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Multi-branch selection fusion fine-grained classification algorithm based on coordinate attention localization

Multi-branch selection fusion fine-grained classification al...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Feng Zhang Gaocai Wang Department of Computer and Electronic Information Guangxi University Nanning China

Object localization has been the focus of research in FGVC(Fine-Grained Visual Categorization). With the aim of improving the accuracy and precision of object localization in multi-branch networks, as well as the robustness and universality of object localization methods, our study mainly focus on how to combines coordinate attention and feature activation map for target localization. the model in this paper is a three-branch model including raw branch, object branch and part branch. the images are fed directly into the raw branch. CAOLM is used to localize and crop objects in the image to generate the input for the object branch. APPM is used to propose part regions at different scales. the three classes of input images undergo end-to-end weakly supervised learning through different branches of the network. the model expands the receptive field to capture multi-scale features by SB-ASPP. It can fuse the feature maps obtained from the raw branch and the object branch with SBBlock, and the complete features of the raw branch are used to supplement the missing information of the object branch. Extensive experimental results on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets show that our method has the best classification performance on FGVC-Aircraft and also has competitive performance on other datasets. Few parameters and fast inference speed are also the advantages of our model.

关键词： Location awareness Visualization Fuses Annotations Supervised learning Crops Robustness

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EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. this paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

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Learning based Content Caching for Wireless networks 34

Learning based Content Caching for Wireless Networks

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34th International conference on Information Networking (ICOIN)

作者： Masood, Arooj Lakew, Demeke Shumeye Cho, Sungrae Chung Ang Univ Sch Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781728141985

In recent years, the proliferation of smart mobile devices and multimedia applications has resulted in explosively increasing multimedia traffic over next-generation wireless networks. To tackle such an explosive traffic load, offloading network traffic and proactively caching contents at the edge of the network i.e., small BS (SBS) has been envisioned as an effective solution. However, proactive caching requires accurate predictions on user requests where it is unlikely to provide accurate predictions on dynamic content requests in highly mobile wireless networks. To alleviate this problem, we propose a learning-based content caching scheme, where each small BS learns space-time popularity dynamics by exploiting a multi-armed bandit learning agent in a distributed manner. the small BS determines popularity of requested contents using Zipf's distribution, shares its local popularity knowledge with immediate connected (i.e., one hop neighbor) small BSs, and then performs learning to estimate space-time popularity dynamics of contents for proactive caching based on the long-term observations of the requests.

关键词： content caching wireless cellular networks multi-armed bandits

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EMAS: Efficient Meta Architecture Search for Few-Shot Learning

EMAS: Efficient Meta Architecture Search for Few-Shot Learni...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Dongkai Liu Jiaxing Li Honglong Chen Baodi Liu Xiaoping Lu Weifeng Liu College of Oceanography and Space Informatics China University of Petroleum (East China) Qingdao China College of Control Science and Engineering China University of Petroleum (East China) Qingdao China COSMOPlat Institute of Industrial Intelligence Qingdao China

With the progress of few-shot learning, it has been scaled to many domains in the real world which have few labeled data, such as image classification and object detection. Many efforts for data embedding and feature combination have been made by designing a fixed neural architecture that can also be extended to variable and adaptive neural architectures for better performance. Recent works leverage neural architecture search technique to automatically design networks for few-shot learning but it requires vast computation costs and GPU memory requirements. this work introduces EMAS, an efficient method to speed up the searching process for few-shot learning. Specifically, we build a supernet to combine all candidate operations and then adopt gradient-based methods to search. Instead of training the whole supernet, we adopt Gumbel reparameterization technique to sample and activate a small subset of operations. EMAS handles a single path in a novel task adapted with just a few steps and time. A novel task only needs to learn fewer parameters and compute less content. During meta-testing, the task can well adapt to the network architecture although only with a few iterations. Empirical results show that EMAS yields a fair improvement in accuracy on the standard few-shot classification benchmark and is five times smaller in time.

关键词： Training Memory management Graphics processing units computer architecture Object detection Learning (artificial intelligence) Network architecture

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Data Fusion Method Exploiting Data Correlation for Physical Wireless Parameter Conversion Sensor networks 34

Data Fusion Method Exploiting Data Correlation for Physical ...

