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Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing.
In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.
Papers published in this journal are geared heavily towards applications, with an anticipated split of 70% of the papers published being applications-oriented, research and the remaining 30% containing more theoretical research. Manuscripts should be submitted in *.pdf format only. Please prepare your manuscripts in single space, and include figures and tables in the body of the text where they are referred to. For all enquiries regarding the submission of your manuscript please contact the IDA journal editor: [email protected]
Authors: Zhang, Xu | Xiang, Yanzheng | Liu, Zejie | Hu, Xiaoyu | Zhou, Deyu
Article Type: Research Article
Abstract: Code search, which locates code snippets in large code repositories based on natural language queries entered by developers, has become increasingly popular in the software development process. It has the potential to improve the efficiency of software developers. Recent studies have demonstrated the effectiveness of using deep learning techniques to represent queries and codes accurately for code search. In specific, pre-trained models of programming languages have recently achieved significant progress in code searching. However, we argue that aligning programming and natural languages are crucial as there are two different modalities. Existing pre-train models based approaches for code search do not …effectively consider implicit alignments of representations across modalities (inter-modal representation). Moreover, the existing methods do not take into account the consistency constraint of intra-modal representations, making the model ineffective. As a result, we propose a novel code search method that optimizes both intra-modal and inter-modal representation learning. The alignment of the representation between the two modalities is achieved by introducing contrastive learning. Furthermore, the consistency of intra-modal feature representation is constrained by KL-divergence. Our experimental results confirm the model’s effectiveness on seven different test datasets. This paper proposes a code search method that significantly improves existing methods. Our source code is publicly available on GitHub. 1 Show more
Keywords: Code search, semantic alignment, semantic representations, contrastive learning, pre-trained models
DOI: 10.3233/IDA-230082
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-17, 2023
Authors: Tian, Qing | Zhang, Heng
Article Type: Research Article
Abstract: Nowadays, the idea of active learning is gradually adopted to assist domain adaptation. However, due to the existence of domain shift, the traditional active learning methods originating from semi-supervised scenarios can not be directly applied to domain adaptation. To solve the problem, active domain adaptation is proposed as a new domain adaptation paradigm, which aims to improve the performance of the model by annotating a small amount of target domain samples. In this regard, we propose an active domain adaptation method named Boosting Active Domain Adaptation with Exploration of Samples (BADA), dividing Active DA into two related issues: sample selection …and sample utilization. We design the instability selection criterion based on predictive consistency and the diversity selection criterion. For the remaining unlabeled samples, we design a self-training framework, which screens out reliable samples and unreliable samples through the sample screening mechanism similar to selection criteria. And we adopt respective loss functions for reliable samples and unreliable samples. Experiments show that BADA remarkably outperforms previous active learning methods and Active DA methods on several domain adaptation datasets. Show more
Keywords: Domain adaptation, active learning, active domain adaptation, self-training
DOI: 10.3233/IDA-230150
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-17, 2023
Authors: Tang, Jing | Fan, Yongquan | Du, Yajun | Li, Xianyong | Chen, Xiaoliang
Article Type: Research Article
Abstract: Recommendation systems are an effective solution to deal with information overload, particularly in the e-commerce sector, in which sequential recommendation is extensively utilized. Sequential recommendations aim to acquire users’ interests and provide accurate recommendations by analyzing users’ historical interaction sequences. To improve recommendation performance, it is vital to take into account the long- and short-term interests of users. Despite significant advancements in this domain, some issues need to be addressed. Conventional sequential recommendation models typically express each item with a uniform embedding, ignoring evolutionary patterns among item attributes, such as category, brand, and price. Moreover, these models often model users’ …long- and short-term interests independently, failing to adequately address the issues of interest drift and short-term interest evolution. This study proposes a new model, the Feature-aware Long-Short Interest Evolution Network (FLSIE), to address the above-mentioned issues. Specifically, the model uses explicit feature embedding to represent item attribute information and employs a two-dimensional (2D) attention mechanism to distinguish the significance of individual features in a specific item and the relevance of each item in the interaction sequence. Furthermore, to avoid the issue of interest drift, the model employs a long-term interest guidance mechanism to enhance the representation of short-term interest and adopts a gated recurrent unit with attentional update gate to model the dynamic evolution of users’ short-term interest. Experimental results indicate that our presented model outperforms existing methods on three real-world datasets. Show more
Keywords: Sequential recommendation, behavior sequence, feature-level preference, attention mechanism, long- and short-term interests
DOI: 10.3233/IDA-230288
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-18, 2023
Authors: Huang, Jun | Wang, Dian | Hong, Xudong | Qu, Xiwen | Xue, Wei
Article Type: Research Article
Abstract: Multi-label image classification aims to predict a set of labels that are present in an image. The key challenge of multi-label image classification lies in two aspects: modeling label correlations and utilizing spatial information. However, the existing approaches mainly calculate the correlation between labels according to co-occurrence among them. While the result is easily affected by the label noise and occasional co-occurrences. In addition, some works try to model the correlation between labels and spatial features, but the correlation among labels is not fully considered to model the spatial relationships among features. To address the above issues, we propose a …novel cross-modality semantic guidance-based framework for multi-label image classification, namely CMSG. First, we design a semantic-guided attention (SGA) module, which applies the label correlation matrix to guide the learning of class-specific features, which implicitly models semantic correlations among labels. Second, we design a spatial-aware attention (SAA) module to extract high-level semantic-aware spatial features based on class-specific features obtained from the SGA module. The experiments carried out on three benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art algorithms on multi-label image classification. Show more
Keywords: Multi-label image classification, label relation, cross-modality
DOI: 10.3233/IDA-230239
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-14, 2023
Authors: Fang, Zhao | Cao, Wenming
Article Type: Research Article
Abstract: Deformable medical image registration is a fundamental and critical task in medical image analysis. Recently, deep learning-based methods have rapidly developed and have shown impressive results in deformable image registration. However, existing approaches still suffer from limitations in registration accuracy or generalization performance. To address these challenges, in this paper, we propose a pure convolutional neural network module (CVTF) to implement hierarchical transformers and enhance the registration performance of medical images. CVTF has a larger convolutional kernel, providing a larger global effective receptive field, which can improve the network’s ability to capture long-range dependencies. In addition, we introduce the spatial …interaction attention (SIA) module to compute the interrelationship between the target feature pixel points and all other points in the feature map. This helps to improve the semantic understanding of the model by emphasizing important features and suppressing irrelevant ones. Based on the proposed CVTF and SIA, we construct a novel registration framework named PCTNet. We applied PCTNet to generate displacement fields and register medical images, and we conducted extensive experiments and validation on two public datasets, OASIS and LPBA40. The experimental results demonstrate the effectiveness and generality of our method, showing significant improvements in registration accuracy and generalization performance compared to existing methods. Our code has been available at https://github.com/fz852/PCTNet . Show more
Keywords: Deformable image registration, convolutional neural network, self-attention, unsupervised learning
DOI: 10.3233/IDA-230197
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-22, 2023
