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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3lab/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Mining Knowledge within Categories in Global and Local Fashion for Multi-label Text Classification

Mining Knowledge within Categories in Global and Local Fashi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Sheng Bi Peng Shi Yuntao Du Bin Jin Lingshuang Yu Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Multi-label text classification (MLTC) is an important task in natural language processing, which assigns multiple labels to each text in the dataset. Typical method like Binary Relevance (BR) is arguably the most intuitive solution for the task. It works by decomposing the multi-label learning task into a number of independent binary learning tasks while ignoring the correlation between labels. Recently, neural network models attract much attention. Researchers view the MLTC task as a sequence generation problem. Although some new methods based on generative model (e.g. sequence-to-sequence), such as novel decoder structure and various attention mechanisms, can improve the performance. These methods still have some short-comings, such as unreasonable loss function, unclear ordering of target labels. To address these limitations, we propose a simple and effective novel model, which combines the merits of neural network and BRs methods. Our model also takes into account the categories and levels of labels. We decompose the MLTC problem to binary classification, together with global and local extractor to avoid the impact of label ordering and cumulative error. Experimental results show that our model achieves an improvement of 3.0% micro-F1 and a reduction of 6.0% hamming loss on AAPD dataset compared with the state-of-the-art work. And obtained good performance on RCV1-V2 dataset.

关键词： Task analysis Data mining Encoding Neural networks Decoding computer science Natural language processing

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Momentum Accelerates the Convergence of Stochastic AUPRC Maximization

arXiv

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arXiv 2021年

作者： Wang, Guanghui Yang, Ming Zhang, Lijun Yang, Tianbao National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Hefei University of Technology Hefei230000 China Department of Computer Science The University of Iowa Iowa CityIA52242 United States

In this paper, we study stochastic optimization of areas under precision-recall curves (AUPRC), which is widely used for combating imbalanced classification tasks. Although a few methods have been proposed for maximizing AUPRC, stochastic optimization of AUPRC with convergence guarantee remains an undeveloped territory. A state-of-the-art complexity is O(1/ǫ5) for finding an ǫ-stationary solution. In this paper, we further improve the stochastic optimization of AURPC by (i) developing novel stochastic momentum methods with a better iteration complexity of O(1/ǫ4) for finding an ǫ-stationary solution;and (ii) designing a novel family of stochastic adaptive methods with the same iteration complexity, which enjoy faster convergence in practice. To this end, we propose two innovative techniques that are critical for improving the convergence: (i) the biased estimators for tracking individual ranking scores are updated in a randomized coordinate-wise manner;and (ii) a momentum update is used on top of the stochastic gradient estimator for tracking the gradient of the objective. The novel analysis of Adam-style updates is also one main contribution. Extensive experiments on various data sets demonstrate the effectiveness of the proposed algorithms. Of independent interest, the proposed stochastic momentum and adaptive algorithms are also applicable to a class of two-level stochastic dependent compositional optimization problems. © 2021, CC BY.

关键词： Stochastic systems

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Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation

arXiv

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arXiv 2021年

作者： Shi, Yinghuan Zhang, Jian Ling, Tong Lu, Jiwen Zheng, Yefeng Yu, Qian Qi, Lei Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China Department of Automation Tsinghua University China Tencent Jarvis Lab China School of Data and Computer Science Shandong Women’s University China School of Computer Science and Engineering Southeast University Nanjing211189 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210046 China

In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet in short) based on our uncertainty estimation and separate self-training strategy. In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN) that could be alternatively trained in an end-to-end manner. We have extensively evaluated our method on various segmentation tasks with publicly available benchmark datasets, including CT pancreas, MR endocardium, and MR multi-structures segmentation on the ACDC dataset. Compared with the current state of the art, our CoraNet has demonstrated superior performance. In addition, we have also analyzed its connection with and difference from conventional methods of uncertainty estimation in semi-supervised medical image segmentation. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

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Patch-Based Data Management for Dual-Copy Buffers in RAID-Enabled SSDs

Patch-Based Data Management for Dual-Copy Buffers in RAID-En...

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作者： Li, Jun Sha, Zhibing Cai, Zhigang Trahay, Francois Liao, Jianwei College of Computer and Information Science Southwest University of China Chongqing China Computer Science Department Telecom SudParis Evry-Courcouronnes France State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

While a dual-copy redundant array of independent disk (RAID) buffering can remove single point of failure in the buffer of RAID-enabled solid-state drives (SSDs), it loses a half of buffer space efficiency as a result. This article introduces a patch-based data management scheme for dual-copy buffers in RAID-enabled SSDs, for better improving buffer use efficiency. Different from conventional methods that caches data/parity chunks in the RAID buffer, it caches the modified portions of data chunks (called data patches) and then employs a cost-evaluation model to direct patch data replacement. Besides, in order to correctly respond to applications' read/write requests, we design the patch-based read/write mode by taking the original dirty data chunks and relevant up-to-date patches into account. Through a series of simulation tests on several realistic disk traces, we illustrate that patch-based data management can work for RAID-enabled SSDs to enhance buffer use efficiency, and then yield better I/O performance. Especially, our proposal can noticeably reduce the I/O latency by 39.1% and the number of block erases by 3.9% on average, in contrast to state-of-the-art approaches. © 1982-2012 IEEE.

关键词： Information management

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Multi-label text classification: Select distinct semantic understanding for different labels 3rd

Multi-label text classification: Select distinct semantic un...

