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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Wang, Junling Wu, Dongyang Wang, Yupeng Nanjing University of Science and Technology ZiJin College School of Computer and Artificial Intelligence Nanjing China Nanjing Forestry University College of Information Science and Technology Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351637

Accurate detection of tomato pests and diseases is essential for efficient agriculture as global food demand continues to grow. Traditional methods often struggle with accurate localization and detection, highlighting the need for advanced solutions. Leveraging the Internet of Things (IoT), which facilitates interconnected devices for data acquisition and transmission, this study introduces the TPDDM algorithm to significantly enhance detection capabilities in tomato farming. To address existing challenges, we employ innovative techniques within TPDDM. Data augmentation and mosaic optimization methods are utilized to enrich the dataset and enhance image information, improving model robustness. Integration of a pre-trained Vision Transformer (ViT) into the YOLOX model boosts feature extraction and enriches the model's ability to detect subtle patterns. Additionally, the design of the Adaptive Multi-scale Feature Pyramid Network (AMFPN) enables effective fusion of features across different scales, optimizing target detection accuracy. Evaluation using comprehensive performance metrics demonstrates TPDDM's superiority over traditional approaches and other contemporary models. Real-world application of TPDDM on diverse datasets validates its high accuracy and practical utility in tomato pest and disease detection. This research underscores the transformative potential of IoT-driven technologies in advancing precision agriculture, offering scalable solutions to meet the demands of modern farming practices. © 2024 IEEE.

关键词： Fruits

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LMCF-FS: A Novel Lightweight Malware Classification Framework Driven by Feature Selection 6th

LMCF-FS: A Novel Lightweight Malware Classification Framewor...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Yun, Cui Zhou, Lei Xing, Shuangshuang Yang, Ning Zhao, Pan Chen, Zhiguo School of Computer Jiangsu University of Science and Technology Zhenjiang212100 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science and School of Cyber Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

The rapid increase in the number of malware and its variants poses a significant threat to internet security. While existing machine learning-based malware classification methods can improve accuracy, they often require complex feature engineering. In contrast, converting executable files into images and classifying them using deep learning models can reduce the reliance on prior knowledge of malware features and simplify the feature processing. However, most studies have not adequately considered the impact of image pixel features and size on classification results. In addition, the model structure of deep learning classification methods based on pre-trained parameters and transfer learning is usually more complex, resulting in the classification model requiring more memory and time overhead. To solve the above problems, this paper proposes a novel lightweight malware classification framework LMCF-FS driven by feature selection. This framework uses a proposed new feature selection method based on pixel pair features to improve feature sparsity and optimize image texture features, thereby improving feature expression capabilities. At the same time, an interpolation algorithm is used to balance the image size. In addition, a new lightweight malware classification model ConvInceptionNet is built based on the Inception-C module to balance accuracy, computational cost, and number of parameters, thereby improving malware classification efficiency. Our proposed LMCF-FS framework achieves a classification accuracy of 99.12% on the Microsoft Malware Classification Challenge Dataset (BIG2015 Dataset), and it only takes 0.92 ms to predict a 224 × 224 malware image, verifying LMCF-FS effectiveness in dealing with malware classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image texture

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Cocrystal@protein-anchoring nanococktail for combinatorially treating multidrug-resistant cancer

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Acta Pharmaceutica Sinica B 2024年第10期14卷 4509-4525页

作者： Jiahui Zou Xuyang Xing Chao Teng Qingling Zhao Wei He Xuri Wu Yuanzheng Xia School of Pharmacy China Pharmaceutical UniversityNanjing 211198China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Life Science and Technology China Pharmaceutical UniversityNanjing 211198China State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Bioactive Natural Product Research School of Traditional Chinese PharmacyChina Pharmaceutical UniversityNanjing 211198China

