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SSRN 2024年

作者： Zhou, Pengfei Min, Weiqing Fu, Chaoran Jin, Ying Huang, Mingyu Li, Xiangyang Mei, Shuhuan Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Intelligent Computing Technology Chinese Academy of Sciences Suzhou China Zhongke Shenjian Technology Co. Ltd. Suzhou China

Food is the cornerstone of both survival and social life. With the increasing complexity of global dietary needs and preferences, there is a growing demand for food intelligence to enable tasks like recipe recommendation and diet-disease correlation discovery. To address this, we introduce the Food-oriented Large Language Model (LLM) FoodSky, which offers fine-grained perception and reasoning of food data. We constructed a food corpus, FoodEarth, from various authoritative sources to enhance FoodSky's knowledge. We also developed the Topic-based Selective State Space Model and Hierarchical Topic Retrieval Augmented Generation algorithms to improve FoodSky's ability to capture fine-grained food semantics and generate context-aware food-relevant text. Extensive experiments show that FoodSky outperforms general-purpose LLMs on the Chinese National Chef Exam and Dietetic Exam, achieving accuracies of 67.2% and 66.4%, respectively. FoodSky not only enhances culinary creativity and promotes healthier eating patterns but also establishes a new standard for domain-specific LLMs tackling real-world food-related issues. © 2024, The Authors. All rights reserved.

关键词： Food ingredients

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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OTFS Channel Estimator based on Embedded Pilot Sequence Similarity

OTFS Channel Estimator based on Embedded Pilot Sequence Simi...

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Communication technology and information technology (ICCTIT), International Conference on

作者： Youzhi Zhao Linyu Wang Jianhong Xiang Yang Liu College of Information and Communication Engineering Harbin Engineering University Harbin China Agile and Intelligent Computing Key Laboratory China Electronic Technology Group(cetc-10) Sichuan China

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

Traditional orthogonal time-frequency space (OTFS) channel estimation schemes place guard symbols around pilots to avoid interference from data symbols, thereby reducing spectrum efficiency (SE). To address this issue, this paper proposes an OTFS channel estimator based on the similarity of embedded pilot sequences (EPSS). Firstly, according to prior channel information, multiple pilots are sparsely embedded into an OTFS data frame, preventing mutual interference between pilots while fully utilizing the channel diversity gain, and the guard symbols of the pilots at the transmitter are revoked to improve SE. Secondly, at the receiver, a shift sequence with strong correlation to the embedded pilot sequence at the transmitter is designed. Through linear search, when the autocorrelation value of the shift sequence with the pilot sequence is greater than a threshold, the delay-Doppler taps of the channel can be estimated. Finally, an inverse gain matrix is designed to eliminate interference from data symbols to the pilots, thereby obtaining the channel gain. Through the design of an OTFS communication system, simulation results verify that the EPSS scheme significantly improves SE with a slight reduction in reliable communication performance.

关键词： Time-frequency analysis Spectral efficiency Partial transmit sequences Simulation Channel estimation Symbols Interference Receivers Diversity methods Autocorrelation

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ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environmental Microorganism Image Object Detection

ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environm...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Yang, Hechen Yang, Jinzhu Jiang, Tao Zhao, Xin Zhang, Jinghua Zhao, Peng Chen, Ao Nie, Qianqing Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Chengdu University of Information Technology International Joint Institute of Robotics and Intelligent Systems Chengdu China Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network. © 2023 IEEE.

关键词： Attention Convolutional Neural Network Environmental Miroorganisms Feature Fusion Object Detection

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An Entity Alignment Algorithm Based on Ontology Enhancement and Multi-Feature Fusion

An Entity Alignment Algorithm Based on Ontology Enhancement ...

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Frontiers technology of information and Computer (ICFTIC), IEEE International Conference on

作者： Hao Song Yuxia Lei Kangli Zi Fuyuan Quan Tianhang Zhang Qi Li School of Computer Science Qufu Normal University Rizhao Shandong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Entity alignment plays a crucial role in the integration of knowledge graphs. However, current knowledge graphs often suffer from inconsistent construction standards, structural heterogeneity, and imprecise semantic representation of entities, which can lead to suboptimal embedding of structural information. Moreover, most existing entity alignment methods rely heavily on structural information, which limits alignment efficiency. To address these challenges, this paper presents Onto-IMF, an ontology-enhanced entity alignment method based on multi-feature fusion. This approach aims to enhance the semantic representation of entities by incorporating ontology information, thereby reducing structural discrepancies among heterogeneous graphs and improving the embedding of structural information. The multi-feature fusion entity alignment leverages diverse semantic features, including relations, attributes, and entity names, to achieve more accurate alignment. Experimental results on various DBP15K subsets indicate that the proposed method achieves Hits@l scores of 86.6%, 93.0%, and 97.7%, respectively, representing an average improvement of 7.9 % over the best-performing baseline models.

关键词： Accuracy Semantics Knowledge graphs Contrastive learning Ontologies Data models Standards

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Near-field Beamforming Technique Based on 3-D Cylindrical Array: Enlarging Region and Improving Spectral Efficiency

Near-field Beamforming Technique Based on 3-D Cylindrical Ar...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Shuai Gao Kai Zhou Ying Wang Jianhong Xiang Yu Zhong College of Information and Communication Engineering Harbin Engineering University Harbin China Agile and Intelligent Computing Key Laboratory China Electronic Technology Group (Cetc-10) Chengdu China

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

For large incidence/emergence angles in uniform linear arrays, the near-field region shrinks sharply. In this paper, we present Uniform 3D Cylindrical Array (UCA-3D), which can enlarge the near-field region and improve the spectral efficiency. Specifically, using the rotational symmetry of UCA-3D, we can obtain near-field regions that are uniformly enlarged at all angles. As UCA-3D can generate orthogonal near-field beams along the same direction, we can improve the spectral efficiency. The simulation results prove that the theory is valid, and the UCA-3D is feasible.

