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Combining Trend-Based Loss with Neural Network for Air Quality Forecasting in Internet of Things

computer Modeling in Engineering & sciences 2020年第11期125卷 849-863页

作者： Weiwen Kong BaoweiWang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044China

Internet of Things(IoT)is a network that connects things in a special *** embeds a physical entity through an intelligent perception system to obtain information about the component at any *** connects various *** has the ability of information transmission,information perception,and information *** air quality forecasting has always been an urgent problem,which affects people’s quality of life *** far,many air quality prediction algorithms have been proposed,which can be mainly classified into two *** is regression-based prediction,the other is deep learning-based ***-based prediction is aimed to make use of the classical regression algorithm and the various supervised meteorological characteristics to regress themeteorological *** learning methods usually use convolutional neural networks(CNN)or recurrent neural networks(RNN)to predict the meteorological *** an excellent feature extractor,CNN has achieved good performance in many *** the same way,as an efficient network for orderly data processing,RNN has also achieved good ***,few or none of the above methods can meet the current accuracy requirements on ***,there is no way to pay attention to the trend monitoring of air quality *** the sake of accurate results,this paper proposes a novel predicted-trend-based loss function(PTB),which is used to replace the loss function in *** the same time,the trend of change and the predicted value are constrained to obtain more accurate prediction results of PM_(2.5).In addition,this paper extends the model scenario to the prediction of the whole existing training data *** the data on the next day of the model is mixed labels,which effectively realizes the prediction of all *** experiments show that the loss function proposed in this paper is effective.

关键词： Air quality forecasting Internet of Things recurrent neural network predicted trend loss function

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Deep Model Compression for Mobile Platforms:A Survey

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Tsinghua science and technology 2019年第6期24卷 677-693页

作者： Kaiming Nan Sicong Liu Junzhao Du Hui Liu School of Computer Science and Technology Xidian UniversityXi'an 710071China School of Software and Institute of Software Engineering Xidian UniversityXi'an 710071China

Despite the rapid development of mobile and embedded hardware, directly executing computationexpensive and storage-intensive deep learning algorithms on these devices’ local side remains constrained for sensory data analysis. In this paper, we first summarize the layer compression techniques for the state-of-theart deep learning model from three categories: weight factorization and pruning, convolution decomposition, and special layer architecture designing. For each category of layer compression techniques, we quantify their storage and computation tunable by layer compression techniques and discuss their practical challenges and possible improvements. Then, we implement Android projects using TensorFlow Mobile to test these 10 compression methods and compare their practical performances in terms of accuracy, parameter size, intermediate feature size,computation, processing latency, and energy consumption. To further discuss their advantages and bottlenecks,we test their performance over four standard recognition tasks on six resource-constrained Android ***, we survey two types of run-time Neural Network(NN) compression techniques which are orthogonal with the layer compression techniques, run-time resource management and cost optimization with special NN architecture,which are orthogonal with the layer compression techniques.

关键词： deep learning model compression run-time resource management cost optimization

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Modelling Safety of Connected and Autonomous Vehicles (CAVs) under Cyber-Attacks on Perception and Safety Monitors

Modelling Safety of Connected and Autonomous Vehicles (CAVs)...

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IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Aria Kosari Peter Popov Rajkumar Roy Department of Computer Science City University of London United Kingdom Centre for Software Reliability City University of London United Kingdom School of Science & Technology City University of London United Kingdom

ISBN: (纸本)9798350333053

CAVs have recently attracted interest both for researchers and automotive industry. Among the various issues with the design of Autonomous vehicles (AV) including CAV, safety and security assurance as well as dealing effectively with the trade-off among these have been recognized as very important. The debate regarding what level of safety and security of (C)AV is socially acceptable is very active at the moment [1], [2]. In this paper, we present a probabilistic modelling approach to dealing with the problem of safety assessment of CAV under cyber-attacks, and demonstrate its plausibility and usefulness in ranking various modes of vulnerability of the essential components of (C) AV such as the AV perception system and safety monitors.