Authors: Dai, Zerui | Tian, Shengwei | Yu, Long | Yang, Qimeng
Article Type: Research Article
Abstract: Event extraction (EE) is an important natural language processing task. With the passage of time, many powerful and effective models for event extraction tasks have been developed. However, there has been limited research on complex overlapping event extraction. Therefore, we propose a new cascade decoding model: A Joint Learning Framework for Cascade Decoding with Multi-Feature Fusion and Conditional Enhancement for Overlapping Event Extraction. 1) In this model, we introduce a cascade decoding mechanism with multi-feature fusion to better capture the interaction between decoding layers. 2) Additionally, we introduce an enhanced conditional layer normalization (ECLN) mechanism to enhance the interaction between subtasks. …Simultaneously, the use of a cascade decoding model effectively addresses the problem of overlapping events. The model successively performs three subtasks, type detection, trigger word extraction and argument extraction. All three subtasks learned together in a framework, and a new conditional normalization mechanism is used to capture dependencies among these subtasks. The experiments are conducted using the overlapping event benchmark, FewFC dataset. The experimental evaluation demonstrates that our model achieves a higher F1 score on the overlapping event extraction task compared to the original overlapping event extraction model. Show more
Keywords: Event extraction, overlapping events, ECLN, cascade decoding
DOI: 10.3233/IDA-230284
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-16, 2023
Authors: Wu, Xiangping | Yao, Shuaiwei | Zhang, Zheng | Hu, Jun
Article Type: Research Article
Abstract: Among the problems of specifying the style and number of elements of a travel magazine, the problem of generating magazine layout by constraining text, and constraining graph layout remains a complex and unsolved problem. In this paper, we generate layouts of text satisfying constraints via GAN. Due to the complexity and variety of graph designs, we enhance the performance of the discriminator and the generator so that the layouts generated by the generator are more constrained. Add non-corresponding constraint text and real layout pairs to the discriminator to enhance the performance of the discriminator; then add a spatial attention mechanism …to the layout encoder to extract the features of the layout and generate high-quality layouts. We demonstrate that the proposed method can generate high-quality layouts of text satisfying the constraints, and we validate the effectiveness of this method through user ratings. Show more
Keywords: Layout, generative adversarial network, layout design, customization
DOI: 10.3233/IDA-230063
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Hosszú, Gábor
Article Type: Research Article
Abstract: This article explains the idea of pattern systems that develop gradually. These systems involve symbolic communication that includes symbols, syntax, and layout rules. Some pattern systems change over time, like historical scripts. The scientific study of pattern systems is called pattern evolution research, and scriptinformatics is concerned with the modelling of the evolution of scripts. The symbol series consists of symbols from a pattern system, while the graph sequence is a symbol sequence applied with a specific technology. This article describes a method for examining tested pattern systems to confirm their classification, which focuses on more ancient features. The method’s …effectiveness was tested on Rovash scripts and graph sequences. Multivariate analysis was carried out by using PAST4 software, employing principal coordinates analysis ordination and k -means clustering algorithms. Show more
Keywords: Feature selection, k-means, machine learning, principal coordinates analysis, scriptinformatics
DOI: 10.3233/IDA-230326
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-17, 2023
Authors: Zhang, Cheng | Zhong, Jianqi | Cao, Wenming | Ji, Jianhua
Article Type: Research Article
Abstract: Unsupervised action recognition based on spatiotemporal fusion feature extraction has attracted much attention in recent years. However, existing methods still have several limitations: (1) The long-term dependence relationship is not effectively extracted at the time level. (2) The high-order motion relationship between non-adjacent nodes is not effectively captured at the spatial level. (3) The model complexity is too high when the cascade layer input sequence is long, or there are many key points. To solve these problems, a Multiple Distilling-based spatial-temporal attention (MD-STA) networks is proposed in this paper. This model can extract temporal and spatial features respectively and fuse …them. Specifically, we first propose a Screening Self-attention (SSA) module; this module can find long-term dependencies in distant frames and high-order motion patterns between non-adjacent nodes in a single frame through a sparse metric on dot product pairs. Then, we propose the Frames and Keypoint-Distilling (FKD) module, which uses extraction operations to halve the input of the cascade layer to eliminate invalid key points and time frame features, thus reducing time and memory complexity. Finally, the Dim-reduction Fusion (DRF) module is proposed to reduce the dimension of existing features to further eliminate redundancy. Numerous experiments were conducted on three distinct datasets: NTU-60, NTU-120, and UWA3D, showing that MD-STA achieves state-of-the-art standards in skeleton-based unsupervised action recognition. Show more
Keywords: 3D human motion prediction, distilling, unsupervised, attention
DOI: 10.3233/IDA-230399
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-21, 2023
Authors: Xu, Jinlei | Wen, Yonghua | Huang, Shuanghong | Yu, Zhengtao
Article Type: Research Article
Abstract: Most methods for multi-domain adaptive neural machine translation (NMT) currently rely on mixing data from multiple domains in a single model to achieve multi-domain translation. However, this mixing can lead to imbalanced training data, causing the model to focus on training for the large-scale general domain while ignoring the scarce resources of specific domains, resulting in a decrease in translation performance. In this paper, we propose a multi-domain adaptive NMT method based on Domain Data Balancer (DDB) to address the problems of imbalanced data caused by simple fine-tuning. By adding DDB to the Transformer model, we adaptively learn the sampling …distribution of each group of training data, replace the maximum likelihood estimation criterion with empirical risk minimization training, and introduce a reward-based iterative update of the bilevel optimizer based on reinforcement learning. Experimental results show that the proposed method improves the baseline model by an average of 1.55 and 0.14 BLEU (Bilingual Evaluation Understudy) scores respectively in English-German and Chinese-English multi-domain NMT. Show more
Keywords: Multi-domain adaptation, machine translation, domain data balancer, empirical risk minimization
DOI: 10.3233/IDA-230155
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-14, 2023
Authors: Zhang, Chenran | Bao, Qingsen | Zhang, Feng | Li, Ping | Chen, Lei
Article Type: Research Article
Abstract: Accurate and reliable prediction of Alzheimer’s disease (AD) progression is crucial for effective interventions and treatment to delay its onset. Recently, deep learning models for AD progression achieve excellent predictive accuracy. However, their predictions lack reliability due to the non-calibration defects, that affects their recognition and acceptance. To address this issue, this paper proposes a temporal attention-aware evidential recurrent network for trustworthy prediction of AD progression. Specifically, evidential recurrent network explicitly models uncertainty of the output and converts it into a reliability measure for trustworthy AD progression prediction. Furthermore, considering that the actual scenario of AD progression prediction frequently relies …on historical longitudinal data, we introduce temporal attention into evidential recurrent network, which improves predictive performance. We demonstrate the proposed model on the TADPOLE dataset. For predictive performance, the proposed model achieves mAUC of 0.943 and BCA of 0.881, which is comparable to the SOTA model MinimalRNN. More importantly, the proposed model provides reliability measures of the predicted results through uncertainty estimation and the ECE of the method on the TADPOLE dataset is 0.101, which is much lower than the SOTA model at 0.147, indicating that the proposed model can provide important decision-making support for risk-sensitive prediction of AD progression. Show more
Keywords: Alzheimer’s disease, disease progression prediction, evidential recurrent network, temporal attention, trustworthy deep learning
DOI: 10.3233/IDA-230220
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-18, 2023
Authors: Modarres, Reza
Article Type: Research Article