Multidrug resistance(MDR),the major mechanism by which various cancers develop specific resistance to therapeutic agents,has set up enormous obstacles to many forms of tumor *** cocktail therapy administration,based on the combination of multiple drugs for anti-MDR chemotherapy,often suffers from inconsistent in vivo pharmacokinetic behaviors that cannot act synchronously on the lesions,leading to limited pharmacodynamic *** the emergence of nanomedicines,which has improved chemotherapeutic drugs’bioavailability and therapeutic effect on clinical application,these monotherapy-based nano-formulations still show poor progression in overcoming ***,a“one stone and three birds”nanococktail integrated by a cocrystal@protein-anchoring strategy was purposed for triple-payload delivery,which paclitaxel-disulfiram cocrystal-like nanorods(NRs)were anchored with the basic protein drug Cytochrome c(Cyt C),followed by hyaluronic-acid *** particular,NRs were utilized as carrier-like particles to synchronously deliver biomacromolecule Cyt C into tumor cells and then promote cell *** note,on A549/Taxol drug-resistant tumor-bearing mice,the system with extraordinarily high encapsulation efficiency demonstrated prolonged in vivo circulation and increased tumor-targeting accumulation,significantly reversing tumor drug resistance and improving therapeutic *** mechanistic study indicated that the system induced the apoptosis of Taxol-resistant tumor cells through the signal axis P-glycoprotein/Cyt C/caspase ***,this nanococktail strategy offers a promising approach to improve the sensitivity of tumor cells to chemotherapeutic drugs and strengthen intractable drug-resistant oncotherapy.

关键词： Cocktail Multidrug-resistant Co-delivery combined therapy Cytochrome C Nanocrystals

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MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space 38

MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Weng, Jiangwei Yan, Zhiqiang Tai, Ying Qian, Jianjun Yang, Jian Li, Jun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Intelligence Science and Technology Nanjing University Suzhou215163 China

Recent advances in low light image enhancement have been dominated by Retinex-based learning framework, leveraging convolutional neural networks (CNNs) and Transformers. However, the vanilla Retinex theory primarily addresses global illumination degradation and neglects local issues such as noise and blur in dark conditions. Moreover, CNNs and Transformers struggle to capture global degradation due to their limited receptive fields. While state space models (SSMs) have shown promise in the long-sequence modeling, they face challenges in combining local invariants and global context in visual data. In this paper, we introduce MambaLLIE, an implicit Retinex-aware low light enhancer featuring a global-then-local state space design. We first propose a Local-Enhanced State Space Module (LESSM) that incorporates an augmented local bias within a 2D selective scan mechanism, enhancing the original SSMs by preserving local 2D dependencies. Additionally, an Implicit Retinex-aware Selective Kernel module (IRSK) dynamically selects features using spatially-varying operations, adapting to varying inputs through an adaptive kernel selection process. Our Global-then-Local State Space Block (GLSSB) integrates LESSM and IRSK with layer normalization (LN) as its core. This design enables MambaLLIE to achieve comprehensive global long-range modeling and flexible local feature aggregation. Extensive experiments demonstrate that MambaLLIE significantly outperforms state-of-the-art CNN and Transformer-based methods. Our code is available at Project Page. © 2024 Neural information processing systems foundation. All rights reserved.

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On building automation system security

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High-Confidence Computing 2024年第3期4卷 103-122页

作者： Christopher Morales-Gonzalez Matthew Harper Michael Cash Lan Luo Zhen Ling Qun Z.Sun Xinwen Fu Department of Computer Science University of Massachusetts LowellLowell 01854USA Department of Electrical and Computer Engineering University of Central FloridaOrlando 32816USA School of Computer Science and Technology Southeast UniversityMa’anshan 243032China School of Computer Science and Engineering Anhui University of TechnologyNanjing 211189China

Building Automation Systems(BASs)are seeing increased usage in modern society due to the plethora of benefits they provide such as automation for climate control,HVAC systems,entry systems,and lighting *** BASs in use are outdated and suffer from numerous vulnerabilities that stem from the design of the underlying BAS *** this paper,we provide a comprehensive,up-to-date survey on BASs and attacks against seven BAS protocols including BACnet,EnOcean,KNX,LonWorks,Modbus,ZigBee,and *** studies of secure BAS protocols are also presented,covering BACnet Secure Connect,KNX Data Secure,KNX/IP Secure,ModBus/TCP Security,EnOcean High Security and Z-Wave *** and ZigBee do not have security *** point out how these security protocols improve the security of the BAS and what issues remain.A case study is provided which describes a real-world BAS and showcases its vulnerabilities as well as recommendations for improving the security of *** seek to raise awareness to those in academia and industry as well as highlight open problems within BAS security.

