作者机构：
[Zhang, Yang; Zhang, Y] Shanghai Polytech Univ, Sch Econ & Management, Shanghai 201209, Peoples R China.;[Wang, Xuechun] Hubei Univ Technol, Sch Elect & Elect Engn, Wuhan 430068, Peoples R China.;[Wen, Jinghao] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.;[Zhu, Xianxun] Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China.
通讯机构：
[Zhang, Y ] S;Shanghai Polytech Univ, Sch Econ & Management, Shanghai 201209, Peoples R China.
关键词：
Human presence sensing;Machine learning;Non-contact;Wireless perception
摘要：
In the swiftly evolving landscape of Internet of Things (IoT) technology, the demand for adaptive non-contact sensing has seen a considerable surge. Traditional human perception technologies, such as vision-based approaches, often grapple with problems including lack of sensor versatility and sub-optimal accuracy. To address these issues, this paper introduces a novel, non-contact method for human presence perception, relying on WiFi. This innovative approach involves a sequential process, beginning with the pre-processing of collected Channel State Information (CSI), followed by feature extraction, and finally, classification. By establishing signal models that correspond to varying states, this method enables the accurate perception and recognition of human presence. Remarkably, this technique exhibits a high level of precision, with sensing accuracy reaching up to 99%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}. The potential applications of this approach are extensive, proving to be particularly beneficial in contexts such as smart homes and healthcare, amongst various other everyday scenarios. This underscores the significant role this novel method could play in enhancing the sophistication and effectiveness of human presence detection and recognition systems in the IoT era.
期刊：
IEEE ROBOTICS AND AUTOMATION LETTERS,2024年9(3):2646-2653 ISSN：2377-3766
通讯作者：
Lu, ZY
作者机构：
[Zhao, Zhou] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.;[Lu, Zhenyu; He, Wenhao] Univ West England, Fac Environm & Technol, Bristol BS16 1QY, England.;[Lu, Zhenyu; He, Wenhao] Univ West England, Bristol Robot Lab, Bristol BS16 1QY, England.
通讯机构：
[Lu, ZY ] U;Univ West England, Fac Environm & Technol, Bristol BS16 1QY, England.;Univ West England, Bristol Robot Lab, Bristol BS16 1QY, England.
关键词：
Grasping;Robots;Robot sensing systems;Tactile sensors;Deep learning;Exoskeletons;Sensors;deep learning in grasping and manipulation;learning from experience
摘要：
To minimize irrelevant and redundant information in tactile data and harness the dexterity of human hands. In this paper, we introduce a novel binary classification network with normalized differential convolution (NDConv) layers. Our method leverages the recent progress in visual-based tactile sensing to significantly improve the accuracy of grasp stability prediction. First, we collect a dataset from human demonstration by grasping 15 different daily objects. Then, we rethink pixel correlation and design a novel NDConv layer to fully utilize spatio-temporal information. Finally, the classification network not only achieves a real-time temporal sequence prediction but also obtains an average classification accuracy of 92.97%. The experimental results show that the network can hold a high classification accuracy even when facing unseen objects.
作者机构：
[Jing Hu; Po Hu] School of Computer Science, Central China Normal University, Wuhan, China;[Lingfei Wu] Pinterest, San Francisco, USA;[Yu Chen] Meta, Mountain View, USA;[Mohammed J.Zaki] Rensselaer Polytechnic Institute, Troy, USA
通讯机构：
[Lingfei Wu] P;Pinterest, San Francisco, USA
摘要：
The conversation machine comprehension (MC) task aims to answer questions in the multi-turn conversation for a single passage. However, recent approaches don’t exploit information from historical conversations effectively, which results in some references and ellipsis in the current question cannot be recognized. In addition, these methods do not consider the rich semantic relationships between words when reasoning about the passage text. In this paper, we propose a novel model GraphFlow+, which constructs a context graph for each conversation turn and uses a unique recurrent graph neural network (GNN) to model the temporal dependencies between the context graphs of each turn. Specifically, we exploit three different ways to construct text graphs, including the dynamic graph, static graph, and hybrid graph that combines the two. Our experiments on CoQA, QuAC and DoQA show that the GraphFlow+ model can outperform the state-of-the-art approaches.
