CAAI Transactions on Intelligence Technology最新文献

{"title":"Guest Editorial: Special issue on trustworthy machine learning for behavioural and social computing","authors":"Zhi-Hui Zhan, Jianxin Li, Xuyun Zhang, Deepak Puthal","doi":"10.1049/cit2.12353","DOIUrl":"https://doi.org/10.1049/cit2.12353","url":null,"abstract":"<p>Machine learning has been extensively applied in behavioural and social computing, encompassing a spectrum of applications such as social network analysis, click stream analysis, recommendation of points of interest, and sentiment analysis. The datasets pertinent to these applications are inherently linked to human behaviour and societal dynamics, posing a risk of disclosing personal or sensitive information if mishandled or subjected to attacks. To safeguard individuals from potential privacy breaches, numerous governments have enacted a range of legal frameworks and regulatory measures. Examples include the Personal Information Protection Law of the People's Republic of China, the European Union's GDPR for privacy, and Australia's Artificial Intelligence Ethics Framework for many ethical aspects like fairness and reliability. Despite these legislative efforts, the technical implementation of these regulations to ensure trustworthy machine learning in behavioural and social computing remains a significant challenge. Trustworthy machine learning, being a fast-developing field, necessitates further in-depth exploration across multiple dimensions, including but not limited to fairness, privacy, reliability, explainability, robustness, and security, from a holistic and interdisciplinary viewpoint. This special issue is dedicated to facilitating the exchange and discussion of state-of-the-art research findings from academia and industry alike. The seven high-quality papers collected in this special issue place a particular emphasis on showcasing the latest advancements in concepts, algorithms, systems, platforms, and applications, as well as exploring future trends pertinent to the field of trustworthy machine learning for behavioural and social computing.</p><p>In the first paper, ‘Trustworthy semi-supervised anomaly detection for online-to-offline logistics business in merchant identification’, Yong Li et al. have developed a semi-supervised framework for the detection of anomalous merchants within the logistics sector. The methodology begins with an extensive data-driven examination comparing the behaviours of regular and anomalous customers. Utilising the insights from this analysis, the authors then implemented a contrastive learning for data augmentation, which capitalises on the imprecise labelling of customer data. Subsequently, their model is employed to identify customers exhibiting abnormal package reception and dispatch patterns in logistics operations. The framework's efficacy is substantiated by an empirical study that leverages 8 months of authentic order data, sourced from Beijing and provided by one of China's foremost logistics corporations.</p><p>The second paper, entitled ‘Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs’ by Sandra Carrasco Limeros et al., is advancing toward the creation of dependable motion prediction models, with a focus on the evaluation, robustness, and","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 3","pages":"541-543"},"PeriodicalIF":5.1,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430192","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}

{"title":"4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome","authors":"Gang Wang,&nbsp;Weisheng Li,&nbsp;Mingliang Zhou,&nbsp;Haobo Zhu,&nbsp;Guang Yang,&nbsp;Choon Hwai Yap","doi":"10.1049/cit2.12354","DOIUrl":"10.1049/cit2.12354","url":null,"abstract":"<p>Hypoplastic left heart syndrome (HLHS) is a rare, complex, and incredibly foetal congenital heart disease. To decrease neonatal mortality, evolving HLHS (eHLHS) in pregnant women should be critically diagnosed as soon as possible. However, diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images, making it difficult to rapidly and easily examine for this disease. Herein, the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS (RDeH), which we have named RDeH-Net. Briefly, the framework implements a coarse-to-fine two-stage detection approach, with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains, and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician assistance. The experiments extensively compare the authors’ network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits: (1) it achieved superior average accuracy of 99.37% on three categories of foetal ultrasound images from different cases; (2) it demonstrates visually fine detection performance with weakly supervised localisation. This framework could be used to accelerate the diagnosis of eHLHS, and hence significantly lessen reliance on experienced medical physicians.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1485-1499"},"PeriodicalIF":8.4,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141373007","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}

{"title":"A fault-tolerant and scalable boosting method over vertically partitioned data","authors":"Hai Jiang,&nbsp;Songtao Shang,&nbsp;Peng Liu,&nbsp;Tong Yi","doi":"10.1049/cit2.12339","DOIUrl":"10.1049/cit2.12339","url":null,"abstract":"<p>Vertical federated learning (VFL) can learn a common machine learning model over vertically partitioned datasets. However, VFL are faced with these thorny problems: (1) both the training and prediction are very vulnerable to stragglers; (2) most VFL methods can only support a specific machine learning model. Suppose that VFL incorporates the features of centralised learning, then the above issues can be alleviated. With that in mind, this paper proposes a new VFL scheme, called FedBoost, which makes private parties upload the compressed partial order relations to the honest but curious server before training and prediction. The server can build a machine learning model and predict samples on the union of coded data. The theoretical analysis indicates that the absence of any private party will not affect the training and prediction as long as a round of communication is achieved. Our scheme can support canonical tree-based models such as Tree Boosting methods and Random Forests. The experimental results also demonstrate the availability of our scheme.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 5","pages":"1092-1100"},"PeriodicalIF":8.4,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141384583","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}

