as the onfocus detection in the wild(OFDIW). It consists of 20623 images in unconstrained capture conditions(thus called “in the wild”) and contains individuals with diverse emotions, ages, facial characteristics, and rich interactions with surrounding objects and background scenes. On top of that, we propose a novel end-to-end deep model, i.e., the eye-context interaction inferring network(ECIIN), for onfocus detection, which explores eye-context interaction via dynamic capsule routing. Finally, comprehensive experiments are conducted on the proposed OFDIW dataset to benchmark the existing learning models and demonstrate the effectiveness of the proposed ECIIN.

关键词： onfocus detection deep neural network capsule routing computer vision deep learning

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7-2: Truly Bi-stable Ferroelectric Liquid Crystal Based Modulators

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SID Symposium Digest of Technical Papers 2024年第1期55卷 49-52页

作者： Vigneshwaran Swaminathan Valerii Vashchenko Olena Vashchenko Debjyoti Bhadra Chris Mathew Hoi-sing Kwok Abhishek. K. Srivastava State Key Laboratory of Advanced Displays and Optoelectronics Technologies and Center for Display Research Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Road Kowloon Hong Kong

We developed a novel method for achieving long-term (>24 hours) true bi-stability in helix-free Ferroelectric Liquid Crystals (FLCs). Instead of employing asymmetric boundary conditions, traditionally used for achieving bi-stability, we utilized symmetric boundary conditions and carefully optimized the anchoring conditions on both the top and bottom substrates using the photoalignment technology. The key to the newly observed long term bi-stability is the twisted alignment structure. Surface roughness was found to play a crucial role in achieving sustained bi-stability. This breakthrough holds promise for developing truly bi-stable, fast modulators for photonic applications.

关键词： Bi-Stability Faster response time High efficiency High Contrast Modulators Memory effect photoalignment novel Ferroelectric Liquid Crystals

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