摘要：
With the rapid development of IoT technology, smart homes have emerged. At the same time, data security and privacy protection are also of great concern. However, the traditional centralized authentication scheme has defects such as single point of failure, poor scalability, center dependence, vulnerability to attacks, etc., and is not suitable for the distributed and dynamically changing smart home environment. Thus, many researchers have proposed decentralized authentication schemes based on blockchain technology. Although many characteristics of blockchain technology such as decentralization, non -tampering, and solving single point of failure have good application scenarios in authentication, the mature integration of the two applications has to be further explored. For example, the introduction of blockchain also brings security issues; the balance between security and performance in most blockchain-based authentication schemes remains to be investigated; and resource -constrained IoT devices tend to perform a large number of intensive computations, which is clearly inappropriate. Consequently, this paper introduces fog computing in blockchain-based authentication schemes, proposes a network architecture in which cloud and fog computing work together, and investigates the security and performance issues of authentication schemes under this architecture. Finally, formal and informal security analysis show that our scheme has multiple security properties, and our scheme has better performance than existing solutions.
摘要：
Entities and relations extraction are the key tasks in the construction of biomedical knowledge graph, which play an important role in the biomedical artificial intelligence. However, extraction of entities and relations from biomedical texts is challenging because of the overlapping triples problem. The previous approaches typically divided the task into two separate sub-tasks. However, these methods failed to address the error propagation problem. Recent methods have been proposed to perform both sub-tasks simultaneously. Nonetheless, most current methods still encounter issues related to imbalanced interactions and independent features. In this paper, we propose a novel method based on feature partition encoding and relative positional embedding to joint extract biomedical entity and relation triples simultaneously. Compared to previous work, our method shows exceptional accurate in extracting entities and relations, while efficiently tackling the challenge of overlapping triples in biomedical texts. Our work has two contributions. Firstly, our method divides the features into task-specific and shared parts through entity, relation and sharing partitions at the encoding stage. And the encoded features will be aggregated according to the subsequent tasks. Secondly, we introduce a relative positional embedding method to capture the relative distance information between token pairs. In this way, our method can effectively deal with the sub-tasks interactions problem and improve entities and relations extraction. The experimental results show that our method improves the F1 scores of relations extraction by 3.2%, 2.1%, 3.4%, and 2.8% on four biomedical datasets, respectively.
摘要：
With the development of information networks, the entities from different network domains interact with each other more and more frequently. Therefore, identity management and authentication are essential in cross-domain setting. The traditional Public Key Infrastructure (PKI) architecture has some problems, including single point of failure, inefficient certificate revocation status management and also lack of privacy protection, which cannot meet the demand of cross-domain identity authentication. Blockchain is suitable for multi-participant collaboration in multi-trust domain scenarios. In this paper, a cross-domain certificate management scheme CD-BCM based on the consortium blockchain is proposed. For the issue of Certificate Authority’s single point of failure, we design a multi-signature algorithm. In addition, we propose a unified structure for batch certificates verification and conversion, which improve the efficiency of erroneous certificate identification. Finally, by comparing with current related schemes, our scheme achieves good functionality and scalability in the scenario of cross-domain certificate management.
摘要：
Visual Dialog aims to answer an appropriate response based on a multi-round dialog history and a given image. Existing methods either focus on semantic interaction, or implicitly capture coarse-grained structural interaction (e.g., pronoun co-references). The fine-grained and explicit structural interaction feature for dialog history is seldom explored, resulting in insufficient feature learning and difficulty in capturing precise context. To address these issues, we propose a structure-aware dual-level graph interactive network (SDGIN) that integrates verb-specific semantic roles and co-reference resolution to explicitly capture context structural features for discriminative and generative tasks in visual dialog. Specifically, we create a novel structural interaction graph that injects syntactic knowledge priors into dialog by introducing semantic role labeling that imply which words are sentence stems. Furthermore, considering the single perspective limitation of previous algorithms, we design a dual-perspective mechanism that learns fine-grained token-level context structure features and coarse-grained utterance-level interactions in parallel. It possess an elegant view to explore precise context interactions, realizing the mutual complementation and enhancement of different granularity features. Experimental results show the superiority of our proposed approach. Compared to other task-specific models, our SDGIN outperforms previous models and achieves a significant improvement on the benchmark dataset VisDial v1.0.
关键词：
Semantic processing;Word sense disambiguation;Anaphora resolution;Named entity recognition;Concept extraction;Subjectivity detection
摘要：
Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.
