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.
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.
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.
作者：
Jing Hu;Lingfei Wu*;Yu Chen;Po Hu;Mohammed J. Zaki
期刊：
国际自动化与计算杂志,2024年21(2):272-282 ISSN：1476-8186
通讯作者：
Lingfei Wu
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.
作者机构：
[Huan Yang; Yajun Guo] School of Computer Science, Central China Normal University, Wuhan, 430079, Hubei, China;[Yimin Guo] School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, 430073, Hubei, China
通讯机构：
[Yajun Guo] S;School of Computer Science, Central China Normal University, Wuhan, 430079, Hubei, China
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.
作者：
Yanan Chang;Feng Huang;Yi Yang;Jianqun Cui*;Tong Wang
期刊：
Electronics,2024年13(5):868- ISSN：2079-9292
通讯作者：
Jianqun Cui
作者机构：
[Yi Yang] Department of Computer Science, Northeastern Illinois University, Chicago, IL 60625, USA;[Yanan Chang] School of Computer Science, Central China Normal University, No. 152 Luoyu Road, Wuhan 430079, China;Author to whom correspondence should be addressed.;School of Physical Science and Technology, Central China Normal University, No. 152 Luoyu Road, Wuhan 430079, China;[Feng Huang; Tong Wang] School of Physical Science and Technology, Central China Normal University, No. 152 Luoyu Road, Wuhan 430079, China<&wdkj&>School of Computer Science, Central China Normal University, No. 152 Luoyu Road, Wuhan 430079, China
通讯机构：
[Jianqun Cui] S;School of Computer Science, Central China Normal University, No. 152 Luoyu Road, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
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∼31.11%, and more messages were delivered by 14.82∼115.9%.
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.
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.
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.
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.
期刊：
IEEE/ACM Transactions on Computational Biology and Bioinformatics,2024年21(1):120-128 ISSN：1545-5963
作者机构：
[Li, Dandan; Xiao, Zhen; Sun, Han; Jiang, Xingpeng; Zhao, Weizhong; Shen, Xianjun] School of Computer, Central China Normal University, Wuhan, China
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.
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.
作者机构：
[Ma, Yingjun] School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China;[Zhao, Yongbiao] School of Computer, Central China Normal University, Wuhan, China;[Ma, Yuanyuan] School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, China. chonghua_1983@126.com;[Ma, Yuanyuan] Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang, China. chonghua_1983@126.com
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.
摘要：
Traffic prediction is vital to traffic planning, control, and optimization, which is necessary for intelligent traffic management. Existing methods mostly capture spatiotemporal correlations on a fine-grained traffic graph, which cannot make full use of cluster information in coarse-grained traffic graph. However, the flow variation of clusters in the coarse-grained traffic graph is more stable compared with nodes in the fine-grained traffic graph. And the flow variation of a fine-grained node is generally consistent with the trend of the cluster to which the node belongs. Thus information in the coarse-grained traffic graph can guide feature learning in the fine-grained traffic graph. To this end, we propose a Spatiotemporal Multiscale Graph Convolutional Network (SMGCN) that explores spatiotemporal correlations on a multiscale graph. Specifically, given a fine-grained traffic graph, we first generate a coarse-grained traffic graph by graph clustering, and extract spatiotemporal correlations on both fine-grained and coarse-grained traffic graphs. Then we propose a cross-scale fusion (CF) to implement information diffusion between the fine-grained and coarse-grained traffic graphs. Moreover, we employ an adaptive dynamic graph convolution network to mine both static and dynamic spatial features. We evaluate SMGCN on real-world datasets and obtain a 1.18% -3.32% improvement over state-of-the-arts.
作者机构：
[Liao, Kaibo; Peng, Xi] School of Computer, Central China Normal University, Wuhan, Hubei, China;[Peng, Xi] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China
摘要：
Underwater images are often scattered due to suspended particles in the water, resulting in light scattering and blocking and reduced visibility and contrast. Color shifts and distortions are also caused by the absorption of different wavelengths of light in the water. This series of problems will make the underwater image quality greatly impaired, resulting in some advanced visual work can not be carried out underwater. In order to solve these problems, this paper proposes an underwater image enhancement method based on multi-task fusion, called MTF. Specifically, we first use linear constraints on the input image to achieve color correction based on the gray world assumption. The corrected image is then used to achieve visibility enhancement using an improved type-II fuzzy set-based algorithm, while the image is contrast enhanced using standard normal distribution probability density function and softplus function. However, in order to obtain more qualitative results, we propose multi-task fusion, in which we solve for similarity, then we obtain fusion weights that guarantee the best features of the image as much as possible from the obtained similarity, and finally we fuse the image with the weights to obtain the output image, and we find that multi-task fusion has excellent image enhancement and restoration capabilities, and also produces visually pleasing results. Extensive qualitative and quantitative evaluations show that MTF method achieves optimal results compared to ten state-of-the-art underwater enhancement algorithms on 2 datasets. Moreover, the method can achieve better results in application tests such as target detection and edge detection.
