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International Conference on computer Supported Cooperative Work in Design

作者： Kaixin Qin Songtao Guo Guiyan Liu Yue Zeng College of Computer Science Chongqing University Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai Department of Computer Science and Technology The State Key Laboratory for Novel Software Technology Nanjing University

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Ensuring the quality of service (QoS) in the Space-Air-Ground Integrated Network (SAGIN) requires implementing appropriate service function chain (SFC) embedding strategies to provide reliable general services and AI services. However, the complicated topology, limited infrastructure resources, and the heterogeneity in hardware and software in SAGIN hinder existing reliable embedding solutions from delivering high QoS. To address the challenges, this paper takes into account the heterogeneous reliable-aware SFC embedding (HRSE) problem in SAGIN. Specifically, we formulate the HRSE problem as a nonlinear integer programming problem that is NP-hard. Subsequently, an algorithm called HRSFCEA, which consists of deep reinforcement learning (DRL) and greedy rules, is proposed to tackle the challenges. Initially, leveraging the autonomous decision-making capability of DRL, SFC embedding is executed to select backups efficiently. The proposed algorithm reduces frequent trial and error by modifying its actions. Next, the paper gives the algorithm analysis including the complexity and unreliable environment. Finally, simulation results based on real-world datasets demonstrate that the DRL scheme converges to high rewards with minimal differences across varying numbers of hidden layer neurons. The proposed algorithm can improve the acceptance rate by up to 82% while incurring no more than 5% additional cost compared to the state-of-art algorithms.

关键词： Service function chaining computer network reliability software algorithms Quality of service Space-air-ground integrated networks Reliability engineering Deep reinforcement learning software software reliability Topology

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Dynamic Graph Learning with Long and Short-Term for Multivariate Time Series Anomaly Detection 12

Dynamic Graph Learning with Long and Short-Term for Multivar...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: technology and Applications, IDAACS 2023

作者： Tian, Yuyin Gao, Rong Yan, Lingyu Liu, Donghua Ye, Zhiwei School of Computer Science Hubei University of Technology Wuhan430068 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China China Waterborne Transport Research Institue Information Center Beijing100080 China

ISBN: (纸本)9798350358056

Existing multivariate time series anomaly detection methods utilize graph neural networks to model inter-sensor dependencies and achieve satisfactory results. However, these methods often employ a predefined or self-learning static adjacency matrix for graph information aggregation, disregarding the dynamic characteristics of inter-sensor dependencies in the real world, which change over time and are observable at different time scales as dynamic changing graph structure information. Furthermore, existing techniques struggle to capture long-term temporal contextual relationships. To address these issues, we propose a Dynamic Graph Learning with Long and Short-term time model (DGL-LS) for multivariate time series anomaly detection. The network integrates a dynamic graph structure learning method among sensors in multi-time mode and long- and short-term temporal context information to enhance the prediction of multivariate time series anomalies. We propose a strategy for learning dynamic graphs that aggregates sensor node representations at various time intervals, and then uses an attention-based gating unit to evolve the node dynamic characterization and construct a series of adjacency matrices that describe the dynamic changes in graph structure over time. Moreover, we formulate a long-short time convolution to capture the long-term and short-term temporal dependencies within the time series. We demonstrate the superiority of our approach over other state-of-the-art methods by conducting extensive experiments on three publicly available datasets. © 2023 IEEE.

关键词： Anomaly Detection Graph Learning Multivariate Time Series Time Convolution

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MiTAR:a study on human activity recognition based on NLP with microscopic perspective

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Frontiers of computer science 2021年第5期15卷 93-110页

作者： Huichao MEN Botao WANG Gang WU School of Computer Science and Engineering Northeastern UniversityShenyang 110000China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210000China

Nowadays,human activity recognition is becoming a more and more significant topic,and there is also a wide range of applications for it in real world *** data is an important data source in engineering and *** present,some studies have been carried out in the field of human activity recognition based on sensor data in a macroscopic ***,many studies in this perspective face some *** pivotal limitation is uncontrollable data segment length of different kinds of *** feature and data form are also influencing *** paper carries out the study creatively on a microscopic perspective with an emphasis on the logic and relevance between data segments,attempting to apply the idea of natural language processing and the method of data symbolization to the study of human activity recognition and try to solve the problem *** this paper,several activity-element definitions and three algorithms are proposed,including the algorithm of dictionary building,the algorithm of corpus building,and activity recognition algorithm improved from a natural language analysis method,*** experiments on different aspects of this model are *** experiments are carried out on six complex and representative single-level sensor datasets,namely UCI Sports and Daily dataset,Skoda dataset,WISDM Phoneacc dataset,WISDM Watchacc dataset,Healthy Older People dataset and HAPT dataset,which prove that this model can be applied to different datasets and achieve a satisfactory recognition result.

关键词： human activity recognition NLP time series data analysis data symbolization

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Safe and Robust Subgame Exploitation in Imperfect Information Games 41

Safe and Robust Subgame Exploitation in Imperfect Informatio...

