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SSRN 2023年

作者： Chen, Fang Zhao, Weiling Zhou, Xiaobo School of Electronics and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Center for Computational Systems Medicine

The categories of diabetic retinopathy (DR) are interrelated, and different ophthalmologists often give different results for the same fundus image. Automatic cross image retrieval of DR can provide an effective diagnostic solution for ophthalmologists and is of great significance in clinical practice. Cross-image (i.e. left and right fundus images for a patient) information is highly correlated and complementary and can be harnessed to improve various computer vision tasks such as image classification, object detection, image segmentation, and image retrieval. Previous studies did not explore the correlation between lesion areas in left and right fundus images of patients, limiting the effective diagnosis of DR. In this study, we proposed a cross-image siamese graph convolutional network(CIS-GCN) to retrieve fine-grained diabetic retinopathy fundus images. First, we constructed a global-specific structure to obtain the specific features of the left and right eyes. Then, we passed the specific features through the pathological localization network to obtain the location features of the lesion. Finally, a graph convolutional neural network was introduced to construct node sets for the left and right eyes, respectively, to represent relatively consistent regions in the fundus images of patients and learn their correlations. We tested our method using Diabetic Retinopathy Detection datasets and the results showed that our algorithm outperforms other state-of-the-art methods by 2.2% ~ 3.7% in image data retrieval. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Day-ahead Scheduling of Natural Gas system Considering Hierarchical Station-Network Scheduling Characteristics

Day-ahead Scheduling of Natural Gas System Considering Hiera...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Ruimeng Liu Rong Dai Yu Liang Xia Zhao State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing China Marketing Service Center of Fujian Electric Power Co. Ltd Fujian China

Considering the hierarchical scheduling characteristics of the compressor station and natural gas network under practical operating conditions, this paper proposes a two-stage hierarchical station-network scheduling mechanism. The proposed mechanism is based on a refined compressor station model, which regards the dispatching center of the natural gas system as the decision-maker of superior scheduling and the control center of the compressor station as the decision-maker of subordinate scheduling. The effectiveness of the proposed mechanism is demonstrated by a 7-node natural gas system.

关键词： system integration Dispatching Natural gas Optimization

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Study on the influence of international oil price on domestic oil price by APT-ECM 4

Study on the influence of international oil price on domesti...

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4th International Conference on Physics, Mathematics and Statistics, ICPMS 2021

作者： Liu, Ziyuan Wang, Yu Xu, Shiwei Wang, Shengwei Li, Denghua Zhou, Han Agricultural Information Institute of Caas Beijing100081 China Beijing Agricultural Monitoring and Early Warning Engineering Technology Research Center Beijing100081 China Key Open Laboratory of Intelligent Agricultural Early Warning Technology and System of Caas Beijing100081 China Key Lab. of Agric. Information Service Technology of the Ministry of Agricultural and Rural Affairs Beijing100081 China

Based on the monthly data of soybean, soybean oil and soybean meal prices in China and the United States from 2006 to 2020, this paper applied the asymmetric error correction model (APT-ECM) to analyze the influence of international oil price on domestic oil price before and after Sino-US trade friction. The results show that: 1) the influence of international domestic soybean price on domestic soybean price is significant before and after Sino-US trade friction, while the influence of domestic price on international price is insignificant;2) the impact of international soybean oil price on domestic soybean price is stable in the early stage but fluctuates evidently in the latter stage;3) domestic soybean oil price is exposed to insignificant impact of international soybean oil price. © Published under licence by IOP Publishing Ltd.

关键词： Friction

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Chinese named entity recognition method for the field of network security based on RoBERTa

Chinese named entity recognition method for the field of net...

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2021 International Conference on Networking and Network Applications, NaNA 2021

作者： Zhu, Xiaoyan Zhang, Yu Zhu, Lei Hei, Xinhong Wang, Yichuan Hu, Feixiong Yao, Yanni Xi'an Jiaotong University School of Computer Science and Engineering Xi'an710049 China School of Computer Science and Technology Xi'an University of technology Shaanxi Xi'an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Shaanxi Xi'an710048 China Shenzhen Tencent Computer System Co. Ltd Shenzhen518054 China

ISBN: (纸本)9781665441582

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively. © 2021 IEEE.