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34th International conference on Information Networking (ICOIN)

作者： Natsume, Kohei Ohta, Mai Takyu, Osamu Fujii, Takeo Univ Electrocommun Adv Wireless & Commun Res Ctr AWCC 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan Fukuoka Univ Jonan Ku 8-19-1 Nanakuma Fukuoka 8140180 Japan Shinshu Univ 4-17-1 Wakasato Nagano 3808553 Japan

ISBN: (纸本)9781728141985

Physical wireless parameter conversion sensor networks (Phy-C-SN) were proposed to gather information at high time efficiency. In this method, a fusion center can simultaneously receive multiple signals from sensor nodes (SNs). It is because each SN transmits a subcarrier of orthogonal frequency division multiplexing (OFDM) which corresponds to its sensing information. It is important for wireless sensor networks to decrease the power consumption of SNs in order to prolong the network lifetime. In the proposed method, SNs are divided into clusters based on the similarity of their sensing values and only an SN of each cluster transmits its data. the SNs in the same cluster transmit in turns so that their power consumptions are fair. In a fusion center, the missing data is interpolated with the data from SNs belong to the same cluster. the computer simulation results show that the number of transmission is decreased while the error is suppressed in this scheme. If the allowable error is set 0.2, 0.5 degrees C, the total number of transmissions within the network is decreased by 20 % and 31 % in the proposed method compared with that of the conventional method and 98.1 % and 97.8 % of the transmissions are kept less than or equal to the acceptable error, respectively.

关键词： Wireless Sensor networks Phy-C-SN

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Device Behavior Identification in Encrypted Home Security Camera Traffic

Device Behavior Identification in Encrypted Home Security Ca...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Shuhe Liu Xiaolin Xu Zhefeng Nan Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center Beijing China

Home security cameras have become one of the most popular IoT devices due to rigid demand and low cost. However, these devices have become a disaster area where security issues such as cyberattacks and privacy breaches often occur. Researchers and intruders often employ traffic behavior analyzing methods to mine vulnerabilities. Nevertheless, the content transmitted by the HSC device contains a lot of dynamic interference video traffic, so it is hard to mine the behavior information of the HSC device from it. In contrast, the HSC device's non-TLS one-way response packets carry more efficient behavior information. therefore, we propose an approach to identify device behavior based on the features of one-way response packets in non-TLS traffic. Based on the functional characteristics of the HSC device, we have a more fine-grained type division of behaviors, including eight behaviors and five states. In addition, we propose an automatic labeling approach based on countercurrent and operation logs for the problem of tedious and inaccurate manual labeling. Based on the features of three attributes, we compared the recognition effects of nine classifiers on two datasets, the real-world dataset and the IMC 2019 payload public dataset. Finally, the CNN-based classifier can achieve the most desirable identification effect with an accuracy rate of 97.47%, a recall rate of 97.42%, and an F1 score of 97.4%. the results show that the proposed approach can accurately identify the behavior and state of HSC at a fine-grained level. Moreover, this work has a significant reference value for device anomalous behavior detection and threat awareness.

关键词： Neural networks Interference Cameras Privacy breach Behavioral sciences Object recognition Labeling

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Visualizing deep networks by optimizing with integrated gradients 34

Visualizing deep networks by optimizing with integrated grad...

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34th AAAI conference on Artificial Intelligence, AAAI 2020

作者： Qi, Zhongang Khorram, Saeed Fuxin, Li School of Electrical Engineering and Computer Science Oregon State University Applied Research Center Pcg Tencent

ISBN: (纸本)9781577358350

Understanding and interpreting the decisions made by deep learning models is valuable in many domains. In computer vision, computing heatmaps from a deep network is a popular approach for visualizing and understanding deep networks. However, heatmaps that do not correlate with the network may mislead human, hence the performance of heatmaps in providing a faithful explanation to the underlying deep network is crucial. In this paper, we propose I-GOS, which optimizes for a heatmap so that the classification scores on the masked image would maximally decrease. the main novelty of the approach is to compute descent directions based on the integrated gradients instead of the normal gradient, which avoids local optima and speeds up convergence. Compared with previous approaches, our method can flexibly compute heatmaps at any resolution for different user needs. Extensive experiments on several benchmark datasets show that the heatmaps produced by our approach are more correlated with the decision of the underlying deep network, in comparison with other state-of-the-art approaches. ©2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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U-Net Based Discriminator for Real-World Super-Resolution

