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2024 International Conference on Electronics, Electrical and Information engineering, ICEEIE 2024

作者： Karim, Shahid Qadir, Akeel Han, Gao Li, Jing Ibrar, Muhammad Bibi, Irfana School of Information Engineering Xi'an Eurasia University 710065 China Software College Shenyang Normal University Shenyang China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper introduces a new method for traffic anomaly identification within the Faster R-CNN framework by combining the DenseNet module with the region proposals (RPs) processing block. The suggested approach seeks to improve feature extraction and RP creation in aerial imagery or CCTV frames to get beyond the drawbacks of the current methods. Discriminative features are extracted through DenseNet, and the RPs block makes it easier to provide a wide range of accurate and varied area proposals. Contrast correction techniques are applied to overcome the difficulties presented by low-resolution and low-contrast images frequently seen in surveillance scenarios. Comparing experimental results to state-of-the-art techniques, one can observe notable resilience and detection accuracy increases. The suggested method works exceptionally well in various traffic scenarios and environmental circumstances, indicating its potential for practical implementation in traffic surveillance systems. Overall, the study contributes to advancing the efficacy and reliability of anomaly detection in urban traffic environments. © 2024 SPIE.

关键词： Aerial photography

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QUIC meets ICN: A Versatile Wireless Transport Strategy in Multi-access Edge Environments

QUIC meets ICN: A Versatile Wireless Transport Strategy in M...

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International Workshop on Quality of Service

作者： Yaodong Huang Changkang Mo Tianhang Liu Biying Kong Lei Zhang Yukun Yuan Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science and Engineering University of Tennessee at Chattanooga Chattanooga TN USA

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Information-centric Networking in edge computing environments exhibits the potential to significantly enhance the efficiency, reliability, and security of data transmission, making it a promising technology for future network deployments. However, the differences from traditional networks require applications to actively redevelop and redeploy onto edge devices, incurring additional costs for the proliferation of ICN applications. In this paper, we propose a system to adapt QUIC protocol over ICN networks in multi-access edge networks. The system aims to expand the application repertoire for ICN by providing a smooth transition of applications using QUIC to run on ICN networks. We design an ICN-QUIC conversion layer to manage the transmission of data from QUIC-based applications. We implement and evaluate the designed system. The experiment results show that, compared to existing networks, our system can enhance the transmission efficiency, i.e., up to 20 times better goodput in multicast situations, and achieves comparable results in unicast scenarios. We test the scalability of our system in real edge and wireless environments. We also deploy the real applications over the proposed system to demonstrate its compatibility in ICN and MEC environments.

关键词： Wireless communication Protocols Unicast Scalability Quality of service Data communication Communication system security

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of computer science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models

arXiv

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arXiv 2024年

作者： Lin, Xuan Chen, Long Wang, Yile Zeng, Xiangxiang Yu, Philip S. School of Computer Science Xiangtan University China College of Computer Science and Software Engineering Shenzhen University China College of Information Science and Engineering Hunan University China Department of Computer Science University of Illinois United States

Large language models (LLMs) are widely applied in various natural language processing tasks such as question answering and machine translation. However, due to the lack of labeled data and the difficulty of manual annotation for biochemical properties, the performance for molecule generation tasks is still limited, especially for tasks involving multi-properties constraints. In this work, we present a two-step framework PEIT (Property Enhanced Instruction Tuning) to improve LLMs for molecular-related tasks. In the first step, we use textual descriptions, SMILES, and biochemical properties as multimodal inputs to pre-train a model called PEIT-GEN, by aligning multimodal representations to synthesize instruction data. In the second step, we fine-tune existing open-source LLMs with the synthesized data, the resulting PEIT-LLM can handle molecule captioning, text-based molecule generation, molecular property prediction, and our newly proposed multi-constraint molecule generation tasks. Experimental results show that our pre-trained PEIT-GEN outperforms MolT5, BioT5, MolCA and Text+Chem-T5 in molecule captioning, demonstrating modalities align well between textual descriptions, structures, and biochemical properties. Furthermore, PEIT-LLM shows promising improvements in multi-task molecule generation, demonstrating the effectiveness of the PEIT framework for various molecular tasks. We release the code, constructed instruction data, and model checkpoints in https://***/chenlong164/PEIT. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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Mobile-based Invasive Species Identifier for Biodiversity Conservation 17

Mobile-based Invasive Species Identifier for Biodiversity Co...

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17th IEEE International Conference on Application of Information and Communication Technologies, AICT 2023

作者： Hussien, Ilham Regasa, Tsion Debele, Deborah El Moutaouakil, Amine United Arab Emirates University Department of Computer Science and Software Engineering College of Information Technology Al Ain United Arab Emirates United Arab Emirates University Department of Electrical and Communication Engineering College of Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350303568

Alien species have the potential to significantly threaten and harm the living beings and the environment. The search for cheap and easy-to-deploy solutions has been among the priorities of environmentalists and researchers. In this work, we developed a mobile application that enables users to differentiate registered plants in the gene database in the UAE from alien species, and offers a handy method for alerting authorities through a series of easy steps. The mobile application will be useful for agencies or government personnel who oversee the conservation of the environment in order to keep track of local and alien species. The mobile application will be also useful for people to use it in their farms and gardens. © 2023 IEEE.