作者机构：
[Liu, Hui] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.;[Liu, Hui] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.;[Guo, Jiabao; Zhao, Bo] Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China.;[Zhang, Kehuan] Chinese Univ Hong Kong, Coll Informat Engn, Hong Kong 999077, Peoples R China.;[Liu, Peng] Penn State Univ, Coll Informat Sci & Technol, State Coll, PA 16801 USA.
通讯机构：
[Liu, H ] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.
关键词：
Deep neural networks;Adversarial examples;Adversarial detection;Autoencoder;Isolation forest
摘要：
Although deep neural networks (DNNs) have performed well on many perceptual tasks, they are vulnerable to adversarial examples that are generated by adding slight but maliciously crafted perturbations to benign images. Adversarial detection is an important technique for identifying adversarial examples before they are entered into target DNNs. Previous studies that were performed to detect adversarial examples either targeted specific attacks or required expensive computation. Designing a lightweight unsupervised detector is still a challenging problem. In this paper, we propose an AutoEncoder-based Adversarial Examples (AEAE) detector that can guard DNN models by detecting adversarial examples with low computation in an unsupervised manner. The AEAE includes only a shallow autoencoder that performs two roles. First, a well-trained autoencoder has learned the manifold of benign examples. This autoencoder can produce a large reconstruction error for adversarial images with large perturbations, so we can detect significantly perturbed adversarial examples based on the reconstruction error. Second, the autoencoder can filter out small noises and change the DNN's prediction on adversarial examples with small perturbations. It helps to detect slightly perturbed adversarial examples based on the prediction distance. To cover these two cases, we utilize the reconstruction error and prediction distance from benign images to construct a two-tuple feature set and train an adversarial detector using the isolation forest algorithm. We show empirically that AEAE is an unsupervised and inexpensive detector against most state-of-the-art attacks. Through the detection in these two cases, there is nowhere to hide adversarial examples.
作者机构：
[Guo, Yajun; Duan, Xinrui] Cent China Normal Univ, Sch Comp Sci, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Guo, Yimin] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, South Lake Ave, Wuhan 430073, Hubei, Peoples R China.
通讯机构：
[Guo, YJ ] C;Cent China Normal Univ, Sch Comp Sci, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
关键词：
Authentication protocol;Vehicular ad hoc networks;Blockchain;Physically unclonable function;Vehicular fog services;Conditional anonymity
摘要：
With the advances in smart vehicles and fog computing, Fog computing is extended to traditional Vehicular Ad Hoc Networks (VANETs). As a geographically distributed paradigm, Vehicle Fog Service (VFS) overcomes the limitations of VANETs in real-time response and location awareness. It supports a wide range of traffic information services, such as road warnings, congestion control, and autonomous driving. Secure communication between VFS entities is a critical problem in an open network. Meanwhile, most fog nodes are deployed in the public domain and are vulnerable to physical attacks. This paper proposes a secure authentication scheme for VFS to address the above issues. The scheme combines blockchain and physical unclonable function (PUF) to achieve two-way authentication of on-board units (OBU) and road side units (RSU) with the untrusted fog nodes. Our scheme provides conditional anonymity and non-repudiation, offering recourse in case of malicious behavior. Unlike other schemes, the proposed scheme only needs to determine whether the pseudo-identity has a revocation tag instead of scanning the whole certificate revocation list (CLS), significantly reducing the computational overhead. In addition, we use the Real-Or-Random ROR model and formally prove that the proposed scheme is provably secure, and informal security analysis shows that the scheme is robust to various known attacks. Finally, compared with existing schemes, our scheme maintains lower communication and computation costs and provides more security features, which shows that our scheme is more suitable for secure VFS environments.
作者机构：
[Zhang, Miao] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Zhang, Miao; He, Tingting; Dong, Ming; Dong, M] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.;[Zhang, Miao; He, Tingting; Dong, Ming; Dong, M] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.;[He, Tingting; Dong, Ming; Dong, M] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
通讯机构：
[He, TT; Dong, M ] C;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
摘要：
Commonsense question answering (CQA) requires understanding and reasoning over QA context and related commonsense knowledge, such as a structured Knowledge Graph (KG). Existing studies combine language models and graph neural networks to model inference. However, traditional knowledge graph are mostly concept-based, ignoring direct path evidence necessary for accurate reasoning. In this paper, we propose MRGNN (Meta-path Reasoning Graph Neural Network), a novel model that comprehensively captures sequential semantic information from concepts and paths. In MRGNN, meta-paths are introduced as direct inference evidence and an original graph neural network is adopted to aggregate features from both concepts and paths simultaneously. We conduct sufficient experiments on the CommonsenceQA and OpenBookQA datasets, showing the effectiveness of MRGNN. Also, we conduct further ablation experiments and explain the reasoning behavior through the case study.