期刊：
Pervasive and Mobile Computing,2024年98:101877 ISSN：1574-1192
通讯作者：
Yimin Guo
作者机构：
[Yimin Guo; Ping Xiong; Fan Yang; Chengde Zhang] Zhongnan University of Economics and Law, School of Information and Safety Engineering, China;[Yajun Guo] Central China Normal University, School of Computer, China
通讯机构：
[Yimin Guo] Z;Zhongnan University of Economics and Law, School of Information and Safety Engineering, China
摘要：
In the Industrial Internet of Things (IIoT), haptic control of machines or robots can be managed remotely. However, with the emergence of Tactile Industrial Internet of Things (TIIoT), the transmission of haptic data over public channels has raised security and privacy concerns. In such an environment, mutual authentication between haptic users and remotely controlled entities is crucial to prevent illegal control by adversaries. Therefore, we propose an end-to-end authentication scheme, SecTIIoT, to establish secure communication between haptic users and remote IoT devices. The scheme addresses security issues by using lightweight hash cryptographic primitives and employs a useful piggyback strategy to improve authentication efficiency. We demonstrate that SecTIIoT is resilient to various known attacks with formal security proofs and informal security analysis. Furthermore, our detailed performance analysis shows that SecTIIoT outperforms existing lightweight authentication schemes as it provides more security features while reducing computation and communication costs.
摘要：
Abstract Event detection plays an essential role in the task of event extraction. It aims at identifying event trigger words in a sentence and classifying event types. Generally, multiple event types are usually well‐organized with a hierarchical structure in real‐world scenarios, and hierarchical correlations between event types can be used to enhance event detection performance. However, such kind of hierarchical information has received insufficient attention which can lead to misclassification between multiple event types. In addition, the most existing methods perform event detection in Euclidean space, which cannot adequately represent hierarchical relationships. To address these issues, we propose a novel event detection network HyperED which embeds the event context and types in Poincaré ball of hyperbolic geometry to help learn hierarchical features between events. Specifically, for the event detection context, we first leverage the pre‐trained BERT or BiLSTM in Euclidean space to learn the semantic features of ED sentences. Meanwhile, to make full use of the dependency knowledge, a GNN‐based model is applied when encoding event types to learn the correlations between events. Then we use a simple neural‐based transformation to project the embeddings into the Poincaré ball to capture hierarchical features, and a distance score in hyperbolic space is computed for prediction. The experiments on MAVEN and ACE 2005 datasets indicate the effectiveness of the HyperED model and prove the natural advantages of hyperbolic spaces in expressing hierarchies in an intuitive way.
期刊：
ACM Transactions on Knowledge Discovery from Data,2024年18(1):1–22 ISSN：1556-4681
通讯作者：
Hu, P
作者机构：
[Wang, Huan] Huazhong Agr Univ, PKU Wuhan Inst Artificial Intelligence, Coll Informat, Wuhan 430070, Hubei, Peoples R China.;[Liu, Guoquan; Hu, Po; Hu, P] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Hu, P ] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
关键词：
Transfer learning;link prediction;type-shared knowledge
摘要：
Link prediction has received increased attention in social network analysis. One of the unique challenges in heterogeneous social networks is link prediction in new link types without verified link information, such as recommending products to new overseas groups. Existing link prediction models tend to learn type-specific knowledge on specific link types and predict missing or future links on the same link types. However, because of the uncertainty of new link types in the evolving process of social networks, it is difficult to collect sufficient verified link information in new link types. Therefore, we propose the Transferable Domain Adversarial Network (TDAN) based on transfer learning to handle the challenge. TDAN exploits transferable type-shared knowledge in historical link types to help predict the unobserved links in new link types. TDAN mainly comprises a structural encoder, a domain discriminator, and an optimization decoder. The structural encoder learns the link representations in a heterogeneous social network. Subsequently, to learn transferable type-shared knowledge, the domain discriminator distinguishes link representations into different link types while minimizing the differences between type-specific knowledge in adversarial training. Inspired by the denoising auto-encoder, the optimization decoder reconstructs the learned type-shared knowledge to eliminate the noise generated during the adversarial training. Extensive experiments on Facebook and YouTube show that TDAN can outperform the state-of-the-art models.