{"title":"WaveSeg-UNet model for overlapped nuclei segmentation from multi-organ histopathology images","authors":"Hameed Ullah Khan,&nbsp;Basit Raza,&nbsp;Muhammad Asad Iqbal Khan,&nbsp;Muhammad Faheem","doi":"10.1049/cit2.12351","DOIUrl":"10.1049/cit2.12351","url":null,"abstract":"<p>Nuclei segmentation is a challenging task in histopathology images. It is challenging due to the small size of objects, low contrast, touching boundaries, and complex structure of nuclei. Their segmentation and counting play an important role in cancer identification and its grading. In this study, WaveSeg-UNet, a lightweight model, is introduced to segment cancerous nuclei having touching boundaries. Residual blocks are used for feature extraction. Only one feature extractor block is used in each level of the encoder and decoder. Normally, images degrade quality and lose important information during down-sampling. To overcome this loss, discrete wavelet transform (DWT) alongside max-pooling is used in the down-sampling process. Inverse DWT is used to regenerate original images during up-sampling. In the bottleneck of the proposed model, atrous spatial channel pyramid pooling (ASCPP) is used to extract effective high-level features. The ASCPP is the modified pyramid pooling having atrous layers to increase the area of the receptive field. Spatial and channel-based attention are used to focus on the location and class of the identified objects. Finally, watershed transform is used as a post processing technique to identify and refine touching boundaries of nuclei. Nuclei are identified and counted to facilitate pathologists. The same domain of transfer learning is used to retrain the model for domain adaptability. Results of the proposed model are compared with state-of-the-art models, and it outperformed the existing studies.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 1","pages":"253-267"},"PeriodicalIF":8.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141269985","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}

{"title":"Multi-objective interval type-2 fuzzy linear programming problem with vagueness in coefficient","authors":"Shokouh Sargolzaei,&nbsp;Hassan Mishmast Nehi","doi":"10.1049/cit2.12336","DOIUrl":"10.1049/cit2.12336","url":null,"abstract":"<p>One of the most widely used fuzzy linear programming models is the multi-objective interval type-2 fuzzy linear programming (IT2FLP) model, which is of particular importance due to the simultaneous integration of multiple criteria and objectives in a single problem, the fuzzy nature of this type of problems, and thus, its closer similarity to real-world problems. So far, many studies have been done for the IT2FLP problem with uncertainties of the vagueness type. However, not enough studies have been done regarding the multi-objective interval type-2 fuzzy linear programming (MOIT2FLP) problem with uncertainties of the vagueness type. As an innovation, this study investigates the MOIT2FLP problem with vagueness-type uncertainties, which are represented by membership functions (MFs) in the problem. Depending on the localisation of vagueness in the problem, that is, vagueness in the objective function vector, vagueness in the technological coefficients, vagueness in the resources vector, and any possible combination of them, various problems may arise. Furthermore, to solve problems with MOIT2FLP, first, using the weighted sum method as an efficient and effective method, each of the MOIT2FLP problems is converted into a single-objective problem. In this research, these types of problems are introduced, their MFs are stated, and different solution methods are suggested. For each of the proposed methods, the authors have provided an example and presented the results in the corresponding tables.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 5","pages":"1229-1248"},"PeriodicalIF":8.4,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12336","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140983368","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}

{"title":"Prediction and optimisation of gasoline quality in petroleum refining: The use of machine learning model as a surrogate in optimisation framework","authors":"Husnain Saghir,&nbsp;Iftikhar Ahmad,&nbsp;Manabu Kano,&nbsp;Hakan Caliskan,&nbsp;Hiki Hong","doi":"10.1049/cit2.12324","DOIUrl":"10.1049/cit2.12324","url":null,"abstract":"<p>Hardware-based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes. A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of ±5% in process conditions, such as temperature, flow rates, etc. The generated data was used to train support vector machines (SVM), Gaussian process regression (GPR), artificial neural networks (ANN), regression trees (RT), and ensemble trees (ET). Hyperparameter tuning was performed to enhance the prediction capabilities of GPR, ANN, SVM, ET and RT models. Performance analysis of the models indicates that GPR, ANN, and SVM with <i>R</i>² values of 0.99, 0.978, and 0.979 and RMSE values of 0.108, 0.262, and 0.258, respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables. ET and RT had an <i>R</i>² value of 0.94 and 0.89, respectively. The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method. Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%. The proposed methodology of surrogate-based optimisation will provide a platform for plant-level implementation to realise the concept of industry 4.0 in the refinery.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 5","pages":"1185-1198"},"PeriodicalIF":8.4,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140983874","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}

