计算机科学最新文献

{"title":"Reinforcement learning for online testing of autonomous driving systems: a replication and extension study.","authors":"Luca Giamattei, Matteo Biagiola, Roberto Pietrantuono, Stefano Russo, Paolo Tonella","doi":"10.1007/s10664-024-10562-5","DOIUrl":"https://doi.org/10.1007/s10664-024-10562-5","url":null,"abstract":"<p><p>In a recent study, Reinforcement Learning (RL) used in combination with many-objective search, has been shown to outperform alternative techniques (random search and many-objective search) for online testing of Deep Neural Network-enabled systems. The empirical evaluation of these techniques was conducted on a state-of-the-art Autonomous Driving System (ADS). This work is a replication and extension of that empirical study. Our replication shows that RL does not outperform pure random test generation in a comparison conducted under the same settings of the original study, but with no confounding factor coming from the way collisions are measured. Our extension aims at eliminating some of the possible reasons for the poor performance of RL observed in our replication: (1) the presence of reward components providing contrasting feedback to the RL agent; (2) the usage of an RL algorithm (Q-learning) which requires discretization of an intrinsically continuous state space. Results show that our new RL agent is able to converge to an effective policy that outperforms random search. Results also highlight other possible improvements, which open to further investigations on how to best leverage RL for online ADS testing.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":"30 1","pages":"19"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11538197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142602130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of data complexity on classifier performance.","authors":"Jonas Eberlein, Daniel Rodriguez, Rachel Harrison","doi":"10.1007/s10664-024-10554-5","DOIUrl":"10.1007/s10664-024-10554-5","url":null,"abstract":"<p><p>The research area of Software Defect Prediction (SDP) is both extensive and popular, and is often treated as a classification problem. Improvements in classification, pre-processing and tuning techniques, (together with many factors which can influence model performance) have encouraged this trend. However, no matter the effort in these areas, it seems that there is a ceiling in the performance of the classification models used in SDP. In this paper, the issue of classifier performance is analysed from the perspective of data complexity. Specifically, data complexity metrics are calculated using the Unified Bug Dataset, a collection of well-known SDP datasets, and then checked for correlation with the defect prediction performance of machine learning classifiers (in particular, the classifiers C5.0, Naive Bayes, Artificial Neural Networks, Random Forests, and Support Vector Machines). In this work, different domains of competence and incompetence are identified for the classifiers. Similarities and differences between the classifiers and the performance metrics are found and the Unified Bug Dataset is analysed from the perspective of data complexity. We found that certain classifiers work best in certain situations and that all data complexity metrics can be problematic, although certain classifiers did excel in some situations.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":"30 1","pages":"16"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral integrated neural networks (SINNs) for solving forward and inverse dynamic problems.","authors":"Lin Qiu, Fajie Wang, Wenzhen Qu, Yan Gu, Qing-Hua Qin","doi":"10.1016/j.neunet.2024.106756","DOIUrl":"10.1016/j.neunet.2024.106756","url":null,"abstract":"<p><p>This study introduces an innovative neural network framework named spectral integrated neural networks (SINNs) to address both forward and inverse dynamic problems in three-dimensional space. In the SINNs, the spectral integration technique is utilized for temporal discretization, followed by the application of a fully connected neural network to solve the resulting partial differential equations in the spatial domain. Furthermore, the polynomial basis functions are employed to expand the unknown function, with the goal of improving the performance of SINNs in tackling inverse problems. The performance of the developed framework is evaluated through several dynamic benchmark examples encompassing linear and nonlinear heat conduction problems, linear and nonlinear wave propagation problems, inverse problem of heat conduction, and long-time heat conduction problem. The numerical results demonstrate that the SINNs can effectively and accurately solve forward and inverse problems involving heat conduction and wave propagation. Additionally, the SINNs provide precise and stable solutions for dynamic problems with extended time durations. Compared to commonly used physics-informed neural networks, the SINNs exhibit superior performance with enhanced convergence speed, computational accuracy, and efficiency.</p>","PeriodicalId":6,"journal":{"name":"ACS Applied Nano Materials","volume":"180 ","pages":"106756"},"PeriodicalIF":6.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-source Selective Graph Domain Adaptation Network for cross-subject EEG emotion recognition.","authors":"Jing Wang, Xiaojun Ning, Wei Xu, Yunze Li, Ziyu Jia, Youfang Lin","doi":"10.1016/j.neunet.2024.106742","DOIUrl":"10.1016/j.neunet.2024.106742","url":null,"abstract":"<p><p>Affective brain-computer interface is an important part of realizing emotional human-computer interaction. However, existing objective individual differences among subjects significantly hinder the application of electroencephalography (EEG) emotion recognition. Existing methods still lack the complete extraction of subject-invariant representations for EEG and the ability to fuse valuable information from multiple subjects to facilitate the emotion recognition of the target subject. To address the above challenges, we propose a Multi-source Selective Graph Domain Adaptation Network (MSGDAN), which can better utilize data from different source subjects and perform more robust emotion recognition on the target subject. The proposed network extracts and selects the individual information specific to each subject, where public information refers to subject-invariant components from multi-source subjects. Moreover, the graph domain adaptation network captures both functional connectivity and regional states of the brain via a dynamic graph network and then integrates graph domain adaptation to ensure the invariance of both functional connectivity and regional states. To evaluate our method, we conduct cross-subject emotion recognition experiments on the SEED, SEED-IV, and DEAP datasets. The results demonstrate that the MSGDAN has superior classification performance.