关键词： Three-dimensional displays Spectral efficiency Array signal processing Simulation Antenna arrays

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Three-Dimensional Data Security Architecture of Agricultural Supply Chain Driven by Blockchain 5

Three-Dimensional Data Security Architecture of Agricultural...

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5th International Conference on High Performance Big Data and intelligent Systems, HDIS 2023

作者： Chen, Xiao Zhang, Guofeng Zhao, Chuan Wang, Bingbing Feng, Bin Wang, Peng Li, Qiuling Li, Songhe Shen, Cong School of Economics and Management Taishan University Taian China School of Information Science and Technology Taishan University Taian China School of Information Science and Engineering Shandong Provincial Key Laboratory of Network-based Intelligent Computing University of Jinan Jinan China School of Information Science and Engineering University of Jinan Jinan China College of Physics and Electronic Engineering Taishan University Taian China

ISBN: (纸本)9798350394160

Agricultural supply chain systems mainly focus on technological applications, but do not pay enough attention to the privacy protection and data security guarantee of the entire supply chain entities. They face practical challenges such as low enthusiasm, poor data quality, and low credibility. This work starts from three dimensions of supply chain entities, blockchain technology, and government/regulatory agencies, fully integrating blockchain and the Internet of Things, blockchain and big data, blockchain and artificial intelligence, and designing a three-dimensional data security architecture for agricultural supply chain. Research suggests that utilizing the data security guarantees provided by three dimensions, the distributed storage, tamper resistance, transparency, traceability and other technical features of blockchain, as well as the automatic perception, real-time aggregation, intelligent processing and other functions of the technology integration platform, can effectively eliminate commercial and security concerns of supply chain entities. It has important reference significance and value for promoting supply chain data security sharing and regulating regulation. More importantly, this work emphasizes the comprehensive guarantee of supply chain security, effectively reversing the direction of integrated application of blockchain and agricultural supply chain, and has important implications for guiding the construction of agricultural supply chain systems. ©2023 IEEE.

关键词： Blockchain

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A Novel Dual-Stage Algorithm for Capacitated Arc Routing Problems with Time-Dependent Service Costs

SSRN

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SSRN 2024年

作者： Li, Qingya Liu, Shengcai Zou, Juan Tang, Ke Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan411105 China

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitations, we propose a novel dual-stage algorithm, called MAENS-GN, that consists of a routing stage and a vehicle departure time optimization stage. The former obtains the routing plan, while the the latter determines the vehicle departure time. Importantly, existing literature often ignores the characteristic information contained in the relationship between the route cost and the vehicle departure time. The most signiﬁcant innovation in this paper lies in the exploitation of this characteristic information during the vehicle departure time optimization stage. Speciﬁcally, we conduct a detailed analysis of this relationship under various scenarios and employ tailored methods to obtain the (approximately) optimal vehicle departure time. Furthermore, we propose an improved initialization strategy that considers time-dependent characteristics to achieve better solution quality. In addition to the modiﬁed benchmark test sets, we also experiment on a real-world test set. Experimental results demonstrate that MAENS-GN can obtain high-quality solutions on both small-scale and larger-scale instances of CARPTDSC. © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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A novel dual-stage algorithm for capacitated arc routing problems with time-dependent service costs

arXiv

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arXiv 2024年

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitations, we propose a novel dual-stage algorithm, called MAENS-GN, that consists of a routing stage and a vehicle departure time optimization stage. The former obtains the routing plan, while the the latter determines the vehicle departure time. Importantly, existing literature often ignores the characteristic information contained in the relationship between the route cost and the vehicle departure time. The most significant innovation in this paper lies in the exploitation of this characteristic information during the vehicle departure time optimization stage. Specifically, we conduct a detailed analysis of this relationship under various scenarios and employ tailored methods to obtain the (approximately) optimal vehicle departure time. Furthermore, we propose an improved initialization strategy that considers time-dependent characteristics to achieve better solution quality. In addition to the modified benchmark test sets, we also experiment on a real-world test set. Experimental results demonstrate that MAENS-GN can obtain high-quality solutions on both small-scale and larger-scale instances of CARPTDSC. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Hierarchical Policies of Subgoals for Safe Deep Reinforcement Learning 2nd

Hierarchical Policies of Subgoals for Safe Deep Reinforcem...

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2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Yu, Fumin Gao, Feng Yuan, Yao Xing, Xiaofei Dai, Yinglong College of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China College of Liberal Arts and Sciences National University of Defense Technology Changsha410073 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9789819902712

Reinforcement learning is a machine learning method that relies on the agent to learn by trial and error to solve decision optimization problems. It is well known that an agent based on deep reinforcement learning in complex environments is difficult to train. Moreover, the agent will generate unsafe and strange actions due to the lack of sufficient reward feedback from the environment. To make the agent converge to a better policy and make its behavior safer and more controllable under sparse rewards, we propose a subgoal embedding method based on prior knowledge and hierarchical strategy that can make the training process converge faster. The subgoal embedding method can be combined with existing reinforcement learning methods. In this paper, we combine the subgoal embedding method with REINFORCE algorithm and PPO(Proximal Policy Optimization) algorithm to test the method in the MiniGrid-Doorkey game environment of the gym platform. The experiments demonstrate the effectiveness of the subgoal embedding method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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