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arXiv 2022年

作者： Hu, Mengshun Jiang, Kui Nie, Zhixiang Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University China

Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates high-quality videos with higher resolution and higher frame rates. Existing advanced methods accomplish ST-VSR tasks through the association of Spatial and Temporal video super-resolution (S-VSR and T-VSR). These methods require two alignments and fusions in S-VSR and T-VSR, which is obviously redundant and fails to sufficiently explore the information flow of consecutive spatial LR frames. Although bidirectional learning (future-to-past and past-to-future) was introduced to cover all input frames, the direct fusion of final predictions fails to sufficiently exploit intrinsic correlations of bidirectional motion learning and spatial information from all frames. We propose an effective yet efficient recurrent network with bidirectional interaction for ST-VSR, where only one alignment and fusion is needed. Specifically, it first performs backward inference from future to past, and then follows forward inference to super-resolve intermediate frames. The backward and forward inferences are assigned to learn structures and details to simplify the learning task with joint optimizations. Furthermore, a Hybrid Fusion Module (HFM) is designed to aggregate and distill information to refine spatial information and reconstruct high-quality video frames. Extensive experiments on two public datasets demonstrate that our method outperforms state-of-the-art methods in efficiency, and reduces calculation cost by about 22%. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical resolving power

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Analyzing facial temporal patterns for face anti-spoofing 9

Analyzing facial temporal patterns for face anti-spoofing

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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Xia, Jingtian Song, Siyang Tang, Yan Shen, Linlin College of Computer Science and Software Engineering Shenzhen University China School of Computer Science The University of Nottingham United Kingdom

ISBN: (纸本)9781450387835

Face anti-spoofing is crucial as face recognition systems are widely challenged by the print attack and replay attack. Since facial temporal patterns of these attacks and real face are naturally different, this paper proposes two temporal modelling approaches to face anti-spoofing tasks. Firstly, we propose to analyze the temporal patterns of mid-level facial attributes in spectral domain, aiming to find the unique frequency patterns of real face and each attack, respectively. Then, we propose to directly model dynamics from the given data, by employing the dynamic image algorithm to generate low-level spatiotemporal representations of videos. In particular, we extract deep features from both global and local face parts, i.e. eyes, nose and mouth, and then fuse them for face spoofing detection. Then, a Convolutional Neural Networks (CNN) - Long Short-Term Memory (LSTM) units (CNN-LSTM) architecture is introduced to learn the high-level spatiotemporal features from dynamic facial images. The proposed approaches were evaluated on two benchmark databases. The results suggest the effectiveness of the second approaches, i.e. as low as 1.85% Equal Error Rate (EER) on CASIA-FASD and 0.00% Average Classification Error Rate (ACER) on REPLAY-ATTACK have been achieved. © 2020 ACM.

关键词： Long short-term memory

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Perceptions Toward Using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: Qualitative Exploration of Maori Views

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JMIR Formative Research 2024年 8卷 e59811页

作者： Jayamini, Widana Kankanamge Darsha Mirza, Farhaan Bidois-Putt, Marie-Claire Naeem, M. Asif Chan, Amy Hai Yan Department of Computer Science School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Software Engineering Faculty of Computing and Technology University of Kelaniya Kelaniya Sri Lanka School of Pharmacy Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Department of Data Science & Artificial Intelligence National University of Computer and Emerging Sciences (NUCES) Islamabad Pakistan

Background: Asthma is a significant global health issue, impacting over 500,000 individuals in New Zealand and disproportionately affecting Maori communities in New Zealand, who experience worse asthma symptoms and attacks. Digital technologies, including artificial intelligence (AI) and machine learning (ML) models, are increasingly popular for asthma risk prediction. However, these AI models may underrepresent minority ethnic groups and introduce bias, potentially exacerbating disparities. Objective: This study aimed to explore the views and perceptions that Maori have toward using AI and ML technologies for asthma self-management, identify key considerations for developing asthma attack risk prediction models, and ensure Maori are represented in ML models without worsening existing health inequities. Methods: Semistructured interviews were conducted with 20 Maori participants with asthma, 3 male and 17 female, aged 18-76 years. All the interviews were conducted one-on-one, except for 1 interview, which was conducted with 2 participants. Altogether, 10 web-based interviews were conducted, while the rest were kanohi ki te kanohi (face-to-face). A thematic analysis was conducted to identify the themes. Further, sentiment analysis was carried out to identify the sentiments using a pretrained Bidirectional Encoder Representations from Transformers model. Results: We identified four key themes: (1) concerns about AI use, (2) interest in using technology to support asthma, (3) desired characteristics of AI-based systems, and (4) experience with asthma management and opportunities for technology to improve care. AI was relatively unfamiliar to many participants, and some of them expressed concerns about whether AI technology could be trusted, kanohi ki te kanohi interaction, and inadequate knowledge of AI and technology. These concerns are exacerbated by the Maori experience of colonization. Most of the participants were interested in using technology to support their as

