计算机科学最新文献

{"title":"Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.","authors":"Kangning Wang, Wei Wei, Weibo Yi, Shuang Qiu, Huiguang He, Minpeng Xu, Dong Ming","doi":"10.1016/j.neunet.2024.106617","DOIUrl":"10.1016/j.neunet.2024.106617","url":null,"abstract":"<p><p>Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits the practical application of the methods. This study aimed to build a reliable vigilance estimation method using a small amount of unlabeled calibration data. We conducted a vigilance experiment in the designed BCI-based cursor-control task. Electroencephalogram (EEG) signals of eighteen participants were recorded in two sessions on two different days. And, we proposed a contrastive fine-grained domain adaptation network (CFGDAN) for vigilance estimation. Here, an adaptive graph convolution network (GCN) was built to project the EEG data of different domains into a common space. The fine-grained feature alignment mechanism was designed to weight and align the feature distributions across domains at the EEG channel level, and the contrastive information preservation module was developed to preserve the useful target-specific information during the feature alignment. The experimental results show that the proposed CFGDAN outperforms the compared methods in our BCI vigilance dataset and SEED-VIG dataset. Moreover, the visualization results demonstrate the efficacy of the designed feature alignment mechanisms. These results indicate the effectiveness of our method for vigilance estimation. Our study is helpful for reducing calibration efforts and promoting the practical application potential of vigilance estimation methods.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning.","authors":"Cong Wang, Yaoming Wang, Ying Yuan, Sancheng Peng, Guorui Li, Pengfei Yin","doi":"10.1016/j.neunet.2024.106621","DOIUrl":"10.1016/j.neunet.2024.106621","url":null,"abstract":"<p><p>Vehicular edge computing (VEC), a promising paradigm for the development of emerging intelligent transportation systems, can provide lower service latency for vehicular applications. However, it is still a challenge to fulfill the requirements of such applications with stringent latency requirements in the VEC system with limited resources. In addition, existing methods focus on handling the offloading task in a certain time slot with statically allocated resources, but ignore the heterogeneous tasks' different resource requirements, resulting in resource wastage. To solve the real-time task offloading and heterogeneous resource allocation problem in VEC system, we propose a decentralized solution based on the attention mechanism and recurrent neural networks (RNN) with a multi-agent distributed deep deterministic policy gradient (AR-MAD4PG). First, to address the partial observability of agents, we construct a shared agent graph and propose a periodic communication mechanism that enables edge nodes to aggregate information from other edge nodes. Second, to help agents better understand the current system state, we design an RNN-based feature extraction network to capture the historical state and resource allocation information of the VEC system. Thirdly, to tackle the challenges of excessive joint observation-action space and ineffective information interference, we adopt the multi-head attention mechanism to compress the dimension of the observation-action space of agents. Finally, we build a simulation model based on the actual vehicle trajectories, and the experimental results show that our proposed method outperforms the existing approaches.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141996788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An information-theoretic perspective of physical adversarial patches.","authors":"Bilel Tarchoun, Anouar Ben Khalifa, Mohamed Ali Mahjoub, Nael Abu-Ghazaleh, Ihsen Alouani","doi":"10.1016/j.neunet.2024.106590","DOIUrl":"10.1016/j.neunet.2024.106590","url":null,"abstract":"<p><p>Real-world adversarial patches were shown to be successful in compromising state-of-the-art models in various computer vision applications. Most existing defenses rely on analyzing input or feature level gradients to detect the patch. However, these methods have been compromised by recent GAN-based attacks that generate naturalistic patches. In this paper, we propose a new perspective to defend against adversarial patches based on the entropy carried by the input, rather than on its saliency. We present Jedi, a new defense against adversarial patches that tackles the patch localization problem from an information theory perspective; leveraging the high entropy of adversarial patches to identify potential patch zones, and using an autoencoder to complete patch regions from high entropy kernels. Jedi achieves high-precision adversarial patch localization and removal, detecting on