关键词： Building automation system BAS protocols Security Attack

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Counterfactual discriminative micro-expression recognition

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Visual Intelligence 2024年第1期2卷 350-359页

作者： Yong Li Menglin Liu Lingjie Lao Yuanzhi Wang Zhen Cui Department of Computer Science City University of Hong KongHong Kong999077China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China

Micro-expressions are spontaneous,rapid and subtle facial movements that can hardly be suppressed or ***-expression recognition(MER)is one of the most challenging topics in affective *** aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting ***,many deep learning-based MER methods have been ***,how to effectively capture subtle temporal variations for robust MER still perplexes *** propose a counterfactual discriminative micro-expression recognition(CoDER)method to effectively learn the slight temporal variations for video-based *** explicitly capture the causality from temporal dynamics hidden in the micro-expression(ME)sequence,we propose ME counterfactual reasoning by comparing the effects of the facts *** ME sequences and the counterfactuals ***-revised ME sequences,and then perform causality-aware prediction to encourage the model to learn those latent ME temporal *** experiments on four widely-used ME databases demonstrate the effectiveness of CoDER,which results in comparable and superior MER performance compared with that of the state-of-the-art *** visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces.

关键词： Affective computing Micro-expression recognition Temporal variation Counterfactual reasoning Causal graph

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Semantic and secure search over encrypted outsourcing cloud based on BERT

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Frontiers of computer science 2022年第2期16卷 152-159页

作者： Zhangjie FU Yan WANG Xingming SUN Xiaosong ZHANG Engineering Research Center of Digital Forensice Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Searchable encryption provides an effective way for data security and privacy in cloud *** can retrieve encrypted data in the cloud under the premise of protecting their own data security and ***,most of the current content-based retrieval schemes do not contain enough semantic information of the article and cannot fully reflect the semantic information of the *** this paper,we propose two secure and semantic retrieval schemes based on BERT(bidirectional encoder representations from transformers)named SSRB-1,*** training the documents with BERT,the keyword vector is generated to contain more semantic information of the documents,which improves the accuracy of retrieval and makes the retrieval result more consistent with the user’s ***,through testing on real data sets,it is shown that both of our solutions are feasible and effective.

关键词： cloud computing semantic search BERT model searchable encryption

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Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots

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Tsinghua science and technology 2024年第5期29卷 1481-1494页

作者： Xu Zhou Benlian Xu Zhengqiang Jiang Jun Li Brett Nener School of Mechanical Engineering Changshu Institute of TechnologyChangshu 215500China School of Automation Nanjing University of Science and TechnologyNanjing 210094China School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Faculty of Medicine and Health The University of SydneySydney 2006Australia Department of Electrical Electronic and Computer EngineeringThe University of Western AustraliaPerth 6009Australia

Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.

关键词： reinforcement learning manual reset multi-state recovery failure prediction

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Exposing Audio-Visual Forgeries in Frequency Domain 18th

Exposing Audio-Visual Forgeries in Frequency Domain

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Wan, Yuanfei Wang, Jian Cui, Jinrong Sun, Yunlian School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China South China Agricultural University Guangzhou China

ISBN: (纸本)9789819610679

Recently, the rapid development of deepfake technology attracted strong attention from the community. Some previous work on deepfake detection achieved good results in the frequency domain, which inspires us to combine frequency-domain information with temporal and spatial domains of visual to detect deepfakes. In addition, the audio signal can be represented in the frequency domain, so we can explore multimodal frequency-domain cues by combining audio and visual modalities. In this paper, we propose a Frequency-aware Audio-Visual Deepfake Detection(FAVDD) method. Specifically, we design a Frequency-Temporal-Spatial(FTS) visual encoder that extracts spatial, frequency, and temporal forgery cues and embeds them into visual features to form a unified representation. In addition, we project the audio signal into the frequency domain by Fourier transform and capture the forgery traces, which are later combined with visual features for deepfake detection. The results show that our proposed framework effectively combines multiple cues and achieves good results on three multimodal deepfake datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Audio acoustics

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Zero Trust Networks: Evolution and Application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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