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3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019

作者： Sun, Wei Ran, Xiangying Luo, Xiangyang Xu, Yunlai Wang, Chongjun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783030260743

Multi-label classification is a challenging task in natural language processing. Most of existing methods tend to ignore the semantic information of the text. Besides, different parts of the text contribute differently to each label, which is not considered by most of existing methods. In this paper, we propose a novel model for multi-label text classification. This model generates high-level semantic understanding representations with a multi-level dilated convolution. The multi-level dilated convolution effectively reduces dimension and expands the receptive fields without loss of information. Moreover, a hybrid attention mechanism is designed to capture most relevant information of the text based on trainable label embeddings and semantic understanding. Experimental results on the dataset AAPD and RCV1-V2 show that our model has significant advantages over baseline methods. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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作者： Lianyong Qi Fan Wang Xiaolong Xu Wanchun Dou Xuyun Zhang Mohammad R.Khosravi Xiaokang Zhou School of Computer Science Qufu Normal University School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Department of Computing Macquarie University Department of Computer Engineering Persian Gulf University Department of Electrical and Electronic Engineering Shiraz University of Technology Faculty of Data Science Shiga University

With the continuous development of Io T, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies,which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacyaware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy.

关键词： Traffic flow prediction Multi-party sensors Privacy Locality-Sensitive Hashing

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Experimental optimal verification of three-dimensional entanglement on a silicon chip

arXiv

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arXiv 2022年

作者： Xia, Lijun Lu, Liangliang Wang, Kun Jiang, Xinhe Zhu, Shining Ma, Xiaosong National Laboratory of Solid-state Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210093 China Key Laboratory of Optoelectronic Technology of Jiangsu Province School of Physical Science and Technology Nanjing Normal University Nanjing210023 China Institute for Quantum Computing Baidu Research Beijing100193 China Institute for Quantum Optics and Quantum Information Austrian Academy of Sciences Vienna1090 Austria

High-dimensional entanglement is significant for the fundamental studies of quantum physics and offers unique advantages in various quantum information processing (QIP) tasks. Integrated quantum devices have recently emerged as a promising platform for creating, processing, and detecting complex high-dimensional entangled states. A crucial step towards practical quantum technologies is to verify that these devices work reliably with an optimal strategy. In this work, we experimentally implement an optimal quantum verification strategy on a three-dimensional maximally entangled state using local projective measurements on a silicon photonic chip. A 95% confidence is achieved from 1190 copies to verify the target quantum state. The obtained scaling of infidelity as a function of the number of copies is - 0.5497±0.0002, exceeding the standard quantum limit of -0.5 with 248 standard deviations. Our results indicate that quantum state verification could serve as an efficient tool for complex quantum measurement tasks. © 2022, CC BY-NC-ND.

关键词： Quantum optics

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Evaluating Code Summarization with Improved Correlation with Human Assessment

Evaluating Code Summarization with Improved Correlation with...

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

作者： Juanjuan Shen Yu Zhou Yongchao Wang Xiang Chen Tingting Han Taolue Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Lab. for Novel Software Technology Nanjing University China School of Information Science and Technology Nantong University China Birkbeck University of London UK

ISBN: (纸本)9781665458146

Code summarization aims to automatically generate functionality descriptions of code snippets. Faithful metrics are needed to measure to which degree the machine generated summaries capture the semantics of the code snippets. Most commonly used metrics in code summarization, such as BLEU -4, METEOR, and ROUGE-L, originate from machine translation and text summarization, and have constantly been found to be inconsistent with human assessment. In this paper, we propose a novel evaluation metric, Consensus-based Code Summarization Evaluation (CCSE), which assigns different semantic weights to the n-grams of the summary. We also provide an algorithm to match the n-gram pairs from the reference and candidate based on the similarities. To validate the effectiveness of our proposed metric, we collect summary pairs from two public Java datasets and calculate the correlation coefficients between CCSE and the human evaluations. The experiment results show that, compared with BLEU-4, METEOR, and ROUGE-L, CCSE is more consistent with the scores assessed by human developers.

关键词： Measurement Java Codes Correlation Semantics software quality Reliability engineering

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DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning

arXiv

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arXiv 2024年

作者： Wang, Yifan Luo, Xiao Chen, Chong Hua, Xian-Sheng Zhang, Ming Ju, Wei School of Information Technology & Management University of International Business and Economics Beijing100029 China Department of Computer Science University of California Los Angeles90095 United States Terminus Group Beijing100027 China School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University Anker Embodied AI Lab Peking University Beijing100871 China College of Computer Science Sichuan University Chengdu610064 China

Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in the real world, labeled graph data is always limited or scarce. To address this issue, we focus on the semi-supervised graph classification task, which involves both supervised and unsupervised models learning from labeled and unlabeled data. In contrast to recent approaches that transfer the entire knowledge from the unsupervised model to the supervised one, we argue that an effective transfer should only retain the relevant semantics that align well with the supervised task. We introduce a novel framework termed DisenSemi in this paper, which learns disentangled representation for semi-supervised graph classification. Specifically, a disentangled graph encoder is proposed to generate factor-wise graph representations for both supervised and unsupervised models. Then we train two models via supervised objective and mutual information (MI)-based constraints respectively. To ensure the meaningful transfer of knowledge from the unsupervised encoder to the supervised one, we further define an MI-based disentangled consistency regularization between two models and identify the corresponding rationale that aligns well with the current graph classification task. Experiments conducted on various publicly available datasets demonstrate the effectiveness of our DisenSemi. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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