Abstract: Distance or dissimilarity matrices are widely used in applications. We study the relationships between the eigenvalues of the distance matrices and outliers and show that outliers affect the pairwise distances and inflate the eigenvalues. We obtain the eigenvalues of a distance matrix that is affected by k outliers and compare them to the eigenvalues of a distance matrix with a constant structure. We show a discrepancy in the sizes of the eigenvalues of a distance matrix that is contaminated with outliers, present an algorithm and offer a new outlier detection method based on the eigenvalues of the …distance matrix. We compare the new distance-based outlier technique with several existing methods under five distributions. The methods are applied to a study of public utility companies and gene expression data. Show more
Keywords: Distance matrix, decomposition, eigenvalue, outlier, detection
DOI: 10.3233/IDA-230048
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-19, 2023
Authors: Tian, Qing | Cheng, Yao
Article Type: Research Article
Abstract: The aim of unsupervised domain adaptation (UDA) in person re-identification (re-ID) is to develop a model that can identify the same individual across different cameras in the target domain, using labeled data from the source domain and unlabeled data from the target domain. However, existing UDA person re-ID methods typically assume a single source domain and a single target domain, and seldom consider the scenario of multiple source domains and a single target domain. In the latter scenario, differences in sample size between domains can lead to biased training of the model. To address this, we propose an unsupervised multi-source …domain adaptation person re-ID method via sample weighting. Our approach utilizes multiple source domains to leverage valuable label information and balances the inter-domain sample imbalance through sample weighting. We also employ an adversarial learning method to align the domains. The experimental results, conducted on four datasets, demonstrate the effectiveness of our proposed method. Show more
Keywords: Person re-identification, unsupervised domain adaptation, sample weighting, unsupervised multi-source domain adaptation
DOI: 10.3233/IDA-230178
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-18, 2023
Authors: Zhang, Shuo | Hu, Xingbang | Zhang, Wenbo | Chen, Jinyi | Huang, Hejiao
Article Type: Research Article
Abstract: For modern Intelligent Transportation System (ITS), data missing during traffic raster acquisition can be inevitable because of the loop detector malfunction or signal interference. Nevertheless, missing data imputation is meaningful due to the periodic spatio-temporal characteristics and individual randomness of traffic raster data. In this paper, traffic raster data collected from all spatial regions at each time interval are considered as a multiple channel image. Accordingly, the traffic raster data over a period of time can be regarded as video, on which an unsupervised generative neural network called MSST-VAE (Multiple Streams Spatial Temporal-VAE) is proposed for traffic raster data imputation, …and this model can even robustly performs at varied missing rates while many other approaches fail to conduct. Two major innovations can be summarized in MSSTVAE: Firstly, it uses multiple periodic streams of Variational Auto-Encoders (VAEs) with Sylvester Normalizing Flows (SNFs), which shows strong generalization ability. Secondly, after the traffic raster data are transferred into videos, an ECB (Extraction-and-Calibration Block) consisting of dilated P3D gated convolution and multi-horizon attention mechanism is employed to learn global-local-granularity spatial features and long-short-term temporal features. Extensive experiments on three real traffic flow datasets validate that MSST-VAE outperforms other classical traffic imputation models with the least imputation error. Show more
Keywords: Intelligent transportation system, traffic raster data, data imputation
DOI: 10.3233/IDA-230091
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Jiang, Yun | Qiao, Hao
Article Type: Research Article
Abstract: Skin lesion segmentation from dermatoscopic images is essential for the diagnosis of skin cancer. However, it is still a challenging task due to the ambiguity of the skin lesions, the irregular shape of the lesions and the presence of various interfering factors. In this paper, we propose a novel Ambiguous Context Enhanced Attention Network (ACEANet) based on the classical encoder-decoder architecture, which is able to accurately and reliably segment a variety of lesions with efficiency. Specifically, a novel Ambiguous Context Enhanced Attention module is embedded in the skip connection to augment the ambiguous boundary information. A Dilated Gated Fusion block …is employed in the end of the encoding phase, which effectively reduces the loss of spatial location information due to continuous downsampling. In addition, we propose a novel Cascading Global Context Attention to fuse feature information generated by the encoder with features generated by the decoder of the corresponding layer. In order to verify the effectiveness and advantages of the proposed network, we have performed comparative experiments on ISIC2018 dataset and PH2 dataset. Experiments results demonstrate that the proposed model has superior segmentation performance for skin lesions. Show more
Keywords: Skin lesion segmentation, medical image processing, feature extraction, encoder-decoder architecture
DOI: 10.3233/IDA-230298
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Shi, Xuefeng | Hu, Min | Ren, Fuji | Shi, Piao
Article Type: Research Article
Abstract: Active Learning (AL) is a technique being widely employed to minimize the time and labor costs in the task of annotating data. By querying and extracting the specific instances to train the model, the relevant task’s performance is improved maximally within limited iterations. However, rare work was conducted to fully fuse features from different hierarchies to enhance the effectiveness of active learning. Inspired by the thought of information compensation in many famous deep learning models (such as ResNet, etc.), this work proposes a novel TextCNN-based Two ways Active Learning model (TCTWAL) to extract task-relevant texts. TextCNN takes the advantage of …little hyper-parameter tuning and static vectors and achieves excellent results on various natural language processing (NLP) tasks, which are also beneficial to human-computer interaction (HCI) and the AL relevant tasks. In the process of the proposed AL model, the candidate texts are measured from both global and local features by the proposed AL framework TCTWAL depending on the modified TextCNN. Besides, the query strategy is strongly enhanced by maximum normalized log-probability (MNLP), which is sensitive to detecting the longer sentences. Additionally, the selected instances are characterized by general global information and abundant local features simultaneously. To validate the effectiveness of the proposed model, extensive experiments are conducted on three widely used text corpus, and the results are compared with with eight manual designed instance query strategies. The results show that our method outperforms the planned baselines in terms of accuracy, macro precision, macro recall, and macro F1 score. Especially, to the classification results on AG’s News corpus, the improvements of the four indicators after 39 iterations are 40.50%, 45.25%, 48.91%, and 45.25%, respectively. Show more
Keywords: Active learning, TextCNN, maximum normalized log-probability, global information, local feature
DOI: 10.3233/IDA-230332
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Zhong, Qing | Shao, Xinhui
Article Type: Research Article
Abstract: For the aspect-based sentiment analysis task, traditional works are only for text modality. However, in social media scenarios, texts often contain abbreviations, clerical errors, or grammatical errors, which invalidate traditional methods. In this study, the cross-model hierarchical interactive fusion network incorporating an end-to-end approach is proposed to address this challenge. In the network, a feature attention module and a feature fusion module are proposed to obtain the multimodal interaction feature between the image modality and the text modality. Through the attention mechanism and gated fusion mechanism, these two modules realize the auxiliary function of image in the text-based aspect-based sentiment …analysis task. Meanwhile, a boundary auxiliary module is used to explore the dependencies between two core subtasks of the aspect-based sentiment analysis. Experimental results on two publicly available multi-modal aspect-based sentiment datasets validate the effectiveness of the proposed approach. Show more
Keywords: Multimodal aspect-based sentiment analysis, hierarchical interactive fusion, multi-head interaction attention mechanism, gated mechanism
DOI: 10.3233/IDA-230305
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Chen, Hongwei | Shi, Dewei | Zhou, Xun | Zhang, Man | Liu, Luanxuan
Article Type: Research Article