关键词： artificial intelligence asthma risk prediction health system development machine learning maori perceptions mobile phone

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A Small-Signal Stability Criterion for a Power System Dynamic Model with Constant Power Loads

SSRN

引用

SSRN 2024年

作者： Levron, Yoash Belikov, Juri Weiss, George The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Tallinn12618 Estonia School of Electrical Engineering Tel-Aviv University Tel-Aviv69978 Israel

This theoretical paper continues a long line of research works about the stability of power systems represented by reduced order models. We provide a small-signal stability criterion for a dynamic model that uses time-varying phasors, and is characterized by a lossless transmission network and constant power loads located anywhere in the network. A key concept in this study is the matrix H of the partial derivatives of the generator powers with respect to their relative angles. Our main result is that the local stability of any equilibrium point is completely determined by properties of this matrix H. More specifically, we show that an equilibrium point is asymptotically stable if H is positive definite, and is unstable if H is full-rank and not positive definite. In addition, we also prove several relations between the eigenvalues of H and the eigenvalues of the system's Jacobian matrix. These results are demonstrated on a number of case-studies, and compared with related earlier ones from the literature. © 2024, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

arXiv

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arXiv 2023年

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen China The Department of Computer Science Hong Kong Baptist University Hong Kong The Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This paper therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. Source code is available at https://***/Keke921/GCLLoss. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Analysis of Policy Anomalies in Distributed Firewalls

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International Journal of Network Security 2022年第4期24卷 617-627页

作者： Cheng, Yu-Zhu Shi, Qiu-Ying School of Software Changsha Social Work College Changsha410004 China School of Computer Science and Engineering Central South University Changsha410083 China

In order to solve the problem of anomaly analysis in distributed firewalls, a rule anomaly detection method based on spatial relationship comparison is proposed. Firstly, all firewall rules on the network path are mapped into the Firewall Design Matrix(FDM) in reverse order to form independent unit space sets. Secondly, the unit space overlap of all the upstream firewalls corresponding to the most downstream firewall FWd is obtained. Then this overlap is mapped to the rule space of FWd, where the uncovered area of FWd is the desired shadowing anomaly. For spuriousness anomaly, we first calculate the unit space overlap of all downstream firewalls corresponding to the most upstream firewall FWu and then map the overlap to the rule space of FWu, where the uncovered area of FWu is the desired spuriousness anomaly. Simulation results show that this method can accurately and efficiently detect all the rule shadowing anomalies and spuriousness anomalies. © Institute of Mathematical Statistics, 2022

关键词： Anomaly detection

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An IoT-Based Air Pollution Monitoring System for Smart City 4

An IoT-Based Air Pollution Monitoring System for Smart City

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4th International Conference on computer Systems, ICCS 2024

作者： Aubakirov, Alibek Makhanov, Kanat Issainova, Aliya Glazyrina, Natalya Razkhova, Bibigul Satybaldina, Dina School of Computer Science Hangzhou Dianzi University Hangzhou China Electronics and Telecommunications L.N Gumilyov Eurasian National University Department of Radio Engineering Astana Kazakhstan L.N. Gumilyov Eurasian National University Information Security Department Astana Kazakhstan L.N Gumilyov Eurasian National University Computer and Software Engineering Department Astana Kazakhstan L.N. Gumilyov Eurasian National University Department of Artificial Astana Kazakhstan Information Security and Cryptology Research Institute L.N. Gumilyov Eurasian National University Astana Kazakhstan

ISBN: (纸本)9798350366341

In this paper, we present the results of research on the design and development of a gas analytical unit, an electronic nose, with an autonomous power supply, as well as the function of wireless data transmission via the Long-Range Wide Area Networks protocol. We built the device on a circuit board featuring eight gas sensors (MQ series), an STM32F103C8T6 microcontroller with 12-bit analog-to-digital conversion, a RAK3172 wireless data transmission module, voltage dividers, and a USB serial adapter. It also has an HDC2080DMBR temperature and humidity sensor. We evaluated the electronic nose performance towards standard mixtures of carbon dioxide, carbon monoxide, nitrogen dioxide, ammonia, and hydrogen sulfide, which are important analytes in outdoor and indoor air quality monitoring. Multidimensional linear regression methods were employed to build calibration curves for electronic nose sensors, which allowed for converting the measured sensor responses into quantitative values of the concentrations of the analyzed substances. The estimated coefficients of determination, R2, approach 1 indicating a strong correlation between the approximating model and the original data. The multivariate chemiresistive responses were successfully clustered using the k-means algorithm, revealing clear distinctions between sensor data associated with different gas concentrations. The k-NN classifier, applied to the clustered data, achieved 100% accuracy in identifying gas concentrations, underscoring the potential of the proposed methodology for gas sensing applications. We proposed and implemented the architecture of a network of mobile gas analyzers as the Internet of Things devices for environmental monitoring using the VK Cloud Platform. © 2024 IEEE.