</p>","PeriodicalId":6,"journal":{"name":"ACS Applied Nano Materials","volume":"180 ","pages":"106742"},"PeriodicalIF":6.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational analysis of variability and uncertainty in the clinical reference on magnetic resonance imaging radiomics: modelling and performance.","authors":"Cindy Xue, Jing Yuan, Gladys G Lo, Darren M C Poon, Winnie Cw Chu","doi":"10.1186/s42492-024-00180-9","DOIUrl":"https://doi.org/10.1186/s42492-024-00180-9","url":null,"abstract":"<p><p>To conduct a computational investigation to explore the influence of clinical reference uncertainty on magnetic resonance imaging (MRI) radiomics feature selection, modelling, and performance. This study used two sets of publicly available prostate cancer MRI = radiomics data (Dataset 1: n = 260; Dataset 2: n = 100) with Gleason score clinical references. Each dataset was divided into training and holdout testing datasets at a ratio of 7:3 and analysed independently. The clinical references of the training set were permuted at different levels (increments of 5%) and repeated 20 times. Four feature selection algorithms and two classifiers were used to construct the models. Cross-validation was employed for training, while a separate hold-out testing set was used for evaluation. The Jaccard similarity coefficient was used to evaluate feature selection, while the area under the curve (AUC) and accuracy were used to assess model performance. An analysis of variance test with Bonferroni correction was conducted to compare the metrics of each model. The consistency of the feature selection performance decreased substantially with the clinical reference permutation. AUCs of the trained models with permutation particularly after 20% were significantly lower (Dataset 1 (with ≥ 20% permutation): 0.67, and Dataset 2 (≥ 20% permutation): 0.74), compared to the AUC of models without permutation (Dataset 1: 0.94, Dataset 2: 0.97). The performances of the models were also associated with larger uncertainties and an increasing number of permuted clinical references. Clinical reference uncertainty can substantially influence MRI radiomic feature selection and modelling. The high accuracy of clinical references should be helpful in building reliable and robust radiomic models. Careful interpretation of the model performance is necessary, particularly for high-dimensional data.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"7 1","pages":"28"},"PeriodicalIF":3.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"“I am sorry for judging you”: conceptualizing sentiment reversal among followers in case of falsely alleged social media influencer transgression","authors":"Ishaan Sengupta, Kokil Jain, Arpan Kumar Kar, Isha Sharma","doi":"10.1108/intr-08-2023-0649","DOIUrl":"https://doi.org/10.1108/intr-08-2023-0649","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Influencer transgressions can disappoint their followers. However, there is a lack of clarity about the effects of a false allegation on an influencer–follower relationship. Drawing from cognitive dissonance and moral reasoning theory, the current study aims to examine how this relationship is shaped across three time periods (before the allegation is leveled, after the allegation is leveled, and when the allegation is found to be baseless).</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>We study comments posted by followers of two falsely alleged social media influencers (SMI) on their YouTube and Instagram channels. Latent Dirichlet allocation (LDA) followed by netnography is used for thematic analysis. LDA is a social media topic modeling method that processes a statistically representative set of words to explain the tone and tenor of qualitative conversations. A sentiment analysis of the comments is done using SentiStrength.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>When an allegation is leveled initially, the response from followers is overwhelmingly negative toward the influencer owing to moral coupling. However, when the allegations are proven to be false, the followers return to a positive opinion of the influencer, owing to feelings of dissonance and guilt.</p><!--/ Abstract__block -->\u0000<h3>Practical implications</h3>\u0000<p>The study contributes to the fields of influencer marketing, cognitive dissonance and moral reasoning. It highlights how endorsers can take advantage of the positive sentiment that arises once an accused SMI’s transgression is proven false.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This study introduces the concept of “Sentiment Reversal,” which is exhibited in the social media space. In this phenomenon, sentiments move from negative to positive toward the falsely accused SMI as they are vindicated of the previous charge.</p><!