Abstract: Credit fraud is a common financial crime that causes significant economic losses to financial institutions. To address this issue, researchers have proposed various fraud detection methods. Recently, research on deep forests has opened up a new path for exploring deep models beyond neural networks. It combines the features of neural networks and ensemble learning, and has achieved good results in various fields. This paper mainly studies the application of deep forests to the field of fraud detection and proposes a distributed dense rotation deep forest algorithm (DRDF-spark) based on the improved RotBoost. The model has three main characteristics: firstly, it …solves the problem of multi-granularity scanning due to the lack of spatial correlation in the data by introducing RotBoost. Secondly, Spark is used for parallel construction to improve the processing speed and efficiency of data. Thirdly, a pre-aggregation mechanism is added to the distributed algorithm to locally aggregate the statistical results of sub-forests in the same node in advance to improve communication efficiency. The experiments show that DRDF-spark performs better than deep forests and some mainstream ensemble learning algorithms on the fraud dataset in this paper, and the training speed is up to 3.53 times faster. Furthermore, if the number of nodes is further increased, the speedup ratio will continue to increase. Show more
Keywords: Deep forest, credit fraud detection, ensemble learning, RotBoost, spark
DOI: 10.3233/IDA-230193
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-25, 2024
Authors: Jiménez-Gaona, Yuliana | Rodríguez-Alvarez, María José | Escudero, Líder | Sandoval, Carlos | Lakshminarayanan, Vasudevan
Article Type: Research Article
Abstract: INTRODUCTION: Ultrasound in conjunction with mammography imaging, plays a vital role in the early detection and diagnosis of breast cancer. However, speckle noise affects medical ultrasound images and degrades visual radiological interpretation. Speckle carries information about the interactions of the ultrasound pulse with the tissue microstructure, which generally causes several difficulties in identifying malignant and benign regions. The application of deep learning in image denoising has gained more attention in recent years. OBJECTIVES: The main objective of this work is to reduce speckle noise while preserving features and details in breast ultrasound images using GAN models. …METHODS: We proposed two GANs models (Conditional GAN and Wasserstein GAN) for speckle-denoising public breast ultrasound databases: BUSI, DATASET A, AND UDIAT (DATASET B). The Conditional GAN model was trained using the Unet architecture, and the WGAN model was trained using the Resnet architecture. The image quality results in both algorithms were measured by Peak Signal to Noise Ratio (PSNR, 35–40 dB) and Structural Similarity Index (SSIM, 0.90–0.95) standard values. RESULTS: The experimental analysis clearly shows that the Conditional GAN model achieves better breast ultrasound despeckling performance over the datasets in terms of PSNR = 38.18 dB and SSIM = 0.96 with respect to the WGAN model (PSNR = 33.0068 dB and SSIM = 0.91) on the small ultrasound training datasets. CONCLUSIONS: The observed performance differences between CGAN and WGAN will help to better implement new tasks in a computer-aided detection/diagnosis (CAD) system. In future work, these data can be used as CAD input training for image classification, reducing overfitting and improving the performance and accuracy of deep convolutional algorithms. Show more
Keywords: Breast cancer, ultrasound image denoising, generative adversarial network
DOI: 10.3233/IDA-230631
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Göcs, László | Johanyák, Zsolt Csaba
Article Type: Research Article
Abstract: Intrusion detection systems (IDSs) are essential elements of IT systems. Their key component is a classification module that continuously evaluates some features of the network traffic and identifies possible threats. Its efficiency is greatly affected by the right selection of the features to be monitored. Therefore, the identification of a minimal set of features that are necessary to safely distinguish malicious traffic from benign traffic is indispensable in the course of the development of an IDS. This paper presents the preprocessing and feature selection workflow as well as its results in the case of the CSE-CIC-IDS2018 on AWS dataset, focusing …on five attack types. To identify the relevant features, six feature selection methods were applied, and the final ranking of the features was elaborated based on their average score. Next, several subsets of the features were formed based on different ranking threshold values, and each subset was tried with five classification algorithms to determine the optimal feature set for each attack type. During the evaluation, four widely used metrics were taken into consideration. Show more
Keywords: Ddataset preprocessing, dimension reduction, feature selection, classification, Python, CE-CIC-IDS2018
DOI: 10.3233/IDA-230264
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-27, 2024
Authors: Hao, Sun | Qin, Xiaolin | Liu, Xiaojing
Article Type: Research Article
Abstract: There are two mainstream strategies for image-text matching at present. The one, termed as joint embedding learning, aims to model the semantic information of both image and sentence in a shared feature subspace, which facilitates the measurement of semantic similarity but only focuses on global alignment relationship. To explore the local semantic relationship more fully, the other one, termed as metric learning, aims to learn a complex similarity function to directly output score of each image-text pair. However, it significantly suffers from more computation burden at retrieval stage. In this paper, we propose a hierarchically joint embedding model to incorporate …the local semantic relationship into a joint embedding learning framework. The proposed method learns the shared local and global embedding spaces simultaneously, and models the joint local embedding space with respect to specific local similarity labels which are easy to access from the lexical information of corpus. Unlike the methods based on metric learning, we can prepare the fixed representations of both images and sentences by concatenating the normalized local and global representations, which makes it feasible to perform the efficient retrieval. And experiments show that the proposed model can achieve competitive performance when compared to the existing joint embedding learning models on two publicly available datasets Flickr30k and MS-COCO. Show more
Keywords: Information retrieval, cross-modal representation, hierarchical embedding, local alignment
DOI: 10.3233/IDA-230214
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-19, 2023
Authors: Johnson, J. | Giraud-Carrier, C.
Article Type: Research Article
Abstract: While increasingly complex algorithms are being developed for graph classification in highly-structured domains, such as image processing and climate forecasting, they often lead to over-fitting and inefficiency when applied to human interaction networks where the confluence of cooperation, conflict, and evolutionary pressures produces chaotic environments. We propose a graph transformation approach for efficient classification in chaotic human systems that is based on game theoretic, network theoretic, and chaos theoretic principles. Graph structural properties are compiled into time-series that are then transposed into the frequency domain to offer a dynamic view of the system for classification. We propose a set of …benchmark data sets and show through experiments that the approach is efficient and appropriate for many dynamic networks in which agents both compete and cooperate, such as social media networks, stock markets, political campaigns, legislation, and geopolitical events. Show more
Keywords: Graph classification, graph time-series, chaotic systems
DOI: 10.3233/IDA-230194
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-17, 2023
Authors: Zhang, Xu | Hu, Xiaoyu | Liu, Zejie | Xiang, Yanzheng | Zhou, Deyu
Article Type: Research Article
Abstract: Text-to-SQL, a computational linguistics task, seeks to facilitate the conversion of natural language queries into SQL queries. Recent methodologies have leveraged the concept of slot-filling in conjunction with predetermined SQL templates to effectively bridge the semantic gap between natural language questions and structured database queries, achieving commendable performance by harnessing the power of multi-task learning. However, employing identical features across diverse tasks is an ill-suited practice, fraught with inherent drawbacks. Firstly, based on our observation, there are clear boundaries in the natural language corresponding to SELECT and WHERE clauses. Secondly, the exclusive features integral to each subtask are inadequately emphasized …and underutilized, thereby hampering the acquisition of discriminative features for each specific subtask. In an endeavor to rectify these issues, the present work introduces an innovative approach: the hierarchical feature decoupling model for SQL query generation from natural language. This novel approach involves the deliberate separation of features pertaining to subtasks within both SELECT and WHERE clauses, further dissociating these features at the subtask level to foster better model performance. Empirical results derived from experiments conducted on the WikiSQL benchmark dataset reveal the superiority of the proposed approach over several state-of-the-art baseline methods in the context of text-to-SQL query generation. Show more
Keywords: Text-to-SQL, multi-task learning, discriminative features, feature decoupling
DOI: 10.3233/IDA-230390
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Al-Jumaili, Ahmed Hadi Ali | Muniyandi, Ravie Chandren | Hasan, Mohammad Kamrul | Singh, Mandeep Jit | Paw, Johnny Koh Siaw | Al-Jumaily, Abdulmajeed
Article Type: Research Article