关键词： Analog to digital conversion

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Towards Understanding Third-party Library Dependency in C/C++ Ecosystem

arXiv

引用

arXiv 2022年

作者： Tang, Wei Xu, Zhengzi Liu, Chengwei Wu, Jiahui Yang, Shouguo Li, Yi Luo, Ping Liu, Yang School of Software Tsinghua University Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Institute of Information Engineering Chinese Academy of Sciences Beijing China

Third-party libraries (TPLs) are frequently reused in software to reduce development cost and the time to market. However, external library dependencies may introduce vulnerabilities into host applications. The issue of library dependency has received considerable critical attention. Many package managers, such as Maven, Pip, and NPM, are proposed to manage TPLs. Moreover, a significant amount of effort has been put into studying dependencies in language ecosystems like Java, Python, and JavaScript except C/C++. Due to the lack of a unified package manager for C/C++, existing research has only few understanding of TPL dependencies in the C/C++ ecosystem, especially at large scale. Towards understanding TPL dependencies in the C/C++ ecosystem, we collect existing TPL databases, package management tools, and dependency detection tools, summarize the dependency patterns of C/C++ projects, and construct a comprehensive and precise C/C++ dependency detector. Using our detector, we extract dependencies from a large-scale database containing 24K C/C++ repositories from GitHub. Based on the extracted dependencies, we provide the results and findings of an empirical study, which aims at understanding the characteristics of the TPL dependencies. We further discuss the implications to manage dependency for C/C++ and the future research directions for software engineering researchers and developers in fields of library development, software composition analysis, and C/C++ package manager. © 2022, CC BY.

关键词： Managers

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Optimized MARL for Latency-Sensitive Collaborative Service Placement in Edge Computing

Optimized MARL for Latency-Sensitive Collaborative Service P...

引用

IEEE International Conference on Web Services (ICWS)

作者： Guoqiang Liu Xiaolong Xu Xiyuan Xu Xinyue Ji Lianyong Qi Xuyun Zhang School of Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) Qingdao China School of Computing Macquarie University Sydney Australia

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

By distributing computing and storage resources to edge servers (ESs), edge computing enables service execution closer to requests, alleviating high latency caused by long-distance data transmission. The communication between ESs facilitates collaborative service placement (CSP) on multiple ESs, leading to latency reduction and thereby improving the quality of service. However, the discrepancies in service request distributions among ESs pose challenge to effective CSP, while limited prior knowledge further increases the difficulty in policy formation. Additionally, the delayed awareness of placement strategies among other ESs exacerbates the influence of non-Markovian nature. To overcome above challenges, a leader-follower based multi-agent reinforcement learning for CSP scheme, named LFMCSP, is proposed in this paper. Specifically, a CSP model is formulated, with ESs abstracted as intelligent agents within it. Then, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is utilized to explore potential patterns in complex requests by experience sharing. To achieve transparency of global states for all agents, the leader-follower architecture is integrated into MADDPG. Through rigorous experiments and comparisons with various baseline schemes, LFMCSP demonstrates effectiveness in reducing service execution delays.

关键词： Collaboration computer architecture Quality of service Reinforcement learning Space exploration Service-oriented architecture Delays Servers Intelligent agents Edge computing

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Feature Fusion from Head to Tail for Long-Tailed Visual Recognition

arXiv

引用

arXiv 2023年

作者： Li, Mengke Hu, Zhikai Lu, Yang Lan, Weichao Cheung, Yiu-Ming Huang, Hui Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China

The imbalanced distribution of long-tailed data presents a considerable challenge for deep learning models, as it causes them to prioritize the accurate classification of head classes but largely disregard tail classes. The biased decision boundary caused by inadequate semantic information in tail classes is one of the key factors contributing to their low recognition accuracy. To rectify this issue, we propose to augment tail classes by grafting the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T). We replace a portion of feature maps from tail classes with those belonging to head classes. These fused features substantially enhance the diversity of tail classes. Both theoretical analysis and practical experimentation demonstrate that H2T can contribute to a more optimized solution for the decision boundary. We seamlessly integrate H2T in the classifier adjustment stage, making it a plug-and-play module. Its simplicity and ease of implementation allow for smooth integration with existing long-tailed recognition methods, facilitating a further performance boost. Extensive experiments on various long-tailed benchmarks demonstrate the effectiveness of the proposed H2T. The source code is available at https://***/Keke921/H2T. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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