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41st International Conference on Machine Learning, ICML 2024

作者： Ge, Zhenxing Xu, Zheng Ding, Tianyu Meng, Linjian An, Bo Li, Wenbin Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore Microsoft Corporation RedmondWA United States Skywork AI Singapore School of Intelligence Science and Technology Nanjing University Suzhou Campus Jiangsu Suzhou China

Opponent exploitation is an important task for players to exploit the weaknesses of others in games. Existing approaches mainly focus on balancing between exploitation and exploitability but are often vulnerable to modeling errors and deceptive adversaries. To address this problem, our paper offers a novel perspective on the safety of opponent exploitation, named Adaptation Safety. This concept leverages the insight that strategies, even those not explicitly aimed at opponent exploitation, may inherently be exploitable due to computational complexities, rendering traditional safety overly rigorous. In contrast, adaptation safety requires that the strategy should not be more exploitable than it would be in scenarios where opponent exploitation is not considered. Building on such adaptation safety, we further propose an Opponent eXploitation Search (OX-Search) framework by incorporating real-time search techniques for efficient online opponent exploitation. Moreover, we provide theoretical analyses to show the adaptation safety and robust exploitation of OX-Search, even with inaccurate opponent models. Empirical evaluations in popular poker games demonstrate OX-Search's superiority in both exploitability and exploitation compared to previous methods. Copyright 2024 by the author(s)

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Enhance Training Objectives for Image Captioning with Decomposed Sequence-level Metric

Enhance Training Objectives for Image Captioning with Decomp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yehuan Wang Jiefeng Long Jian Hu Lin Shang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Image captioning aims to generate fluent and accurate descriptions for images. To evaluate the quality of captions, various metrics have been proposed. However, current metrics only assess captions at sequence-level, which overwhelms the distinctions between each token. Thus, existing objectives tend to treat each token equally, assigning them with identical weights in loss functions. Intuitively, key words in a caption carry the primary information and contribute more than other words to sequence-level metrics. They should be distinguished and weighted more during training. In this work, we propose to explicitly measure each word and guide the model to focus more on key words in captions. Firstly, we devise token-level CIDEr (CIDEr-T) as a new metric to quantify the importance of each word, by decomposing the sequence-level CIDEr into token-level granularity. CIDEr-T maintains consistency with CIDEr and shows the distinctions between tokens. Thus, we engage CIDEr-T scores of each token as their unique weights in the raw loss functions, which can bridge the gap between training and evaluation.

关键词： Measurement Training Accuracy Neural networks

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Automatic Identification and Extraction of Assumptions on GitHub 29

Automatic Identification and Extraction of Assumptions on Gi...

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29th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2023

作者： Yang, Chen Ma, Zinan Liang, Peng Liu, Xiaohua School of Artificial Intelligence Shenzhen Polytechnic Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)1891706578

In software development, due to the lack of knowledge or information, time pressure, complex context, and many other factors, various uncertainties emerge during the development process, leading to assumptions scattered in projects. Being unaware of certain assumptions can result in critical problems (e.g., system vulnerability and failures). The prerequisite of analyzing and understanding assumptions in software development is to identify and extract those assumptions with acceptable effort. In this paper, we proposed a tool (i.e., Assumption Miner) to automatically identify and extract assumptions on GitHub projects. To evaluate the applicability of Assumption Miner, we first presented an example of using the tool to mine assumptions from one large and popular deep learning framework project: the TensorFlow project on GitHub. We then conducted an evaluation of the tool. The results show that Assumption Miner can effectively identify and extract assumptions from the repositories on GitHub. Copyright © 2023 by KSI Research Inc. and Knowledge Systems Institute, USA.

关键词： Deep learning

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USee: Ultrasound-Based Device-Free Eye Movement Sensing

USee: Ultrasound-Based Device-Free Eye Movement Sensing

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Wen Cheng Mingzhi Pang Haoran Wan Shichen Dong Dongxu Liu Wei Wang State Key Laboratory for Novel Software Technology Nanjing University Nanjina China Department of Computer Science Princeton University Princeton USA

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

Eye movements play a significant role in human-computer interaction and are widely recognized as an essential health indicator, making their detection both appealing and technically challenging. In this paper, we present a system named USEE that achieves high-precision capture of weak and aperiodic eye movements by utilizing fine-grained and ubiquitous ultrasound signals, capturing both blinking and more subtle saccades. We first identify signal changes associated with eye movements by capturing the unique impact of blinking. Further, we establish a pioneering relationship between the residuals from signal decomposition and subtle eye movements. Utilizing inno-vative signal processing architectures, we mitigate interference and effectively extract eye movement features. Subsequently, we employ one-dimensional convolutional operations in place of signal cross-correlation, designing filters for motion category identification and a lightweight convolutional neural network for saccade direction classification. This enables our system to serve as a foundational sensing layer for eye movement tracking, applicable across diverse applications. We implement USEE on both a research-purpose platform and a commodity Raspberry Pi. Extensive experimental results demonstrate the effectiveness of our system, achieving 91% accuracy in saccade recognition and 94% in blink detection. The system proves robust, even in challenging scenarios with strong interference, such as the presence of moving pedestrians.