关键词： Biodiversity

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SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification 24

SAM-MIL: A Spatial Contextual Aware Multiple Instance Learni...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Fang, Heng Huang, Sheng Tang, Wenhao Huangfu, Luwen Liu, Bo School of Big Data & Software Engineering Chongqing University Chongqing China Fowler College of Business San Diego State University San DiegoCA United States School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Multiple Instance Learning (MIL) represents the predominant framework in Whole Slide Image (WSI) classification, covering aspects such as sub-typing, diagnosis, and beyond. Current MIL models predominantly rely on instance-level features derived from pretrained models such as ResNet. These models segment each WSI into independent patches and extract features from these local patches, leading to a significant loss of global spatial context and restricting the model's focus to merely local features. To address this issue, we propose a novel MIL framework, named SAM-MIL, that emphasizes spatial contextual awareness and explicitly incorporates spatial context by extracting comprehensive, image-level information. The Segment Anything Model (SAM) represents a pioneering visual segmentation foundational model that can capture segmentation features without the need for additional fine-tuning, rendering it an outstanding tool for extracting spatial context directly from raw WSIs. Our approach includes the design of group feature extraction based on spatial context and a SAM-Guided Group Masking strategy to mitigate class imbalance issues. We implement a dynamic mask ratio for different segmentation categories and supplement these with representative group features of categories. Moreover, SAM-MIL divides instances to generate additional pseudo-bags, thereby augmenting the training set, and introduces consistency of spatial context across pseudo-bags to further enhance the model's performance. Experimental results on the CAMELYON-16 and TCGA Lung Cancer datasets demonstrate that our proposed SAM-MIL model outperforms existing mainstream methods in WSIs classification. Our open-source implementation code is is available at https://***/FangHeng/SAM-MIL. © 2024 ACM.

关键词： Self-supervised learning

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Large Point-to-Gaussian Model for Image-to-3D Generation 24

Large Point-to-Gaussian Model for Image-to-3D Generation

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32nd ACM International Conference on Multimedia, MM 2024

作者： Lu, Longfei Gao, Huachen Dai, Tao Zha, Yaohua Hou, Zhi Wu, Junta Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Tencent Shenzhen Hunyuan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798400706868

Recently, image-to-3D approaches have significantly advanced the generation quality and speed of 3D assets based on large reconstruction models, particularly 3D Gaussian reconstruction models. Existing large 3D Gaussian models directly map 2D image to 3D Gaussian parameters, while regressing 2D image to 3D Gaussian representations is challenging without 3D priors. In this paper, we propose a large Point-to-Gaussian model, that inputs the initial point cloud produced from large 3D diffusion model conditional on 2D image to generate the Gaussian parameters, for image-to-3D generation. The point cloud provides initial 3D geometry prior for Gaussian generation, thus significantly facilitating image-to-3D Generation. Moreover, we present the Attention mechanism, Projection mechanism, and Point feature extractor, dubbed as APP block, for fusing the image features with point cloud features. The qualitative and quantitative experiments extensively demonstrate the effectiveness of the proposed approach on GSO and Objaverse datasets, and show the proposed method achieves state-of-the-art performance. © 2024 ACM.

关键词： 3D reconstruction

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios

A Method Based on Genetic Programming to Automatically Const...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yan Ma Changsheng Zhang Yan Gao Ying Guo School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated.

关键词： Industries Genetic programming Prediction algorithms Knowledge discovery Boosting Decision trees Stock markets Random forests Fuzzy systems

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PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing 15

PowerGen: Resources Utilization and Power Consumption Data G...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Ismail, Leila Materwala, Huned Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. © 2024 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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TreeBERT: Advanced Representation Learning for Relation Extraction

TreeBERT: Advanced Representation Learning for Relation Extr...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hebbar, Shashank Xie, Ying Zhong, Jiang Risk Modeling and Analytics Credigy Solutions Inc Norcross United States Kennesaw State University College of Computing and Software Engineering Kennesaw United States Chongqing University College of Computer Science Chongqing China

ISBN: (纸本)9798350324457

As the COVID-19 pandemic has subsided, it remains crucial to analyze the vast amount of research produced during this period to advance our understanding of effective medical treatments and therapies. The wealth of biomedical literature generated during the pandemic presents a challenge for researchers who need to quickly assimilate new information. To tackle this issue, we introduce Tree-BERT, a novel representation learning language model designed for the automatic extraction of biomedical relationships from medical publications. Unlike traditional BERT models and their variants, which solely focus on the contextual relationship between words in input sentences, Tree-BERT also incorporates sentence structure information, enhancing word embeddings. Our experiments reveal that Tree-BERT outperforms alternative methods in biomedical relationship extraction. We will also demonstrate the model's ability to identify new relationships from Covid-19 publications, showcasing its potential to contribute to the ongoing analysis of pandemic research and its broader applications in the field of bio-medicine. © 2023 IEEE.

关键词： BERT Biomedical Relation Extraction Graph Convolutional Network Representation Learning TreeBERT

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