average 90% of adversarial patches across different benchmarks, and recovering up to 94% of successful patch attacks. Since Jedi relies on an input entropy analysis, it is model-agnostic, and can be applied to off-the-shelf models without changes to the training or inference of the models. Moreover, we propose a comprehensive qualitative analysis that investigates the cases where Jedi fails, comparatively with related methods. Interestingly, we find a significant core failure cases among the different defenses share one common property: high entropy. We think that this work offers a new perspective to understand the adversarial effect under physical-world settings. We also leverage these findings to enhance Jedi's handling of entropy outliers by introducing Adaptive Jedi, which boosts performance by up to 9% in challenging images.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142009846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-focus image fusion with parameter adaptive dual channel dynamic threshold neural P systems.","authors":"Bo Li, Lingling Zhang, Jun Liu, Hong Peng, Qianying Wang, Jiaqi Liu","doi":"10.1016/j.neunet.2024.106603","DOIUrl":"10.1016/j.neunet.2024.106603","url":null,"abstract":"<p><p>Multi-focus image fusion (MFIF) is an important technique that aims to combine the focused regions of multiple source images into a fully clear image. Decision-map methods are widely used in MFIF to maximize the preservation of information from the source images. While many decision-map methods have been proposed, they often struggle with difficulties in determining focus and non-focus boundaries, further affecting the quality of the fused images. Dynamic threshold neural P (DTNP) systems are computational models inspired by biological spiking neurons, featuring dynamic threshold and spiking mechanisms to better distinguish focused and unfocused regions for decision map generation. However, original DTNP systems require manual parameter configuration and have only one stimulus. Therefore, they are not suitable to be used directly for generating high-precision decision maps. To overcome these limitations, we propose a variant called parameter adaptive dual channel DTNP (PADCDTNP) systems. Inspired by the spiking mechanisms of PADCDTNP systems, we further develop a new MFIF method. As a new neural model, PADCDTNP systems adaptively estimate parameters according to multiple external inputs to produce decision maps with robust boundaries, resulting in high-quality fusion results. Comprehensive experiments on the Lytro/MFFW/MFI-WHU dataset show that our method achieves advanced performance and yields comparable results to the fourteen representative MFIF methods. In addition, compared to the standard DTNP systems, PADCDTNP systems improve the fusion performance and fusion efficiency on the three datasets by 5.69% and 86.03%, respectively. The codes for both the proposed method and the comparison methods are released at https://github.com/MorvanLi/MFIF-PADCDTNP.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unpacking the complexity of online incivility: an analysis of characteristics and impact of uncivil behavior during the Hong Kong protests","authors":"Baiqi Li, Yunya Song, Yongren Shi, Hsuan-Ting Chen","doi":"10.1108/intr-12-2023-1169","DOIUrl":"https://doi.org/10.1108/intr-12-2023-1169","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>This study seeks to establish a new framework for categorizing incivility, differentiating between explicit and implicit forms, and to investigate their respective abilities to proliferate and mobilize conversations, along with behavioral outcomes in various social contexts.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>Employing computational techniques, this research analyzed 10,145 protest-related threads from the HK Golden Forum, a prominent online discussion board in Hong Kong.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Our analysis revealed divergent effects of explicit and implicit incivility on their diffusion, influences on deliberative discussions, and user participation. Explicit incivility was found to impede deliberative conversations, while implicit incivility tended to provoke more responses. Explicit uncivil expressions encouraged the propagation of incivility but reduced the likelihood of individual involvement. In contrast, implicit incivility had a stronger dampening effect on further uncivil comments and achieved greater thread popularity. The results showed strong associations between uncivil expressions and the contextual norms surrounding social movements.