关键词： Connected vehicles Probabilistic logic Safety Security Monitoring Cyberattack Autonomous vehicles

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

引用

arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration

arXiv

引用

arXiv 2022年

作者： Zhou, Ziqi Qi, Lei Shi, Yinghuan State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China School of Computer Science and Engineering Southeast University China

For medical image analysis, segmentation models trained on one or several domains lack generalization ability to unseen domains due to discrepancies between different data acquisition policies. We argue that the degeneration in segmentation performance is mainly attributed to overfitting to source domains and domain shift. To this end, we present a novel generalizable medical image segmentation method. To be specific, we design our approach as a multi-task paradigm by combining the segmentation model with a self-supervision domain-specific image restoration (DSIR) module for model regularization. We also design a random amplitude mixup (RAM) module, which incorporates low-level frequency information of different domain images to synthesize new images. To guide our model be resistant to domain shift, we introduce a semantic consistency loss. We demonstrate the performance of our method on two public generalizable segmentation benchmarks in medical images, which validates our method could achieve the state-of-the-art performance. ‡ Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Research on teaching methods and tools of software testing 15

Research on teaching methods and tools of software testing

引用

15th International Conference on computer science and Education, ICCSE 2020

作者： Chen, Honghong Wang, Xu Pan, Liangguang Xihua University School of Computer and Software Engineering Chengdu China University of Electronic Science and Technology of China ChengDu College Chengdu China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9781728172675

With the rapid development of software industry, software testing, as an important means of software quality assurance, has been paid more and more attention by software enterprises. It has become an urgent need to train professional software testing talents, so software testing teaching is of great significance. This paper analyzes the current situation and existing problems of software testing curriculum teaching, discusses and improves the teaching methods of software testing courses, and develops a simple suspicious codes recognition tool based on abstract grammar tree and state machine for the difficulties of teaching content which based on defected modes testing , so as to help improve the quality of classroom teaching and better train professional software testing talents suitable for market demand. © 2020 IEEE.

关键词： software testing

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Learning a Multi-scale Deep Residual Network of Dilated-Convolution for Image Denoising 5

Learning a Multi-scale Deep Residual Network of Dilated-Conv...

引用

5th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020

作者： Chen, Gongping Gao, Zhisheng Zhu, Peijian Chen, Zhenji Xihua University School of Computer and Software Engineering Chengdu China Shihezi University School of Information Science and Technology Shihezi China

ISBN: (纸本)9781728160245

Due to the favorable denoising performance of the discriminative learning model, a large number of discriminative learning models have been designed to remove noise. However, most discriminative learning models can only deal with the noise that already exists in the training data. In this paper, a multi-scale trainable deep residual convolutional neural network (DCMSNet) based on dilated convolution is proposed. DCMSNet consists of a chain of dilated convolution layers, convolution layers, normalized multi-scale convolution blocks (BNMCBlock), multi-scale convolution blocks (MCBlock) and multi-scale dropout convolution blocks (MCDBlock). The use of dilated convolution can avoid over-parameterization problems caused by too deep networks. In order to capture more feature information during feature extraction and image reconstruction, we designed novel BNMCBlock and MCBlock. In order to reduce the degree of coupling between image features and improve the generalization ability of the network, we also designed a MCDBlock. Meanwhile, residual learning, batch normalization and dropout are utilized to speed up the training process and boost the denoising performance. Unlike existing denoising models, DCMSNet is able to remove different degrees of noise. Compared with state-of-the-art image denoising methods, DCMSNet has achieved relatively competitive results. © 2020 IEEE.

关键词： Convolution

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Federated Deep Recommendation System Based on Multi-View Feature Embedding

Federated Deep Recommendation System Based on Multi-View Fea...

