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SSRN 2024年

作者： Huang, Da-Wen Wu, Wenjie Bi, Jichao Li, Junli Gan, Chenquan Zhou, Wei College of Computer Science Sichuan Normal University Chengdu China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating the damage caused by them. However, most studies are not suitable for identifying rumor sources in dynamic OSNs with community structures. Moreover, these studies tend to neglect the impact of timeliness on the spread of rumors, potentially leading to an overestimation of rumors' propagation ability. To overcome these limitations, this paper proposes a multiple rumor sources estimation framework for OSNs. First, a community-based dynamic network model is introduced to depict the temporal nature and community feature of OSNs. Second, a meticulously designed microcosmic SIR model, incorporating the timeliness of rumor topics, is developed to unravel the complex dynamics of rumor propagation. Then, a computationally efficient multiple rumor sources estimation algorithm is proposed. This algorithm utilizes the infection information collected by sensors and applies the maximum likelihood estimation (MLE) method to identify the rumor sources. Finally, experimental results on real-world and synthetic temporal networks demonstrate the effectiveness of the proposed rumor source estimation algorithm. © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices

arXiv

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arXiv 2022年

作者： Ngo, Dat T. Nguyen, Thao T.B. Nguyen, Hieu T. Nguyen, Dung B. Nguyen, Ha Q. Pham, Hieu H. Vingroup Big Data Institute Hanoi10000 Viet Nam Vingroup Big Data Institute VinBigData JSC Hanoi10000 Viet Nam VinUni-Illinois Smart Health Center College of Engineering & Computer Science Hanoi10000 Viet Nam

The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical image analysis and diagnosis. In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment. However, training and inferencing deep neural networks such as D-CNN on high-resolution 3D volumes of Computed Tomography (CT) scans for diagnostic tasks poses formidable computational challenges. This challenge raises the need of developing deep learning-based approaches that are robust in learning representations in 2D images, instead 3D scans. In this work, we propose for the first time a new strategy to train slice-level classifiers on CT scans based on the descriptors of the adjacent slices along the axis. In particular, each of which is extracted through a convolutional neural network (CNN). This method is applicable to CT datasets with per-slice labels such as the RSNA Intracranial Hemorrhage (ICH) dataset, which aims to predict the presence of ICH and classify it into 5 different subtypes. We obtain a single model in the top 4% best-performing solutions of the RSNA ICH challenge, where model ensembles are allowed. Experiments also show that the proposed method significantly outperforms the baseline model on CQ500. The proposed method is general and can be applied for other 3D medical diagnosis tasks such as MRI imaging. To encourage new advances in the field, we will make our codes and pre-trained model available upon acceptance of the paper. © 2022, CC BY.

关键词： Deep neural networks

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A Simple Dashboard for OpenStack Horizon 4

A Simple Dashboard for OpenStack Horizon

引用

4th International Conference on Computing and big data, ICCBD 2021

作者： Wang, Hongbin Li, Shaoxu Deng, Dan Li, Leixiao Gao, Jing Li, Jie College of Data Science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Reg. Software Serv. Eng. Technology Research Center Based on Big Data Hohhot China College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China

ISBN: (纸本)9781450387194

OpenStack is an open source cloud computing management project, which allows flexible resources applying. The traditional OpenStack dashboard is inflexible, thus a simple dashboard is designed. The simple dashboard integrates keystone, horizon, grace, nova, neutron, swift and other components, communicates with the traditional OpenStack dashboard and runs in the form of docker container. We implemented simple dashboard on a small cloud platform. Compared with the traditional dashboard, the interface of simple dashboard is simpler, more flexible and easy to use. © 2021 ACM.

关键词： Platform as a Service (PaaS)

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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A Survey of Stance Detection on Social Media: New Directions and Perspectives

arXiv

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arXiv 2024年

作者： Zhang, Bowen Dai, Genan Niu, Fuqiang Yin, Nan Fan, Xiaomao Wang, Senzhang Cao, Xiaochun Huang, Hu College of Big Data and Internet Shenzhen Technology University Shenzhen518000 China College of Computer Science and Software Shenzhen University Shenzhen518000 China Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Science and Engineering Central South University Changsha410083 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Peking University Shenzhen Graduate School Shenzhen518000 China

In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these opinions offers valuable insights for decision-making in various sectors, including marketing and politics. As a result, stance detection has emerged as a crucial subfield within affective computing, enabling the automatic detection of user stances in social media conversations and providing a nuanced understanding of public sentiment on complex issues. Recent years have seen a surge of research interest in developing effective stance detection methods, with contributions from multiple communities, including natural language processing, web science, and social computing. This paper provides a comprehensive survey of stance detection techniques on social media, covering task definitions, datasets, approaches, and future works. We review traditional stance detection models, as well as state-of-the-art methods based on large language models, and discuss their strengths and limitations. Our survey highlights the importance of stance detection in understanding public opinion and sentiment, and identifies gaps in current research. We conclude by outlining potential future directions for stance detection on social media, including the need for more robust and generalizable models, and the importance of addressing emerging challenges such as multi-modal stance detection and stance detection in low-resource languages. Copyright © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinquan Yang Jinheng Xie Xuechen Li Xuguang Li Linlin Shen Yongqiang Deng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Stomatology Shenzhen University General Hospital Shenzhen China

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods.