关键词： Network security

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Semantic-Aware Prompt Learning for Multimodal Sarcasm Detection

Semantic-Aware Prompt Learning for Multimodal Sarcasm Detect...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guangjin Wang Bao Wang Fuyong Xu Zhenfang Zhu Peipei Wang Ru Wang Peiyu Liu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model’s "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model’s ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method.

关键词： Semantics Benchmark testing Speech enhancement Signal processing Vectors Acoustics Context modeling

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Learning Robust Travel Preferences Via Check-In Masking for Next Poi Recommendation

SSRN

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SSRN 2023年

作者： Duan, Chenghua Zhou, Wei Zeng, Jun Zhang, Yihao Wen, Junhao School of Bigdata and Software Engineering Chongqing University Daxuecheng South Rd. 55 Chognqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Moe Chognqing400044 China School of Artificial Intelligence Chongqing University of Technology Chognqing401135 China

Next point-of-interest (POI) recommendation is a crucial part of many location-based service applications. It predicts users’ next visit based on their historical trajectories and current *** solutions primarily focus on sequential modeling of users’ check-in behavior. Unfortunately, most current research overlooks the fact that check-ins are implicit feedback, and inherent noise exists within them. To this end, we propose an approach to cope with noise-learning robust user travel preferences. Specifically, we design a masking strategy for check-ins, which masks the spatio-temporal information of some check-ins in users’ trajectories and forces our model to learn visit preferences without relying on individual inputs, thereby mitigating the bias caused by noisy check-ins. Then, we utilize multi-level geographical information to recommend the next POI more accurately. Additionally, we propose another masking strategy that considers users’ living area and automatically selects the proper strategy based on trajectory features. To the best of our knowledge, this is the first work that investigates the characteristics of noisy check-ins and performs denoising in next POI recommendation. Through extensive experiments, we demonstrate that our model outperforms state-of-the-art approaches and effectively handles data sparsity and noise issues. © 2023, The Authors. All rights reserved.

关键词： Trajectories

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DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation

arXiv

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arXiv 2022年

作者： Huang, Mengqi Mao, Zhendong Wang, Penghui Wang, Quan Zhang, Yongdong University of Science and Technology of China China MOE Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

Text-to-image generation aims at generating realistic images which are semantically consistent with the given text. Previous works mainly adopt the multi-stage architecture by stacking generator-discriminator pairs to engage multiple adversarial training, where the text semantics used to provide generation guidance remain static across all stages. This work argues that text features at each stage should be adaptively re-composed conditioned on the status of the historical stage (i.e., historical stage's text and image features) to provide diversified and accurate semantic guidance during the coarse-to-fine generation process. We thereby propose a novel Dynamical Semantic Evolution GAN (DSE-GAN) to re-compose each stage's text features under a novel single adversarial multi-stage architecture. Specifically, we design (1) Dynamic Semantic Evolution (DSE) module, which first aggregates historical image features to summarize the generative feedback, and then dynamically selects words required to be re-composed at each stage as well as re-composed them by dynamically enhancing or suppressing different granularity subspace's semantics. (2) Single Adversarial Multistage Architecture (SAMA), which extends the previous structure by eliminating complicated multiple adversarial training requirements and therefore allows more stages of text-image interactions, and finally facilitates the DSE module. We conduct comprehensive experiments and show that DSE-GAN achieves 7.48% and 37.8% relative FID improvement on two widely used benchmarks, i.e., CUB-200 and MSCOCO, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A CGAN-LSTM-Based Framework for Time-Varying Non-Stationary Channel Modeling

arXiv

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arXiv 2025年

作者： Guo, keying He, Ruisi Yang, Mi Zhang, Yuxin Ai, Bo Zhang, Haoxiang Han, Jiahui Chen, Ruifeng School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China State Key Laboratory of Advanced Rail Autonomous Operation Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China China Academy of Industrial Internet Ministry of Industry and Information Technology Beijing China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing100081 China