{"title":"Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm","authors":"Jinduo Liu,&nbsp;Jihao Zhai,&nbsp;Junzhong Ji","doi":"10.1049/cit2.12344","DOIUrl":"10.1049/cit2.12344","url":null,"abstract":"<p>Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi-parameter fluorescence-activated cell sorter dataset, indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1587-1604"},"PeriodicalIF":8.4,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12344","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140989528","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}

{"title":"Residual multimodal Transformer for expression-EEG fusion continuous emotion recognition","authors":"Xiaofang Jin,&nbsp;Jieyu Xiao,&nbsp;Libiao Jin,&nbsp;Xinruo Zhang","doi":"10.1049/cit2.12346","DOIUrl":"10.1049/cit2.12346","url":null,"abstract":"<p>Continuous emotion recognition is to predict emotion states through affective information and more focus on the continuous variation of emotion. Fusion of electroencephalography (EEG) and facial expressions videos has been used in this field, while there are with some limitations in current researches, such as hand-engineered features, simple approaches to integration. Hence, a new continuous emotion recognition model is proposed based on the fusion of EEG and facial expressions videos named residual multimodal Transformer (RMMT). Firstly, the Resnet50 and temporal convolutional network (TCN) are utilised to extract spatiotemporal features from videos, and the TCN is also applied to process the computed EEG frequency power to acquire spatiotemporal features of EEG. Then, a multimodal Transformer is used to fuse the spatiotemporal features from the two modalities. Furthermore, a residual connection is introduced to fuse shallow features with deep features which is verified to be effective for continuous emotion recognition through experiments. Inspired by knowledge distillation, the authors incorporate feature-level loss into the loss function to further enhance the network performance. Experimental results show that the RMMT reaches a superior performance over other methods for the MAHNOB-HCI dataset. Ablation studies on the residual connection and loss function in the RMMT demonstrate that both of them is functional.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 5","pages":"1290-1304"},"PeriodicalIF":8.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141000683","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}

{"title":"Join multiple Riemannian manifold representation and multi-kernel non-redundancy for image clustering","authors":"Mengyuan Zhang,&nbsp;Jinglei Liu","doi":"10.1049/cit2.12347","DOIUrl":"10.1049/cit2.12347","url":null,"abstract":"<p>Image clustering has received significant attention due to the growing importance of image recognition. Researchers have explored Riemannian manifold clustering, which is capable of capturing the non-linear shapes found in real-world datasets. However, the complexity of image data poses substantial challenges for modelling and feature extraction. Traditional methods such as covariance matrices and linear subspace have shown promise in image modelling, and they are still in their early stages and suffer from certain limitations. However, these include the uncertainty of representing data using only one Riemannian manifold, limited feature extraction capacity of single kernel functions, and resulting incomplete data representation and redundancy. To overcome these limitations, the authors propose a novel approach called join multiple Riemannian manifold representation and multi-kernel non-redundancy for image clustering (MRMNR-MKC). It combines covariance matrices with linear subspace to represent data and applies multiple kernel functions to map the non-linear structural data into a reproducing kernel Hilbert space, enabling linear model analysis for image clustering. Additionally, the authors use matrix-induced regularisation to improve the clustering kernel selection process by reducing redundancy and assigning lower weights to identical kernels. Finally, the authors also conducted numerous experiments to evaluate the performance of our approach, confirming its superiority to state-of-the-art methods on three benchmark datasets.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 5","pages":"1305-1319"},"PeriodicalIF":8.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140999031","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}

{"title":"DeepGCN based on variable multi-graph and multimodal data for ASD diagnosis","authors":"Shuaiqi Liu,&nbsp;Siqi Wang,&nbsp;Chaolei Sun,&nbsp;Bing Li,&nbsp;Shuihua Wang,&nbsp;Fei Li","doi":"10.1049/cit2.12340","DOIUrl":"10.1049/cit2.12340","url":null,"abstract":"<p>Diagnosing individuals with autism spectrum disorder (ASD) accurately faces great challenges in clinical practice, primarily due to the data's high heterogeneity and limited sample size. To tackle this issue, the authors constructed a deep graph convolutional network (GCN) based on variable multi-graph and multimodal data (VMM-DGCN) for ASD diagnosis. Firstly, the functional connectivity matrix was constructed to extract primary features. Then, the authors constructed a variable multi-graph construction strategy to capture the multi-scale feature representations of each subject by utilising convolutional filters with varying kernel sizes. Furthermore, the authors brought the non-imaging information into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between subjects. After extracting the deeper features of population graphs using the deep GCN(DeepGCN), the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD patients. The proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I (ABIDE I) dataset, achieving an accuracy of 91.62% and an area under the curve value of 95.74%. These results demonstrated its outstanding performance compared to other ASD diagnostic algorithms.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 4","pages":"879-893"},"PeriodicalIF":8.4,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12340","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017242","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}