--/ Abstract__block -->","PeriodicalId":54925,"journal":{"name":"Internet Research","volume":"17 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665536","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":"Towards user-specific multimodal recommendation via cross-modal attention-enhanced graph convolution network","authors":"Ruidong Wang,&nbsp;Chao Li,&nbsp;Zhongying Zhao","doi":"10.1007/s10489-024-06061-1","DOIUrl":"10.1007/s10489-024-06061-1","url":null,"abstract":"<div><p>Multimodal Recommendation (MR) exploits multimodal features of items (e.g., visual or textual features) to provide personalized recommendations for users. Recently, scholars have integrated Graph Convolutional Networks (GCN) into MR to model complicated multimodal relationships, but still with two significant challenges: (1) Most MR methods fail to consider the correlations between different modalities, which significantly affects the modal alignment, resulting in poor performance on MR tasks. (2) Most MR methods leverage multimodal features to enhance item representation learning. However, the connection between multimodal features and user representations remains largely unexplored. To this end, we propose a novel yet effective Cross-modal Attention-enhanced graph convolution network for user-specific Multimodal Recommendation, named CAMR. Specifically, we design a cross-modal attention mechanism to mine the cross-modal correlations. In addition, we devise a modality-aware user feature learning method that uses rich item information to learn user feature representations. Experimental results on four real-world datasets demonstrate the superiority of CAMR compared with several state-of-the-art methods. The codes of this work are available at https://github.com/ZZY-GraphMiningLab/CAMR</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664487","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":"Calibrating TabTransformer for financial misstatement detection","authors":"Elias Zavitsanos,&nbsp;Dimitrios Kelesis,&nbsp;Georgios Paliouras","doi":"10.1007/s10489-024-05861-9","DOIUrl":"10.1007/s10489-024-05861-9","url":null,"abstract":"<div><p>In this paper, we deal with the task of identifying the probability of misstatements in the annual financial reports of public companies. In particular, we improve the state-of-the-art for financial misstatement detection by training a TabTransformer model with a gated multi-layer perceptron, which encodes and exploits relationships between financial features. We further calibrate a sample-dependent focal loss function to deal with the severe class imbalance in the data and to focus on positive examples that are hard to distinguish. We evaluate the proposed methodology in a realistic setting that preserves the essential characteristics of the task: (a) the imbalanced distribution of classes in the data, (b) the chronological order of data, and (c) the systematic noise in the labels, due to the delay in manually identifying misstatements. The proposed method achieves state-of-the-art results in this setting, compared to recent approaches in the literature. As an additional contribution, we release the dataset to facilitate further research in the field.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664419","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":"Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis","authors":"Haoyu Wang, Xihe Qiu, Bin Li, Xiaoyu Tan, Jingjing Huang","doi":"10.1007/s40747-024-01648-0","DOIUrl":"https://doi.org/10.1007/s40747-024-01648-0","url":null,"abstract":"<p>Polysomnography is the diagnostic gold standard for obstructive sleep apnea-hypopnea syndrome (OSAHS), requiring medical professionals to analyze apnea-hypopnea events from multidimensional data throughout the sleep cycle. This complex process is susceptible to variability based on the clinician’s experience, leading to potential inaccuracies. Existing automatic diagnosis methods often overlook multimodal physiological signals and medical prior knowledge, leading to limited diagnostic capabilities. This study presents a novel <b>hetero</b>geneous <b>g</b>raph <b>c</b>onvolutional <b>f</b>usion <b>net</b>work (<b>HeteroGCFNet</b>) leveraging multimodal physiological signals and domain knowledge for automated OSAHS diagnosis. This framework constructs two types of graph representations: physical space graphs, which map the spatial layout of sensors on the human body, and process knowledge graphs which detail the physiological relationships among breathing patterns, oxygen saturation, and vital signals. The framework leverages heterogeneous graph convolutional neural networks to extract both localized and global features from these graphs. Additionally, a multi-head fusion module combines these features into a unified representation for effective classification, enhancing focus on relevant signal characteristics and cross-modal interactions. This study evaluated the proposed framework on a large-scale OSAHS dataset, combined from publicly available sources and data provided by a collaborative university hospital. It demonstrated superior diagnostic performance compared to conventional machine learning models and existing deep learning approaches, effectively integrating domain knowledge with data-driven learning to produce explainable representations and robust generalization capabilities, which can potentially be utilized for clinical use. Code is available at https://github.com/AmbitYuki/HeteroGCFNet.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"17 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670365","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":"Survey of real-time brainmedia in artistic exploration.","authors":"Rem RunGu Lin, Kang Zhang","doi":"10.1186/s42492-024-00179-2","DOIUrl":"10.1186/s42492-024-00179-2","url":null,"abstract":"<p><p>This survey examines the evolution and impact of real-time brainmedia on artistic exploration, contextualizing developments within a historical framework. To enhance knowledge on the entanglement between the brain, mind, and body in an increasingly mediated world, this work defines a clear scope at the intersection of bio art and interactive art, concentrating on real-time brainmedia artworks developed in the 21st century. It proposes a set of criteria and a taxonomy based on historical notions, interaction dynamics, and media art representations. The goal is to provide a comprehensive overview of real-time brainmedia, setting the stage for future explorations of new paradigms in communication between humans, machines, and the environment.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"7 1","pages":"27"},"PeriodicalIF":3.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}