引用

International Conference on Data science and Advanced Analytics (DSAA)

作者： Xinna Wang Shunmei Meng Yanran Chen Qiyan Liu Rui Yuan Qianmu Li Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Business School University of Auckland Auckland New Zealand Computer Engineering University of Toronto Toronto Canada

ISBN: (纸本)9781665473316

The application of recommendation systems online services is becoming more and more extensive. However, most existing recommendation algorithms centralize multi-party information into a central processor, which may lead to the risk of privacy leakage. And many enterprises or institutions still have the problem that data cannot be shared. Federated learning has been introduced into recommendation algorithms for privacy- aware distributed learning. A typical federated learning is that each client uses local data to train a shared model, the server uses their gradient information to form a global model, and then each client updates. In this paper, we propose a federated deep recommendation algorithm called FedHe-mlp that applies a federated deep learning for data privacy protection, and combines heterogeneous information network (HIN) and matrix factorization technique for better prediction performance. First, each client obtains heterogeneous information through meta- paths, then we combine matrix factorization and heterogeneous information to mine the latent features and heterogeneous features of each client. Finally, We propose a deep neural network that considers features from multiple views. Extensive experiments on three public datasets demonstrate that FedHe- mlp can provide excellent convergence speed, recommendation accuracy, and communication efficiency while preserving data privacy.

关键词： Deep learning Training Data privacy Privacy Analytical models Federated learning Neural networks

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Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning

arXiv

引用

arXiv 2023年

作者： Wen, Xin-Cheng Chen, Yupan Gao, Cuiyun Zhang, Hongyu Zhang, Jie M. Liao, Qing School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Big Data and Software Engineering Chongqing University China Department of Informatics King’s College London United Kingdom Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Peng Cheng Laboratory China

Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in learning the graph representations of source code and are commonly adopted by existing DL-based vulnerability detection methods. However, the existing methods are still limited by the fact that GNNs are essentially difficult to handle the connections between long-distance nodes in a code structure graph. Besides, they do not well exploit the multiple types of edges in a code structure graph (such as edges representing data flow and control flow). Consequently, despite achieving state-of-the-art performance, the existing GNN-based methods tend to fail to capture global information (i.e., long-range dependencies among nodes) of code graphs. To mitigate these issues, in this paper, we propose a novel vulnerability detection framework with grAph siMplification and enhanced graph rePresentation LEarning, named AMPLE. AMPLE mainly contains two parts: 1) graph simplification, which aims at reducing the distances between nodes by shrinking the node sizes of code structure graphs;2) enhanced graph representation learning, which involves one edge-aware graph convolutional network module for fusing heterogeneous edge information into node representations and one kernel-scaled representation module for well capturing the relations between distant graph nodes. Experiments on three public benchmark datasets show that AMPLE outperforms the state-of-the-art methods by 0.39%-35.32% and 7.64%-199.81% with respect to the accuracy and F1 score metrics, respectively. The results demonstrate the effectiveness of AMPLE in learning global information of code graphs for vulnerability detection. © 2023, CC BY.

关键词： Deep learning

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New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning

arXiv

引用

arXiv 2023年

作者： Liang, Guoqiang Chen, Zhaojie Chen, Zhaoqiang Ji, Shiyu Zhang, Yanning Northwestern Polytechnical University 710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China Key Laboratory of Big Data Storage and Management China School of Computer Science China School of Software Engineering China

—To imitate the ability of keeping learning of human, continual learning which can learn from a never-ending data stream has attracted more interests recently. In all settings, the online class incremental learning (OCIL), where incoming samples from data stream can be used only once, is more challenging and can be encountered more frequently in real world. Actually, all continual learning models face a stability-plasticity dilemma, where the stability means the ability to preserve old knowledge while the plasticity denotes the ability to incorporate new knowledge. Although replay-based methods have shown exceptional promise, most of them concentrate on the strategy for updating and retrieving memory to keep stability at the expense of plasticity. To strike a preferable trade-off between stability and plasticity, we propose an Adaptive Focus Shifting algorithm (AFS), which dynamically adjusts focus to ambiguous samples and non-target logits in model learning. Through a deep analysis of the task-recency bias caused by class imbalance, we propose a revised focal loss to mainly keep stability. By utilizing a new weight function, the revised focal loss will pay more attention to current ambiguous samples, which are the potentially valuable samples to make model progress quickly. To promote plasticity, we introduce a virtual knowledge distillation. By designing a virtual teacher, it assigns more attention to non-target classes, which can surmount overconfidence and encourage model to focus on inter-class information. Extensive experiments on three popular datasets for OCIL have shown the effectiveness of AFS. The code will be available at https://***/czjghost/AFS. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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