Abstract: Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout’s distributed machine-learning environment. The study taps into Apache Hadoop’s robust tools for data storage and processing, including HDFS and MapReduce, to effectively manage and analyze big data challenges. The preprocessing phase utilizes Canopy clustering to expedite the initial partitioning of data points, which are subsequently refined by K-means to enhance clustering performance. Experimental results confirm that incorporating the Canopy as an initial step markedly reduces the computational effort to process the vast quantity …of parallel power load abnormalities. The Canopy clustering approach, enabled by distributed machine learning through Apache Mahout, is utilized as a preprocessing step within the K-means clustering technique. The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. Data vectors are generated based on the time needed, sequential and parallel candidate feature data are obtained, and the data rate is combined. After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. The results showed that using canopy as a preprocessing step cut the time it proceeds to deal with the significant number of power load abnormalities found in parallel using a fast density peak dataset and the time it proceeds for the k-means algorithm to run. Additionally, tests demonstrate that combining canopy and the K-means algorithm to analyze data performs consistently and dependably on the Hadoop platform and has a clustering result that offers a scalable and effective solution for power system monitoring. Show more
Keywords: Power load data, abnormality detection and adjustment, hybrid (CKMA), K-means algorithm (KMA), canopy algorithm (CA), Apache Mahout
DOI: 10.3233/IDA-230573
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-26, 2024
Authors: Zhou, Rucheng | Zhang, Dongmei | Zhu, Jiabao | Min, Geyong
Article Type: Research Article
Abstract: Traffic forecasting has become a core component of Intelligent Transportation Systems. However, accurate traffic forecasting is very challenging, caused by the complex traffic road networks. Most existing forecasting methods do not fully consider the topological structure information of road networks, making it difficult to extract accurate spatial features. In addition, spatial and temporal features have different impacts on traffic conditions, but the existing studies ignore the distribution of spatial-temporal features in traffic regions. To address these limitations, we propose a novel graph neural network architecture named Attention-based Spatial-Temporal Adaptive Integration Gated Network (AST-AIGN). The originality of AST-AIGN is to obtain …a spatial feature that more accurately reflects the topological structure of the road networks by embedding Graph Attention Network (GAT) into Jumping Knowledge Net (JK-Net). We propose a data-dependent function called spatial-temporal adaptive integration gate to process the diversity of feature distribution and highlight features in road networks that significantly affects traffic conditions. We evaluate our model on two real-world traffic datasets from the Caltrans Performance Measurement System (PEMS04 and PEMS08), and the extensive experimental results demonstrate the proposed AST-AIGN architecture outperforms other baselines. Show more
Keywords: Traffic forecasting, spatial-temporal dependences, jumping knowledge, gating mechanism, self-attention
DOI: 10.3233/IDA-230101
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-25, 2024
Authors: Yu, Dongjin | Ni, Ke | Li, Zhongyang | Zhang, Shengyi | Sun, Xiaoxiao | Hou, Wenjie | Ying, Yuke
Article Type: Research Article
Abstract: Process discovery techniques analyze process logs to extract models that characterize the behavior of business processes. In real-life logs, however, noises exist and adversely affect the extraction and thus decrease the understandability of discovered models. In this paper, we propose a novel double granularity filtering method, executed on both the event and trace levels, to detect noises by analyzing the directly-following and parallel relations between events. Based on the probability of an event occurring in a sequence, the infrequent behaviors and redundant events in the logs can be filtered out. In addition, the missing events in parallel blocks are detected …to further improve the performance of filtering. Experiments on synthetic logs and five real-life datasets demonstrate that our method significantly outperforms other state-of-the-art methods. Show more
Keywords: Process discovery, process mining, event logs, noise filtering, event dependency, parallel relation
DOI: 10.3233/IDA-230118
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Belbekri, Adel | Benchikha, Fouzia | Slimani, Yahya | Marir, Naila
Article Type: Research Article
Abstract: Named Entity Recognition (NER) is an essential task in Natural Language Processing (NLP), and deep learning-based models have shown outstanding performance. However, the effectiveness of deep learning models in NER relies heavily on the quality and quantity of labeled training datasets available. A novel and comprehensive training dataset called SocialNER2.0 is proposed to address this challenge. Based on selected datasets dedicated to different tasks related to NER, the SocialNER2.0 construction process involves data selection, extraction, enrichment, conversion, and balancing steps. The pre-trained BERT (Bidirectional Encoder Representations from Transformers) model is fine-tuned using the proposed dataset. Experimental results highlight the superior …performance of the fine-tuned BERT in accurately identifying named entities, demonstrating the SocialNER2.0 dataset’s capacity to provide valuable training data for performing NER in human-produced texts. Show more
Keywords: Big data, deep learning, user-generated texts, text analysis, named entity recognition
DOI: 10.3233/IDA-230588
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-25, 2024
Authors: Hu, Haiping | Huo, Wei | Yan, Yingying | Zhu, Qiuyu
Article Type: Research Article
Abstract: For the pattern recognition, most classification models are solved iteratively, except for Linear LDA, KLDA and ELM etc. In this paper, a nonlinear classification network model based on predefined evenly-distributed class centroids (PEDCC) is proposed. Its analytical solution can be obtained and has good interpretability. Using the characteristics of maximizing the inter-class distance of PEDCC and derivative weighted minimum mean square error loss function to minimize the intra-class distance, we can not only realize the effective nonlinearity of the network, but also obtain the analytical solution of the network weight. Then, the sample is classified based on GDA. In order …to further improve the performance of classification, PCA is used to reduces the dimensionality of the original sample, meanwhile, the CReLU activation function are adopted to enhances the expression ability of the features. The network transforms the samples into the higher dimensional feature space through the weighted minimum mean square error, so as to find a better separation hyperplane. In experiments, the feasibility of the network structure is verified from pure linear 𝑾 , 𝑾 + Tanh, and PCA+ 𝑾 + Tanh respectively on many small data sets and large data sets, and compared with SVM and ELM in terms of training speed and recognition rate. The results show that, in general, this model has advantages on small data set both in recognition accuracy and training speed, while it has advantages in training speed on large data sets. Finally, by introducing a multi-stage network structure based on the latent feature norm, the classifier network can further significantly improve the classification performance, the recognition rate of small data sets is effectively improved and much higher than that of existing methods, while the recognition rate of large data sets is similar to that of SVM. Show more
Keywords: Pattern recognition, image classification, machine learning, GDA
DOI: 10.3233/IDA-230044
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Cao, Jinhui | Di, Xiaoqiang | Liu, Xu | Xu, Rui | Li, Jinqing | Ren, Weiwu | Qi, Hui | Hu, Pengfei | Zhang, Kehan | Li, Bo
Article Type: Research Article
Abstract: Logs play an important role in anomaly detection, fault diagnosis, and trace checking of software and network systems. Log parsing, which converts each raw log line to a constant template and a variable parameter list, is a prerequisite for system security analysis. Traditional parsing methods utilizing specific rules can only parse logs of specific formats, and most parsing methods based on deep learning require labels. However, the existing parsing methods are not applicable to logs of inconsistent formats and insufficient labels. To address these issues, we propose a robust Log parsing method based on Self-supervised Learning (LogSL), which can extract …templates from logs of different formats. The essential idea of LogSL is modeling log parsing as a multi-token prediction task, which makes the multi-token prediction model learn the distribution of tokens belonging to the template in raw log lines by self-supervision mode. Furthermore, to accurately predict the tokens of the template without labeled data, we construct a Multi-token Prediction Model (MPM) combining the pre-trained XLNet module, the n-layer stacked Long Short-Term Memory Net module, and the Self-attention module. We validate LogSL on 12 benchmark log datasets, resulting in the average parsing accuracy of our parser being 3.9% higher than that of the best baseline method. Experimental results show that LogSL has superiority in terms of robustness and accuracy. In addition, a case study of anomaly detection is conducted to demonstrate the support of the proposed MPM to system security tasks based on logs. Show more