摘要：
Online collaborative learning (OCL) has become a common instructional strategy in higher education for developing students' skills in collaboration, problem-solving, and critical thinking. Cognitive engagement in OCL evolves dynamically, but we do not yet fully understand which patterns of cognitive engagement are conducive to OCL and when to promote them. This study used entropy analysis, sequential pattern mining, and temporal network analysis to examine the online discourse of 44 college students who participated in three OCL tasks. Results showed that, compared with the low-performance groups, the high-performance groups exhibited patterns of continuous perspective elaboration and low-level regulation, as well as frequent shifts from perspective expression to perspective elaboration. In addition, there were differences in the longitudinal evolution patterns of cognitive engagement between the high- and low- performance groups. These findings have important implications for learning tool design and improving collaborative learning design.
作者：
Zhenyu Lu;Zhou Zhao;Tianqi Yue;Xu Zhu;Ning Wang
期刊：
IEEE Transactions on Cognitive and Developmental Systems,2024年16(2):407-415 ISSN：2379-8920
作者机构：
[Tianqi Yue; Xu Zhu] Department of Engineering Mathematics and the Bristol Robotics Laboratory, University of Bristol, Bristol, U.K.;[Zhou Zhao] School of Computer Science, Central China Normal University, Wuhan, China;[Zhenyu Lu; Ning Wang] Faculty of Environment and Technology and the Bristol Robotics Laboratory, University of the West of England, Bristol, U.K.
摘要：
This article presents a new bioinspired tactile sensor that is multifunctional and has different sensitivity contact areas. The TacTop area is sensitive and is used for object classification when there is a direct contact. On the other hand, the TacSide area is less sensitive and is used to localize the side contact areas. By connecting tendons from the TacSide area to the TacTop area, the sensor is able to perform multiple detection functions using the same expression region. For the mixed contacting signals collected from the expression region with numerous markers and pins, we build a modified DenseNet121 network which specifically removes all fully connected layers and keeps the rest as a subnetwork. The proposed model also contains a global average pooling layer with two branching networks to handle different functions and provide accurate spatial translation of the extracted features. The experimental results demonstrate a high-prediction accuracy of 98% for object perception and localization. Furthermore, the new tactile sensor is utilized for obstacle avoidance, where action skills are extracted from human demonstrations and then an action data set is generated for reinforcement learning to guide robots toward correct responses after contact detection. To evaluate the effectiveness of the proposed framework, several simulations are performed in the MuJoCo environment.
期刊：
BRIEFINGS IN BIOINFORMATICS,2024年25(3) ISSN：1467-5463
通讯作者：
Xingpeng Jiang<&wdkj&>Cuihong Wan
作者机构：
[Wan, Cuihong; Peng, Zhao] School of Life Sciences, and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan 430079, Hubei, People's Republic of China;[Li, Jiaqiang; Jiang, Xingpeng] School of Computer Science, and Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, Hubei, People's Republic of China
通讯机构：
[Xingpeng Jiang; Cuihong Wan] S;School of Computer Science , and Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan 430079, Hubei , People’s Republic of China<&wdkj&>School of Life Sciences , and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University , Wuhan 430079, Hubei , People’s Republic of China
摘要：
Small open reading frames (smORFs) have been acknowledged to play various roles on essential biological pathways and affect human beings from diabetes to tumorigenesis. Predicting smORFs in silico is quite a prerequisite for processing the omics data. Here, we proposed the smORF-coding-potential-predicting framework, sOCP, which provides functions to construct a model for predicting novel smORFs in some species. The sOCP model constructed in human was based on in-frame features and the nucleotide bias around the start codon, and the small feature subset was proved to be competent enough and avoid overfitting problems for complicated models. It showed more advanced prediction metrics than previous methods and could correlate closely with experimental evidence in a heterogeneous dataset. The model was applied to Rattus norvegicus and exhibited satisfactory performance. We then scanned smORFs with ATG and non-ATG start codons from the human genome and generated a database containing about a million novel smORFs with coding potential. Around 72000 smORFs are located on the lncRNA regions of the genome. The smORF-encoded peptides may be involved in biological pathways rare for canonical proteins, including glucocorticoid catabolic process and the prokaryotic defense system. Our work provides a model and database for human smORF investigation and a convenient tool for further smORF prediction in other species.