Time-varying non-stationary channels, with complex dynamic variations and temporal evolution characteristics, have significant challenges in channel modeling and communication system performance evaluation. Most existing methods of time-varying channel modeling focus on predicting channel state at a given moment or simulating short-term channel fluctuations, which are unable to capture the long-term evolution of the channel. This paper emphasizes the generation of long-term dynamic channel to fully capture evolution of non-stationary channel properties. The generated channel not only reflects temporal dynamics but also ensures consistent stationarity. We propose a hybrid deep learning framework that combines conditional generative adversarial networks (CGAN) with long short-term memory (LSTM) networks. A stationarity-constrained approach is designed to ensure temporal correlation of the generated time-series channel. This method can generate channel with required temporal non-stationarity. The model is validated by comparing channel statistical features, and the results show that the generated channel is in good agreement with raw channel and provides good performance in terms of non-stationarity. Copyright © 2025, The Authors. All rights reserved.

关键词： Long Term Evolution (LTE)

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A Cloudedge-Combined Data Placement Strategy Based on User Access Regions 6

A Cloudedge-Combined Data Placement Strategy Based on User A...

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6th International Conference on Big Data and Information Analytics, BigDIA 2020

作者： Cao, Eryang Wang, Pengwei Yan, Chungang Jiang, Changjun Department of Computer Science and Technology Tongji University Shanghai200092 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China School of Computer Science and Technology Donghua University Shanghai201620 China

To improve the availability of data in the cloud and avoid vendor lock-in risk, multi-cloud storage is attracting more and more attentions. However, accessing data from the cloud usually has some disadvantages such as higher latency and more bandwidth-consuming. Edge computing, which has become popular in recent years, could effectively solve the above problems while it usually reduces availability. Storage only in the cloud or edge environment is limited by many factors. To this end, we build mathematical model which is abstracted from real access mode of users in consideration of multi-cloud and edge. This paper presents a method combined NSGAII with multi-group method which has better ability of global search to help users determine cloud and edge services to store and access data object. A large number of experiments using real service provider information demonstrate that the proposed algorithm outperforms existing methods and effectiveness of it. © 2020 IEEE.

关键词： Multiobjective optimization

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A Novel Predictive Approach to Content Popularity-Aware Edge Caching in VEC

A Novel Predictive Approach to Content Popularity-Aware Edge...

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Software services Engineering (SSE) IEEE International Conference on

作者： YiYuan Zuo Yunni Xia Ruilong Yang Xu Wang Xingli Zhong Qing Xia Xiaoning Sun Jiafeng Feng College of Computer Science Chongqing University Chongqing China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China CISDI Group Co. Ltd Chongqing China Chongqing Key Laboratory of Reliability Technology of Intelligent Electronic appliances Chongqing China School of Computer and Information Science Yunnan Key Laboratory of Service Computing Chongqing Normal University Chongqing China Yunnan University of Finance and Economics Kunming China China Huadian Corporation Guangdong branch Guangdong China

ISBN: (数字)9798350368512

ISBN: (纸本)9798350368529

Mobile edge computing is an emerging computing paradigm boosting resource-demanding and delay-sensitive applications through deploying computing infrastructures at the edge of the Internet nearby mobile requesters and users. In an Internet of Vehicles (IoV) environment, Vehicular Edge computing (VEC) is capable of exploiting network edge devices, in terms of, e.g., Roadside Units (RSUs), for predictive content caching for optimizing quality-of-experience (QoE) of nearby content requesters based on content popularity analysis, it remains a great challenge to accurately predict content popularity of mobile requesters and appropriately cache required content with low miss rate accordingly in a VEC environment with high user mobility and dynamics. To address this challenge mentioned above, in this paper, we propose predictive content popularity-aware approach, i.e., KM_SVD++, to edge caching in an VEC environment. The proposed approach is capable of achieving high hit rate of mobile content requestors in VEC and low latency of content delivery by leveraging a Kalman filtering model for predicting locations of vehicles and a SVD++ one for yielding decisions for cache deployment and replacement. We conduct extensive simulations as well to prove its effectiveness.

关键词： Multi-access edge computing Software algorithms Predictive models Prediction algorithms Software Kalman filters Quality of experience

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