Keywords: System security, data analysis, log parsing, deep learning, self-supervised learning
DOI: 10.3233/IDA-230133
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Nayancy, | Dutta, Sandip | Chakraborty, Soubhik
Article Type: Research Article
Abstract: Blockchain has attracted tremendous attention in recent years due to its significant features including anonymity, security, immutability, and audibility. Blockchain technology has been used in several nonmonetary applications, including Internet-of-Things. Though blockchain has limited resources, and scalability is computationally expensive, resulting in delays and large bandwidth overhead that are unsuitable for many IoT devices. In this paper, we work on a lightweight blockchain approach that is suited for IoT needs and provides end-to-end security. Decentralization is achieved in our lightweight blockchain implementation by building a network with a lot of high-resource devices collaborate to maintain the blockchain. The nodes in …the network is arranged in sorted order w.r.t execution time and count to reduce the mining overheads and is accountable for handling the public blockchain. We propose a distributed execution time-based consensus algorithm that decreases the delay and overhead of the mining process. We also propose a randomized node-selection algorithm for the selection of nodes to verify the mined blocks to eliminate the double-spend and 51% attack. The results are encouraging and significantly reduce the mining overhead and keep a check on the double-spending problem and 51% attack. Show more
Keywords: Blockchain, IoT, lightweight consensus, double-spend attack, 51% attack
DOI: 10.3233/IDA-230153
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Boullé, Marc
Article Type: Research Article
Abstract: Histograms are among the most popular methods used in exploratory analysis to summarize univariate distributions. In particular, irregular histograms are good non-parametric density estimators that require very few parameters: the number of bins with their lengths and frequencies. Although many approaches have been proposed in the literature to infer these parameters, most existing histogram methods are difficult to exploit for exploratory analysis in the case of real-world data sets, with scalability issues, truncated data, outliers or heavy-tailed distributions. In this paper, we focus on the G-Enum histogram method, which exploits the Minimum Description Length (MDL) principle to build histograms without …any user parameter. We then propose to extend this method by exploiting a new modeling space based on floating-point representation, with the objective of building histograms resistant to outliers or heavy-tailed distributions. We also suggest several heuristics and a methodology suitable for the exploratory analysis of large scale real-world data sets, whose underlying patterns are difficult to recover for digitization reasons. Extensive experiments show the benefits of the approach, evaluated with a dual objective: the accuracy of density estimation in the case of outliers or heavy-tailed distributions, and the effectiveness of the approach for exploratory data analysis. Show more
Keywords: Density estimation, histograms, model selection, minimum description length, exploratory analysis
DOI: 10.3233/IDA-230638
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-48, 2024
Authors: Yu, Mingxin | Wang, Jun | You, Rui | Ji, Xinglong | Lu, Wenshuai
Article Type: Research Article
Abstract: Person re-identification (ReID) is widely used in intelligent security, monitoring, criminal investigation and other fields. Aiming at the problems of local occlusion, scale misalignment and attitude change of pedestrian images in actual scenes, we propose a Multi-local Feature and Attention fused network (MFA) used for person re-identification task. Firstly, Channel Point Affinity Attention module (CPAA) is embedded in the backbone network to enhance the ability of the network for extracting local details. The feature map output from the backbone network is horizontally segmented into four local feature maps, and further four branch networks are concatenated to the feature map of …the backbone network. The four local feature maps are used to guide the four branch networks to pay more attention on different areas of pedestrians through Global Local Aligned loss (GLA) function. Finally, the pedestrian feature vector containing multi-local features is obtained. The mAP of the network on Market-1501, DukeMTMC-reID,CUHK03 and MSMT17 datasets were 88.6%, 81.4%, 79.5% and 64.7%, and the Rank-1 was 95.8%, 90.1%, 81.2% and 84.1% respectively. In addition, the model also obtained 73.2% and 68.1% of Rank-1 on partial dataset Patial-REID and Patial-iLIDS, respectively. Recently, The MFA model parameter is 28.3M and the inference efficiency is approximately 32 fps to an image with a resulation of 256 × 128. Compared with other ReID methods, our proposed methods achieved a competitive performance for ReID task. The code was available at github:[email protected]:ISCLab-Bistu/MFA.git. Show more
Keywords: Person re-identification, attention mechanism, local feature, multi branches network, deep learning
DOI: 10.3233/IDA-230392
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Zhang, Fu | Zhang, Wei | Wang, Gang
Article Type: Research Article
Abstract: The Resource Description Framework (RDF) is a framework for expressing information about resources in the form of triples (subject , predicate , object ). The information represented by the standard RDF is static, i . e . , that does not change over time. To better deal with a large amount of time-related information, temporal RDF is proposed. Consequently, how to explore index technology to efficiently query temporal information has become an important research issue, but the research on the index of temporal RDF is still short, especially the index of bitemporal RDF. Bitemporal RDF …can represent more complicated situations (e.g., RDF triples with both valid time and transaction time ). Indexes for bitemporal RDF can further expand the application scenarios and functions of temporal RDF. In this paper, we propose an efficient index for bitemporal RDF queries. The index innovatively introduces and re-designs skip list structure into the bitemporal RDF query. We also investigate how to cover almost all query patterns with as few indexes as possible. In addition, although the proposed index is conceived for temporal RDF, it also takes into account the performance of standard RDF queries when the time element is unknown. Finally, we run experiments with synthetic data sets of different sizes using the Lehigh University Benchmark (LUBM), and results prove that the proposed index is scalable and effective. Show more
Keywords: Resource Description Framework (RDF), temporal RDF, bitemporal RDF, index
DOI: 10.3233/IDA-230609
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Malhotra, Ruchika | Cherukuri, Madhukar
Article Type: Research Article
Abstract: BACKGROUND: Software quality prediction models play a crucial role in identifying vulnerable software components during early stages of development, and thereby optimizing the resource allocation and enhancing the overall software quality. While various classification algorithms have been employed for developing these prediction models, most studies have relied on default hyperparameter settings, leading to significant variability in model performance. Tuning the hyperparameters of classification algorithms can enhance the predictive capability of quality models by identifying optimal settings for improved accuracy and effectiveness. METHOD: This systematic review examines studies that have utilized hyperparameter tuning techniques to develop prediction …models in software quality domain. The review focused on diverse areas such as defect prediction, maintenance estimation, change impact prediction, reliability prediction, and effort estimation, as these domains demonstrate the wide applicability of common learning algorithms. RESULTS: This review identified 31 primary studies on hyperparameter tuning for software quality prediction models. The results demonstrate that tuning the parameters of classification algorithms enhances the performance of prediction models. Additionally, the study found that certain classification algorithms exhibit high sensitivity to their parameter settings, achieving optimal performance when tuned appropriately. Conversely, certain classification algorithms exhibit low sensitivity to their parameter settings, making tuning unnecessary in such instances. CONCLUSION: Based on the findings of this review, the study conclude that the predictive capability of software quality prediction models can be significantly improved by tuning their hyperparameters. To facilitate effective hyperparameter tuning, we provide practical guidelines derived from the insights obtained through this study. Show more
Keywords: Hyperparameter tuning, machine learning, defect prediction, effort estimation, maintenance prediction, reliability
DOI: 10.3233/IDA-230653
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Malik, Muhammad Shahid Iqbal | Nawaz, Aftab | Jamjoom, Mona Mamdouh | Ignatov, Dmitry I.