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False data Injection Attack Detection of Cyber-Physical Charging Systems Based on Time-Frequency Analysis

False Data Injection Attack Detection of Cyber-Physical Char...

引用

Smart Electrical Grid and Renewable Energy (SEGRE), International Conference on

作者： Chao Hu Peishun Fan Yuxin Li I-Ju Chiu Yibin Wang Yiquan Zhou Yi Li Heng Li Big Data Institute Central South University Changsha China School of Computer Science and Engineering Central South University Changsha China Department of Information Studies University College London London UK Hunan Post and Telecommunication College Changsha China

Distributed charging systems play a critical role in the operation of supercapacitor electric vehicles. During the charging process, multiple charging modules rely on a communication network to achieve information sharing and interaction. If an external attack occurs on the network, the system will be in danger. To solve this problem, this paper proposes an anomaly detection mechanism based on real-time feature extraction to process measurement data. By analyzing the delayed effect of communication signal propagation, a method for identifying component faults and false data injection attacks (FDIA) is proposed. Moreover, the effectiveness and feasibility of the proposed distributed detection method are verified through experiments. Multiple experiments have consistently demonstrated that the proposed method outperforms existing methods FDIA detection.

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MULTIMODAL TRANSFORMER NETWORKS WITH LATENT INTERACTION FOR AUDIO-VISUAL EVENT LOCALIZATION

MULTIMODAL TRANSFORMER NETWORKS WITH LATENT INTERACTION FOR ...

引用

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： He, Yixuan Xu, Xing Liu, Xin Ou, Weihua Lu, Huimin Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China China Glasgow College University of Electronic Science and Technology of China China Department of Computer Science Huaqiao University China School of Big Data and Computer Science Guizhou Normal University China Department of Mechanical and Control Engineering Kyushu Institute of Technology Japan

ISBN: (纸本)9781665438643

The task of audio-visual event localization (AVEL) aims to localize a visible and audible event in a video. Previous methods first divide a video into segments and then fuse visual and acoustic features at the segment level via a co-attention mechanism. However, existing methods mostly model relations between individual visual and audio segments in a limitedly short period, which may not cover a longer video duration for better high-level event information modeling. In this paper, we proposed a novel model termed Multimodal Transformer Network with Latent Interaction (MTNLI) to tackle this problem. The proposed MTNLI model employs a multimodal Transformer structure to learn the cross-modality relationships between latent visual and audio summarizations in long segment sequences, which summarize the visual and audio segments into a small number of latent representations to avoid modeling uninformative individual visual-audio relations. The cross-modality information between the latent summarizations is propagated to fuse valuable information from both modalities, which can effectively handle large temporal inconsistent between vision and audio. Our MTNLI method achieves state-of-the-art performance on the benchmark AVE (Audio-Visual Event) dataset for the event localization task. © 2021 IEEE

关键词： Benchmarking

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Heterogeneous software Effort Estimation via Cascaded Adversarial Auto-Encoder 1

引用

21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Qi, Fumin Jing, Xiao-Yuan Zhu, Xiaoke Jia, Xiaodong Cheng, Li Dong, Yichuan Fang, Ziseng Ma, Fei Feng, Shengzhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Wuhan University Wuhan China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China National Supercomputing Center in Shenzhen Guangzhou Guangdong China School of Computer Science Qufu Normal University Rizhao China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

In software Effort Estimation (SEE) practice, the data drought problem has been plaguing researchers and practitioners. Leveraging heterogeneous SEE data collected by other companies is a feasible solution to relieve the data drought problem. However, how to make full use of the heterogeneous effort data to conduct SEE, which is called as Heterogeneous software Effort Estimation (HSEE), has not been well studied. In this paper, we propose a HSEE model, called Dynamic Heterogeneous software Effort Estimation (i.e., DHSEE), which leverages the adversarial auto-encoder and convolutional neural network techniques. Meanwhile, we have investigated the scenario of conducting HSEE with dynamically increasing effort data. Experiments on ten public datasets indicate that our approach can significantly outperform the state-of-the-art HSEE method and other competing methods on both static and dynamic SEE scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Drought

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Quantized Analog Beamforming Enabled Multi-task Federated Learning Over-the-air

arXiv

引用

arXiv 2025年

作者： Yao, Jiacheng Xu, Wei Zhu, Guangxu Yang, Zhaohui Huang, Kaibin Niyato, Dusit National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China Shenzhen Research Institute of Big Data Shenzhen China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

Over-the-air computation (AirComp) has recently emerged as a pivotal technique for communication-efficient federated learning (FL) in resource-constrained wireless networks. Though AirComp leverages the superposition property of multiple access channels for computation, it inherently limits its ability to manage inter-task interference in multi-task computing. In this paper, we propose a quantized analog beamforming scheme at the receiver to enable simultaneous multi-task FL. Specifically, inspiring by the favorable propagation and channel hardening properties of large-scale antenna arrays, a targeted analog beamforming method in closed form is proposed for statistical interference elimination. Analytical results reveal that the interference power vanishes by an order of O (1/Nr) with the number of analog phase shifters, Nr, irrespective of their quantization precision. Numerical results demonstrate the effectiveness of the proposed analog beamforming method and show that the performance upper bound of ideal learning without errors can be achieved by increasing the number of low-precision analog phase shifters. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi-task learning

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