U-Net Based Discriminator for Real-World Super-Resolution

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Kevin Ian Ruiz Vargas Fidel Alejandro Guerrero Pena Pedro Diamel Marrero Fernandez Leonardo Lanfranchi Ing Jyh Tsang Tsang Ing Ren Centro de Informática Universidade Federal de Pernambuco Recife Brasil LIVIA/ETS Montreal Canada Centro de Informática CESAR Universidade Federal de Pernambuco Recife Brasil Duke University Pratt School of Engineering Durham USA Department of Computer Science IDLab University of Antwerp-IMEC Antwerpen Belgium

In recent years, single image super-resolution (SISR) deep learning techniques have achieved remarkable improvements in recovering a high-resolution (HR) image from an observed low-resolution (LR) input. Nevertheless, these proposed methods fail in many real-world scenarios since their models are usually trained using a pre-defined degradation process from HR ground truth images to LR ones. To address this issue, new architectures have been proposed focusing on adopting more complicated degradation models to emulate real-world degradation achieving prominent performance but still limited to certain kinds of inputs and dropping considerably in other cases. In this paper, we present a GAN structure for blind super-resolution tasks, applying a technique that has not been very commonly used in SISR proposals: a U-Net architecture as a discriminator of the GAN network. Adding this structural change will encourage the discriminator to focus more on semantic and structural changes between real and fake images and to attend less to domain-preserving perturbations. In addition, the loss function of the generator was modified by adding the LPIPS loss function for the perceptual loss and a per-pixel consistency regularization technique based on the CutMix data augmentation. Numerous novel solutions that have been proposed recently involve powerful deep learning techniques. the proposed model was trained using the DF2K dataset employing a degradation framework for real-world images by estimating blur kernels and real noise distributions to obtain more realistic LR samples. Finally, we present a benchmark comparing our results with other methods in the state-of-the-art. the commonly-used evaluation metrics for image restoration PSNR, SSIM, and LPIPS were used for this evaluation.

关键词： Degradation Measurement Deep learning Training Superresolution Generative adversarial networks Generators

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QDN: An Efficient Value Decomposition Method for Cooperative Multi-agent Deep Reinforcement Learning

QDN: An Efficient Value Decomposition Method for Cooperative...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Zaipeng Xie Yufeng Zhang Pengfei Shao Weiyi Zhao Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer and Information Hohai University Nanjing China The University of Hong Kong Hong Kong China

Multi-agent systems have recently received significant attention from researchers in many scientific fields. the value factorization method is popular for scaling up cooperative reinforcement learning in multi-agent environments. However, the approximation of the joint value function may introduce a significant disparity between the estimated and actual joint reward value function, leading to a local optimum for cooperative multi-agent deep reinforcement learning. In addition, as the number of agents increases, the input space grows exponentially, negatively impacting the convergence performance of multi-agent algorithms. this work proposes an efficient multi-agent rein-forcement learning algorithm, QDN, to enhance the convergence performance in cooperative multi-agent tasks. the proposed QDN scheme utilizes a competitive network to enable the agents to learn the value of the environmental state without the influence of actions. Hence, the error between the estimated joint reward value function and the actual joint reward value function can be significantly reduced, preventing the emergence of sub-optimal actions. Meanwhile, the proposed QDN algorithm utilizes the parametric noise on the network weights to introduce random-ness in the network's weights so that the agents can explore the environments and states effectively, thereby improving the convergence performance of the QDN algorithm. We evaluate the proposed QDN scheme using the SMAC challenges with various map difficulties. Experimental results show that the QDN algorithm excels in the convergence speed and the success rate in all scenarios compared to some state-of-the-art methods. Further experiments using four additional multi-agent tasks demonstrate that the QDN algorithm is robust in various multi-agent tasks and can significantly improve the training convergence performance compared with the state-of-the-art methods.

关键词： Training Deep learning Reinforcement learning Task analysis Artificial intelligence Convergence Multi-agent systems

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Joint Modeling of local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values 34

Joint Modeling of Local and Global Temporal Dynamics for Mul...

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34th AAAI conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence

作者： Tang, Xianfeng Yao, Huaxiu Sun, Yiwei Aggarwal, Charu Mitra, Prasenjit Wang, Suhang Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA IBM TJ Watson Yorktown Hts NY 10598 USA

ISBN: (纸本)9781577358350

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values;while exploring global patterns can alleviate such problem. thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework LGnet, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of LGnet for MTS forecasting with missing values and its robustness under various missing ratios.

关键词： Recurrent neural networks

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