Article Type: Research Article
Abstract: Online product reviews (OPR) are a commonly used medium for consumers to communicate their experiences with products during online shopping. Previous studies have investigated the helpfulness of OPRs using frequency-based, linguistic, meta-data, readability, and reviewer attributes. In this study, we explored the impact of robust contextual word embeddings, topic, and language models in predicting the helpfulness of OPRs. In addition, the wrapper-based feature selection technique is employed to select effective subsets from each type of features. Five feature generation techniques including word2vec, FastText, Global Vectors for Word Representation (GloVe), Latent Dirichlet Allocation (LDA), and Embeddings from Language Models (ELMo), were …employed. The proposed framework is evaluated on two Amazon datasets (Video games and Health & personal care). The results showed that the ELMo model outperformed the six standard baselines, including the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. In addition, ELMo achieved Mean Square Error (MSE) of 0.0887 and 0.0786 respectively on two datasets and MSE of 0.0791 and 0.0708 with the wrapper method. This results in the reduction of 1.43% and 1.63% in MSE as compared to the fine-tuned BERT model on respective datasets. However, the LDA model has a comparable performance with the fine-tuned BERT model but outperforms the other five baselines. The proposed framework demonstrated good generalization abilities by uncovering important factors of product reviews and can be evaluated on other voting platforms. Show more
Keywords: Word2vec, ELMo, LDA, helpfulness prediction, semantic model, Amazon
DOI: 10.3233/IDA-230349
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-21, 2023
Authors: Wang, Sijie | Li, Yifei | Chen, Diansheng | Li, Jiting | Zhang, Xiaochuan
Article Type: Research Article
Abstract: Due to the multiple types of objects and the uncertainty of their geometric structures and scales in indoor scenes, the position and pose estimation of point clouds of indoor objects by mobile robots has the problems of domain gap, high learning cost, and high computing cost. In this paper, a lightweight 6D pose estimation method is proposed, which decomposes the pose estimation into a viewpoint and the in-plane rotation around the optical axis of the viewpoint, and the improved PointNet+ + network structure and two lightweight modules are used to construct a codebook, and the …6d pose estimation of the point cloud of the indoor objects is completed by building and querying the codebook. The model was trained on the ShapeNetV2 dataset, and reports the ADD-S metric validation on the YCB-Video and LineMOD datasets, reaching 97.0% and 94.6% respectively. The experiment shows that the model can be trained to estimate the 6d position and pose of the unknown object point cloud with lower computation and storage cost, and the model with fewer parameters and better real-time performance is superior to other high-recision methods. Show more
Keywords: Domain adaptation, 6d pose estimation, lightweight neural network, indoor scene, mobile robot
DOI: 10.3233/IDA-230278
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-12, 2023
Authors: Zhang, Xi | Chen, Hu | Li, Rui | Fei, Zhaolei | Min, Fan
Article Type: Research Article
Abstract: Effective identification of anomalous data from production time series in the oilfield affects future analysis and forecasting. Such time series is often characterized by irregular time intervals due to uneven manual sampling, and missing values caused by incomplete measurements. Therefore, the identification task becomes more challenging. In this paper, an Attention-Embedded Time-Aware Imputation Network (ATIN) with two sub-networks is proposed for this task. First, Time-Aware Imputation LSTM (TI-LSTM) is designed for modeling irregular time intervals and incomplete measurements. It decays the long-term memory component as the producing well conditions may be varied during the water cut stage. Second, Attention-Embedding LSTM …(ATEM) is designed to improve the effectiveness of anomaly detection. It focuses on the correlation between the last and historical measurements in a given sequence. Comparison experiments with several state-of-the-art methods, including mTAN, GRU-D, T-LSTM, ATTAIN, and BRITS are conducted. Results show that the proposed ATIN performs better in accuracy, F 1 -score, and area under curve (AUC). Show more
Keywords: Attention mechanism, missing value, production data, time series
DOI: 10.3233/IDA-230301
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-21, 2023
Authors: Meng, Shiting | Hao, Qingbo | Xiao, Yingyuan | Zheng, Wenguang
Article Type: Research Article
Abstract: Convolutional neural networks (CNNs) have been successfully applied to music genre classification tasks. With the development of diverse music, genre fusion has become common. Fused music exhibits multiple similar musical features such as rhythm, timbre, and structure, which typically arise from the temporal information in the spectrum. However, traditional CNNs cannot effectively capture temporal information, leading to difficulties in distinguishing fused music. To address this issue, this study proposes a CNN model called MusicNeXt for music genre classification. Its goal is to enhance the feature extraction method to increase focus on musical features, and increase the distinctiveness between different genres, …thereby reducing classification result bias. Specifically, we construct the feature extraction module which can fully utilize temporal information, thereby enhancing its focus on music features. It exhibits an improved understanding of the complexity of fused music. Additionally, we introduce a genre-sensitive adjustment layer that strengthens the learning of differences between different genres through within-class angle constraints. This leads to increased distinctiveness between genres and provides interpretability for the classification results. Experimental results demonstrate that our proposed MusicNeXt model outperforms baseline networks and other state-of-the-art methods in music genre classification tasks, without generating category bias in the classification results. Show more
Keywords: Music genre classification, spectrogram, deep learning, L-softmax loss
DOI: 10.3233/IDA-230428
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Zhang, Ji | Yu, Mingxin | Lu, Wenshuai | Dai, Yuxiang | Shi, Huiyu | You, Rui
Article Type: Research Article
Abstract: Transformer-based networks have revolutionized visual tasks with their continuous innovation, leading to significant progress. However, the widespread adoption of Vision Transformers (ViT) is limited due to their high computational and parameter requirements, making them less feasible for resource-constrained mobile and edge computing devices. Moreover, existing lightweight ViTs exhibit limitations in capturing different granular features, extracting local features efficiently, and incorporating the inductive bias inherent in convolutional neural networks. These limitations somewhat impact the overall performance. To address these limitations, we propose an efficient ViT called Dual-Granularity Former (DGFormer). DGFormer mitigates these limitations by introducing two innovative modules: Dual-Granularity Attention (DG …Attention) and Efficient Feed-Forward Network (Efficient FFN). In our experiments, on the image recognition task of ImageNet, DGFormer surpasses lightweight models such as PVTv2-B0 and Swin Transformer by 2.3% in terms of Top1 accuracy. On the object detection task of COCO, under RetinaNet detection framework, DGFormer outperforms PVTv2-B0 and Swin Transformer with increase of 0.5% and 2.4% in average precision (AP), respectively. Similarly, under Mask R-CNN detection framework, DGFormer exhibits improvement of 0.4% and 1.8% in AP compared to PVTv2-B0 and Swin Transformer, respectively. On the semantic segmentation task on the ADE20K, DGFormer achieves a substantial improvement of 2.0% and 2.5% in mean Intersection over Union (mIoU) over PVTv2-B0 and Swin Transformer, respectively. The code is open-source and available at: https://github.com/ISCLab-Bistu/DGFormer.git . Show more
Keywords: Transformer, object detection, classification, semantic segmentation
DOI: 10.3233/IDA-230799
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Johnson, Joseph | Giraud-Carrier, Christophe | Hatch, Bradley
Article Type: Research Article
Abstract: We introduce a new inductive bias for learning in dynamic event-based human systems. This is intended to partially address the issue of deep learning in chaotic systems. Instead of fitting the data to polynomial expansions that are expressive enough to approximate the generative functions or of inducing a universal approximator to learn the patterns and inductive bias, we only assume that the relationship between the input features and output classes changes over time, and embed this assumption through a form of dynamic contrastive learning in pre-training, where pre-training labels contain information about the class labels and time periods. We do …this by extending and integrating two separate forms of contrastive learning. We note that this approach is not equivalent to inserting an extra feature into the input data that contains time period, because the input data cannot contain the label. We illustrate the approach on a recently designed learning algorithm for event-based graph time-series classification, and demonstrate its value on real-world data. Show more
Keywords: Inductive bias, supervised contrastive learning, evolutionary game theory, dynamic systems, deep learning
DOI: 10.3233/IDA-230555
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Lin, Xinrui | Wang, Wei | Zhu, Xiaohui | Yue, Yong
Article Type: Research Article