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>Theoretically, this research introduced a classification of incivility and underscored the importance of differentiating between implicit and explicit incivility by examining their effects on deliberation and engagement. Although previous studies have extensively covered explicit incivility, this study goes further by analyzing implicit incivility and comparing both forms of uncivil discourse in a less-studied context. Methodologically, the study developed a Cantonese dictionary to differentiate between two types of incivility, providing a practical reference for more nuanced analyses. By revealing how varying movement norms moderate the interplay between deliberative and uncivil expressions, the study drew attention to the highly situational nature of incivility.</p><!--/ Abstract__block -->","PeriodicalId":54925,"journal":{"name":"Internet Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Problem of Plenty? Understanding the Impact of Choice and Information Overload on Airbnb Bookings","authors":"V Athi Karthick, Adrija Majumdar, Indranil Bose","doi":"10.1007/s10796-024-10548-0","DOIUrl":"https://doi.org/10.1007/s10796-024-10548-0","url":null,"abstract":"<p>Online sharing platforms offer countless choices and detailed product descriptions to consumers. In this study, we demonstrate the effect of choice and information overload on booking decisions using large-scale field data from Airbnb and observe an inverse U-shaped association. Furthermore, our results show that providing quality assurance of the product exacerbates the choice and information overload relationship. As a post-hoc analysis, we perform topic modeling to gain better insights into how product information influences booking decisions. Specifically, the post-hoc analyses show that the number of topics in the description has a positive association with the number of bookings. Furthermore, topic count moderates the information overload effect by intensifying the influence of product description on the number of bookings. Our findings have important implications for online sharing platforms, service providers, and travelers as they shed light on the detrimental effects of excessive variety and information on booking decisions.</p>","PeriodicalId":13610,"journal":{"name":"Information Systems Frontiers","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CogCartoon: Towards Practical Story Visualization","authors":"Zhongyang Zhu, Jie Tang","doi":"10.1007/s11263-024-02267-5","DOIUrl":"https://doi.org/10.1007/s11263-024-02267-5","url":null,"abstract":"<p>The state-of-the-art methods for story visualization demonstrate a significant demand for training data and storage, as well as limited flexibility in story presentation, thereby rendering them impractical for real-world applications. We introduce CogCartoon, a practical story visualization method based on pre-trained diffusion models. To alleviate dependence on data and storage, we propose an innovative strategy of character-plugin generation that can represent a specific character as a compact 316 KB plugin by using a few training samples. To facilitate enhanced flexibility, we employ a strategy of plugin-guided and layout-guided inference, enabling users to seamlessly incorporate new characters and custom layouts into the generated image results at their convenience. We have conducted comprehensive qualitative and quantitative studies, providing compelling evidence for the superiority of CogCartoon over existing methodologies. Moreover, CogCartoon demonstrates its power in tackling challenging tasks, including long story visualization and realistic style story visualization.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":null,"pages":null},"PeriodicalIF":19.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging language model for advanced multiproperty molecular optimization via prompt engineering","authors":"Zhenxing Wu, Odin Zhang, Xiaorui Wang, Li Fu, Huifeng Zhao, Jike Wang, Hongyan Du, Dejun Jiang, Yafeng Deng, Dongsheng Cao, Chang-Yu Hsieh, Tingjun Hou","doi":"10.1038/s42256-024-00916-5","DOIUrl":"https://doi.org/10.1038/s42256-024-00916-5","url":null,"abstract":"<p>Optimizing a candidate molecule’s physiochemical and functional properties has been a critical task in drug and material design. Although the non-trivial task of balancing multiple (potentially conflicting) optimization objectives is considered ideal for artificial intelligence, several technical challenges such as the scarcity of multiproperty-labelled training data have hindered the development of a satisfactory AI solution for a long time. Prompt-MolOpt is a tool for molecular optimization; it makes use of prompt-based embeddings, as used in large language models, to improve the transformer’s ability to optimize molecules for specific property adjustments. Notably, Prompt-MolOpt excels in working with limited multiproperty data (even under the zero-shot setting) by effectively generalizing causal relationships learned from single-property datasets. In comparative evaluations against established models