作者：
Wang, Tong;Cui, Jianqun;Chang, Yanan;Huang, Feng;Yang, Yi
期刊：
Electronics,2024年13(5):868- ISSN：2079-9292
通讯作者：
Cui, JQ
作者机构：
[Huang, Feng; Wang, Tong] Cent China Normal Univ, Sch Phys Sci & Technol, 152 Luoyu Rd, Wuhan 430079, Peoples R China.;[Cui, Jianqun; Huang, Feng; Chang, Yanan; Wang, Tong; Cui, JQ] Cent China Normal Univ, Sch Comp Sci, 152 Luoyu Rd, Wuhan 430079, Peoples R China.;[Yang, Yi] NE Illinois Univ, Dept Comp Sci, Chicago, IL 60625 USA.
通讯机构：
[Cui, JQ ] C;Cent China Normal Univ, Sch Comp Sci, 152 Luoyu Rd, Wuhan 430079, Peoples R China.
关键词：
opportunistic mobile networks;energy-efficient;geographic information
摘要：
Opportunistic mobile networks, as an important supplement to the traditional communication methods in unique environments, are composed of mobile communication devices. It is a network form that realizes message transmission by using the opportune encounter of these mobile communication devices. Consequently, mobile communication devices necessitate periodic contact detection in order to identify potential communication opportunities, thereby leading to a substantial reduction in the already limited battery life of such devices. Previous studies on opportunistic networks have often utilized geographic information in routing design to enhance message delivery rate. However, the significance of geographic information in energy conservation has been overlooked. Furthermore, previous research on energy-efficient routing has lacked diversification in terms of the methods employed. Therefore, this paper proposes a dynamic co-operative energy-efficient routing algorithm based on geographic information perception (DCEE-GIP) to leverage geographic information to facilitate dynamic co-operation among nodes and optimize node sleep time through probabilistic analysis. The DCEE-GIP routing and other existing algorithms were simulated using opportunistic network environment (ONE) simulation. The results demonstrate that DCEE-GIP effectively extends network service time and successfully delivers the most messages. The service time of DCEE-GIP increased by 8.05 similar to 31.11%, and more messages were delivered by 14.82 similar to 115.9%.
作者机构：
[Muyang Mei; Wei Li; Zhongshuai Feng] Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China;[Yuan Li; Mengchao Niu; Jiaye Zhu] School of Computer Science, Central China Normal University, Wuhan 430079, China;[Ming Luo] State Key Laboratory of Optical Communication Technologies and Networks, China Information and Communication Technologies Group Corporation, Wuhan 430205, China;[Xuefeng Wu; Liang Mei] Fiberhome Telecommunication Technologies Co., Ltd., Wuhan 430205, China;[Qianggao Hu; Yi Jiang; Xuefeng Yang] Accelink Technologies Co., Ltd. Wuhan 430205, China
通讯机构：
[Wei Li] W;Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
摘要：
We propose using physical-informed neural network (PINN) for power evolution prediction in bidirectional Raman amplified WDM systems with Rayleigh backscattering (RBS). Unlike models based on data-driven machine learning, PINN can be effectively trained without preparing a large amount of data in advance and can learn the potential rules of power evolution. Compared to previous applications of PINN in power prediction, our model considers bidirectional Raman pumping and RBS, which is more practical. We experimentally demonstrate power evolution prediction of 200 km bidirectional Raman amplified wavelength-division multiplexed (WDM) system with 47 channels and 8 pumps using PINN. The maximum prediction error of PINN compared to experimental results is only 0.38 dB, demonstrating great potential for application in power evolution prediction. The power evolution predicted by PINN shows good agreement with the results simulated by traditional numerical method, but its efficiency is more suitable for establishing models and calculating noise, providing convenience for subsequent power configuration optimization.