关键词： Human computer interaction Wireless communication Wireless sensor networks Ultrasonic imaging Tracking Interference Feature extraction Transceivers Sensors Signal resolution

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Knowledge-aware Multi-view Cross Learning for Edge-based Collaborative Recommendation 29

Knowledge-aware Multi-view Cross Learning for Edge-based Col...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Dai, Yang Meng, Shunmei Gu, Huanhuan Liu, Nan Tu, Longchuan NanJing University of Science and Technology School of Cyberspace Security Nanjing China NanJing University State Key Lab. for Novel Software Technology Nanjing China NanJing University of Science and Technology Department of Computer Science and Engineering Nanjing China Nanjing Sinovatio Technology Co. Ltd. Nanjing China

ISBN: (纸本)9798350330717

In order to effectively overcome the sparsity and cold start challenges of collaborative filtering in actual scenarios, and provide more accurate suggestions, knowledge graphs are commonly utilized in recommender systems. Graph neural networks are increasingly used in knowledge graph-based recommender systems. Nevertheless, conventional recommender systems overlook the relationship information between items and entities as well as the sparse supervision signal, potentially leading to a decline in the actual recommendation performance. In this investigation, we propose KMCL, a Knowledge-aware Multi-view Cross-learning framework for edge-based collaborative recommendation. In contrast to the typical manner of dealing with knowledge graphs, our approach investigates three different graph views to improve users' and items' embedding representations, respectively. Firstly, the merged user-item KG graph is utilized to integrate the multi-hop neighbor information of each user, with the Graph Attention Network assigning various weights to different neighbors. Secondly, Graph Neural Network collects the top-x neighbor nodes for learning in the collaborative view of user-item graph and item-entity graph. Finally, a cross module is developed to share high-level information between embeddings with the same features. In addition, we also introduce the idea of edge computing, which preprocesses and calculates the locally obtained user preferences first, and then inputs them into different views to improve the recommendation efficiency. Experimental results demonstrate that KMCL delivers better suggestions than state-of-the-art baseline models in delivering superior recommendations. Furthermore, our approach maintains higher accuracy in predicting outcomes in less familiar interaction scenarios. © 2023 IEEE.

关键词： Edge Computing Graph Attention Network Knowledge Graph Multi-view Cross Learning Recommender System

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Valar: Streamlining Alarm Ranking in Static Analysis with Value-Flow Assisted Active Learning 23

Valar: Streamlining Alarm Ranking in Static Analysis with Va...

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Proceedings of the 38th IEEE/ACM International Conference on Automated software Engineering

作者： Pengcheng Liu Yifei Lu Wenhua Yang Minxue Pan State Key Laboratory for Novel Software Technology Software Institute Nanjing University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9798350329964

Static analyzers play a critical role in program defects and security vulnerabilities detection. Despite their importance, the widespread adoption of static analysis techniques in industrial development faces numerous obstacles, among which the high rate of false alarms constitutes a significant one. To address this issue, we propose a novel approach called Valar, which performs alarm ranking for advanced value-flow analysis using the active learning technique. Active learning algorithms minimize the manual effort for alarm inspection by maximizing the effect of each user labeling in recognizing true/false alarms. Meanwhile, the value-flows provide Valar with a concise and comprehensive summary of the operational semantics about programs. Based on this, Valar is able to reason about the potential correlations between alarms and prioritize the most profitable unlabeled alarm. Additionally, the accuracy of Valar increases as more user labels are given and Valar's active learning model is further *** evaluate Valar on 20 real-world C/C++ programs using three value-flow based checkers. Our experimental results demonstrated that Valar significantly lowers the priorities of false alarms with most true alarms ranked high. Notably, Valar ranked all true alarms in the top 47% in 90% projects and ranked 90% true alarms in the top 22% in 75% projects. Furthermore, Valar has no requirement for pretraining and has a negligible computation time of less than 0.1s for each alarm prioritization.

关键词： static analysis

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AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance

AugRmixAT: A Data Processing and Training Method for Improvi...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Liu, Xiaoliang Shen, Furao Zhao, Jian Nie, Changhai Nanjing University National Key Laboratory for Novel Software Technology China Nanjing University Department of Computer Science and Technology China School of Electronic Science and Engineering Nanjing University China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Deep neural networks are powerful, but they also have short-comings such as their sensitivity to adversarial examples, noise, blur, occlusion, etc. Moreover, ensuring the reliability and robustness of deep neural network models is crucial for their application in safety-critical areas. Much previous work has been proposed to improve specific robustness. However, we find that the specific robustness is often improved at the sacrifice of the additional robustness or generalization ability of the neural network model. In particular, adversarial training methods significantly hurt the generalization performance on unperturbed data when improving adversarial robustness. In this paper, we propose a new data processing and training method, called AugRmixAT, which can simultaneously improve the generalization ability and multiple robustness of neural network models. Finally, we validate the effectiveness of AugRmixAT on the CIFAR-10/100 and Tiny-ImageNet datasets. The experiments demonstrate that AugR-mixAT can improve the model's generalization performance while enhancing the white-box robustness, black-box robustness, common corruption robustness, and partial occlusion robustness. © 2022 IEEE.

关键词： Neural network models

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