such as JTNN, hierG2G and Modof, Prompt-MolOpt achieves over a 15% relative improvement in multiproperty optimization success rates compared with the leading Modof model. Furthermore, a variant of Prompt-MolOpt, named Prompt-MolOpt^P, can preserve the pharmacophores or any user-specified fragments under the structural transformation, further broadening its application scope. By constructing tailored optimization datasets, with the protocol introduced in this work, Prompt-MolOpt steers molecular optimization towards domain-relevant chemical spaces, enhancing the quality of the optimized molecules. Real-world tests, such as those involving blood–brain barrier permeability optimization, underscore its practical relevance. Prompt-MolOpt offers a versatile approach for multiproperty and multi-site molecular optimizations, suggesting its potential utility in chemistry research and drug and material discovery.</p>","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interweaving Insights: High-Order Feature Interaction for Fine-Grained Visual Recognition","authors":"Arindam Sikdar, Yonghuai Liu, Siddhardha Kedarisetty, Yitian Zhao, Amr Ahmed, Ardhendu Behera","doi":"10.1007/s11263-024-02260-y","DOIUrl":"https://doi.org/10.1007/s11263-024-02260-y","url":null,"abstract":"<p>This paper presents a novel approach for Fine-Grained Visual Classification (FGVC) by exploring Graph Neural Networks (GNNs) to facilitate high-order feature interactions, with a specific focus on constructing both inter- and intra-region graphs. Unlike previous FGVC techniques that often isolate global and local features, our method combines both features seamlessly during learning via graphs. Inter-region graphs capture long-range dependencies to recognize global patterns, while intra-region graphs delve into finer details within specific regions of an object by exploring high-dimensional convolutional features. A key innovation is the use of shared GNNs with an attention mechanism coupled with the Approximate Personalized Propagation of Neural Predictions (APPNP) message-passing algorithm, enhancing information propagation efficiency for better discriminability and simplifying the model architecture for computational efficiency. Additionally, the introduction of residual connections improves performance and training stability. Comprehensive experiments showcase state-of-the-art results on benchmark FGVC datasets, affirming the efficacy of our approach. This work underscores the potential of GNN in modeling high-level feature interactions, distinguishing it from previous FGVC methods that typically focus on singular aspects of feature representation. Our source code is available at https://github.com/Arindam-1991/I2-HOFI.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":null,"pages":null},"PeriodicalIF":19.5,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tire wear monitoring using feature fusion and CatBoost classifier","authors":"C. V. Prasshanth,&nbsp;V. Sugumaran","doi":"10.1007/s10462-024-10999-6","DOIUrl":"10.1007/s10462-024-10999-6","url":null,"abstract":"<div><p>Addressing the critical issue of tire wear is essential for enhancing vehicle safety, performance, and maintenance. Worn-out tires often lead to accidents, underscoring the need for effective monitoring systems. This study is vital for several reasons: safety, as worn tires increase the risk of accidents due to reduced traction and longer braking distances; performance, as uneven tire wear affects vehicle handling and fuel efficiency; maintenance costs, as early detection can prevent more severe damage to suspension and alignment systems; and regulatory compliance, as ensuring tire integrity helps meet safety regulations imposed by transportation authorities. In response, this study systematically evaluates tire conditions at 25%, 50%, 75%, and 100% wear, with an intact tire as a reference, using vibration signals as the primary data source. The analysis employs statistical, histogram, and autoregressive–moving-average (ARMA) feature extraction techniques, followed by feature selection to identify key parameters influencing tire wear. CatBoost is used for feature classification, leveraging its adaptability and efficiency in distinguishing varying wear patterns. Additionally, the study incorporates feature fusion to combine different types of features for a more comprehensive analysis. The proposed methodology not only offers a robust framework for accurately classifying tire wear levels but also holds significant potential for real-time implementation, contributing to proactive maintenance practices, prolonged tire lifespan, and overall vehicular safety.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":null,"pages":null},"PeriodicalIF":10.7,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10999-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}