摘要：
Identification of potential human-virus protein-protein interactions (PPIs) contributes to the understanding of the mechanisms of viral infection and to the development of antiviral drugs. Existing computational models often have more hyperparameters that need to be adjusted manually, which limits their computational efficiency and generalization ability. Based on this, this study proposes a kernel Bayesian logistic matrix decomposition model with automatic rank determination, VKBNMF, for the prediction of human-virus PPIs. VKBNMF introduces auxiliary information into the logistic matrix decomposition and sets the prior probabilities of the latent variables to build a Bayesian framework for automatic parameter search. In addition, we construct the variational inference framework of VKBNMF to ensure the solution efficiency. The experimental results show that for the scenarios of paired PPIs, VKBNMF achieves an average AUPR of 0.9101, 0.9316, 0.8727, and 0.9517 on the four benchmark datasets, respectively, and for the scenarios of new human (viral) proteins, VKBNMF still achieves a higher hit rate. The case study also further demonstrated that VKBNMF can be used as an effective tool for the prediction of human-virus PPIs.
作者机构：
[Zhang, Ze; Cui, Jianqun; Hsu, Chingfang; Zhao, Zhuo] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.;[Au, Man Ho] Hong Kong Polytech Univ, Dept Comp, Hong Kong 25809, Peoples R China.;[Harn, Lein] Univ Missouri Kansas City, Dept Comp Sci Elect Engn, Kansas City, MO 64110 USA.;[Xia, Zhe] Wuhan Univ Technol, Dept Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Hsu, CF ] C;Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.
关键词：
Internet of Drones;Lightweight;Mutual authentication;Physical unclonable functions;UAVs;ROR;AVISPA
摘要：
In recent years, the industry and research have cast great attention to the Internet of Drones (IoD), which is becoming progressively popular since it can bring a great convenience to various application scenarios, such as national map exploration, public safety monitoring and automated military applications. In these scenarios, Unmanned Aerial Vehicles (UAVs) (called drones) will be used to collect private information. Due to the fact that the private information are very sensitive, and drones working in public places easily suffer from physical capture or tampering attacks, the primary concern is that this information could be collected by adversaries or unauthorized users. In addition, as resource-constrained devices, drones are mostly equipped with small memory and have limited computing power. Therefore, how to ensure robust security as much as possible while achieving lightweight computing and communication costs has become an urgent problem to be solved in this field. In this paper, we propose A PUF-based Robust and Lightweight Authentication Protocol for Drone-Gateway and Drone-Drone Communication (PRLAP-IoD) to address these issues. Both formal security validation using conventional tools (ROR Model and AVISPA) and other informal security analysis clearly demonstrate that PRLAP-IoD can not only provide physical security, but also defend against a variety of known attacks. Finally, compared with the recent Authentication and Key Agreement (AKA) schemes, PRLAP-IoD can attain a delicate balance between computation cost and communication cost in IoD environment.
期刊：
IEEE/ACM Transactions on Computational Biology and Bioinformatics,2024年21(1):120-128 ISSN：1545-5963
通讯作者：
Shen, XJ
作者机构：
[Shen, Xianjun; Xiao, Zhen; Zhao, Weizhong; Shen, XJ; Jiang, Xingpeng; Sun, Han; Li, Dandan] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
通讯机构：
[Shen, XJ ] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
关键词：
Drugs;Diseases;Proteins;Heterogeneous networks;Kernel;Semantics;Matrix decomposition;Drug repositioning;drug-disease association prediction;heterogeneous networks;graph attention model;multi-kernel deep learning
摘要：
Computational drug repositioning can identify potential associations between drugs and diseases. This technology has been shown to be effective in accelerating drug development and reducing experimental costs. Although there has been plenty of research for this task, existing methods are deficient in utilizing complex relationships among biological entities, which may not be conducive to subsequent simulation of drug treatment processes. In this article, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically, we first construct a heterogeneous information network by combining drug-disease, drug-protein and disease-protein biological networks. Then, a multi-layer graph attention model is utilized to capture the complex associations in the network to derive representations for drugs and diseases. Finally, to maintain the relationship of nodes in different feature spaces, we propose a multi-kernel learning method to transform and combine the representations. Experimental results demonstrate that HMLKGAT outperforms six state-of-the-art methods in drug-related disease prediction, and case studies of five classical drugs further demonstrate the effectiveness of HMLKGAT.