Abstract: In the digital era, the rapid advancement of artificial intelligence has put a spotlight on target detection, especially in traffic settings. This area of study is pivotal for crucial projects like autonomous vehicles, road monitoring, and traffic sign recognition. However, existing Chinese traffic datasets lack comprehensive benchmarks for traffic signs and signals, and foreign datasets do not match Chinese traffic conditions. Manually annotating a large-scale dataset tailored for Chinese traffic conditions presents a significant challenge. This study addresses this gap by proposing a cross-augmentation method for image datasets. We utilized YOLOX for target detection and trained models on the BDD100K …dataset, achieving an impressive mAP of 60.25%, surpassing most algorithms. Leveraging transfer learning, we enhanced the CCTSDB dataset, creating the ACCTSDB dataset, which includes annotations for common traffic objects and Chinese traffic signs. Using YOLOX, we trained a traffic detector tailored for Chinese traffic scenarios, achieving an mAP of 75.79%. To further validate our approach, we conducted experiments on the TT100K dataset and successfully introduced the ATT100K dataset. Our methodology is poised to alleviate the limitations of manually annotating image datasets. The proposed ACCTSDB dataset and ATT100K dataset are expected to compensate for the lack of large-scale, multi-class traffic datasets in China. Show more
Keywords: Data augmentation, computer vision
DOI: 10.3233/IDA-230075
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: He, Xinyu | Liu, Siyu | Yan, Ge | Zhang, Xueyan
Article Type: Research Article
Abstract: Due to the vigorous development of big data, news topic text classification has received extensive attention, and the accuracy of news topic text classification and the semantic analysis of text are worth us to explore. The semantic information contained in news topic text has an important impact on the classification results. Traditional text classification methods tend to default the text structure to the sequential linear structure, then classify by giving weight to words or according to the frequency value of words, while ignoring the semantic information in the text, which eventually leads to poor classification results. In order to solve …the above problems, this paper proposes a BiLSTM-GCN (Bidirectional Long Short-Term Memory and Graph Convolutional Network) hybrid neural network text classification model based on dependency parsing. Firstly, we use BiLSTM to complete the extraction of feature vectors in the text; Then, we employ dependency parsing to strengthen the influence of words with semantic relationship, and obtain the global information of the text through GCN; Finally, aim to prevent the overfitting problem of the hybrid neural network which may be caused by too many network layers, we add a global average pooling layer. Our experimental results show that this method has a good performance on the THUCNews and SogouCS datasets, and the F-score reaches 91.37% and 91.76% respectively. Show more
Keywords: Text classification, semantic information, dependency graph structure, BiLSTM, GCN
DOI: 10.3233/IDA-230061
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Liu, Quanbo | Li, Xiaoli | Wang, Kang
Article Type: Research Article
Abstract: Over the past several years, sulfur dioxide (SO2 ) has raised growing concern in China owing to its adverse impact on atmosphere and human respiratory system. The major contributor to SO2 emissions is flue gas generated by fossil-fired electricity-generating plants, and as a consequence diverse flue gas desulphurization (FGD) techniques are installed to abate SO2 emissions. However, the FGD is a dynamic process with serious nonlinearity and large time delay, making the FGD process modeling problem a formidable one. In our research study, a novel hybrid deep learning model with temporal convolution neural network (TCNN), gated recurrent unit …(GRU) and mutual information (MI) technique is proposed to predict SO2 emissions in an FGD process. Among those technique, MI is applied to select variables that are best suited for SO2 emission prediction, while TCNN and GRU are innovatively integrated to capture dynamics of SO2 emission in the FGD process. A real FGD system in a power plant with a coal-fired unit of 1000 MW is used as a study case for SO2 emission prediction. Experimental results show that the proposed approach offers satisfactory performance in predicting SO2 emissions for the FGD process, and outperforms other contrastive predictive methods in terms of different performance indicators. Show more
Keywords: SO2 emission prediction, flue gas desulphurization, neural network, deep learning, mutual information
DOI: 10.3233/IDA-230890
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Tran, Le-Anh | Kwon, Daehyun | Deberneh, Henock Mamo | Park, Dong-Chul
Article Type: Research Article
Abstract: This paper proposes a data clustering algorithm that is inspired by the prominent convergence property of the Projection onto Convex Sets (POCS) method, termed the POCS-based clustering algorithm . For disjoint convex sets, the form of simultaneous projections of the POCS method can result in a minimum mean square error solution. Relying on this important property, the proposed POCS-based clustering algorithm treats each data point as a convex set and simultaneously projects the cluster prototypes onto respective member data points, the projections are convexly combined via adaptive weight values in order to minimize a predefined objective function for data …clustering purposes. The performance of the proposed POCS-based clustering algorithm has been verified through a large scale of experiments and data sets. The experimental results have shown that the proposed POCS-based algorithm is competitive in terms of both effectiveness and efficiency against some of the prevailing clustering approaches such as the K-Means/K-Means+ + and Fuzzy C-Means (FCM) algorithms. Based on extensive comparisons and analyses, we can confirm the validity of the proposed POCS-based clustering algorithm for practical purposes. Show more
Keywords: POCS, convex sets, clustering algorithm, unsupervised learning, machine learning
DOI: 10.3233/IDA-230655
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Huang, Jiaming | Li, Xianyong | Li, Qizhi | Du, Yajun | Fan, Yongquan | Chen, Xiaoliang | Huang, Dong | Wang, Shumin
Article Type: Research Article
Abstract: Emojis in texts provide lots of additional information in sentiment analysis. Previous implicit sentiment analysis models have primarily treated emojis as unique tokens or deleted them directly, and thus have ignored the explicit sentiment information inside emojis. Considering the different relationships between emoji descriptions and texts, we propose a pre-training Bidirectional Encoder Representations from Transformers (BERT) with emojis (BEMOJI) for Chinese and English sentiment analysis. At the pre-training stage, we pre-train BEMOJI by predicting the emoji descriptions from the corresponding texts via prompt learning. At the fine-tuning stage, we propose a fusion layer to fuse text representations and emoji descriptions …into fused representations. These representations are used to predict text sentiment orientations. Experimental results show that BEMOJI gets the highest accuracy (91.41% and 93.36%), Macro-precision (91.30% and 92.85%), Macro-recall (90.66% and 93.65%) and Macro-F1-measure (90.95% and 93.15%) on the Chinese and English datasets. The performance of BEMOJI is 29.92% and 24.60% higher than emoji-based methods on average on Chinese and English datasets, respectively. Meanwhile, the performance of BEMOJI is 3.76% and 5.81% higher than transformer-based methods on average on Chinese and English datasets, respectively. The ablation study verifies that the emoji descriptions and fusion layer play a crucial role in BEMOJI. Besides, the robustness study illustrates that BEMOJI achieves comparable results with BERT on four sentiment analysis tasks without emojis, which means BEMOJI is a very robust model. Finally, the case study shows that BEMOJI can output more reasonable emojis than BERT. Show more
Keywords: Pre-trained language model, emoji sentiment analysis, implicit sentiment analysis, prompt learning, multi-feature fusion
DOI: 10.3233/IDA-230864
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-25, 2024
Authors: Noronha, Marta D.M. | Zárate, Luis E.
Article Type: Research Article
Abstract: Characterizing longevity profiles from longitudinal studies is a task with many challenges. Firstly, the longitudinal databases usually have high dimensionality, and the similarities between long-lived and non-long-lived records are a highly burdening task for profile characterization. Addressing these issues, in this work, we use data from the English Longitudinal Study of Ageing (ELSA-UK) to characterize longevity profiles through data mining. We propose a method for feature engineering for reducing data dimensionality through merging techniques, factor analysis and biclustering. We apply biclustering to select relevant features discriminating both profiles. Two classification models, one based on a decision tree and the other …on a random forest, are built from the preprocessed dataset. Experiments show that our methodology can successfully discriminate longevity profiles. We identify insights into features contributing to individuals being long-lived or non-long-lived. According to the results presented by both models, the main factor that impacts longevity is related to the correlations between the economic situation and the mobility of the elderly. We suggest that this methodology can be applied to identify longevity profiles from other longitudinal studies since that factor is deemed relevant for profile classification. Show more
Keywords: Longitudinal data mining, human ageing, biclustering, factor analysis, classification
DOI: 10.3233/IDA-230314
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-24, 2024
Authors: Fan, Zeping | Zhang, Xuejun | Huang, Min | Bu, Zhaohui
Article Type: Research Article
Abstract: The Convolution-augmented Transformer (Conformer) model, which was recently introduced, has attained state-of-the-art(SOTA) results in Automatic Speech Recognition (ASR). In this paper, a series of methodical investigations uncover that the Conformer’s design decisions may not represent the most efficient choices when operating within the constraints of a limited computational budget. After a thorough re-evaluation of the Conformer architecture’s design choices, we propose Sampleformer which reduces the Conformer architecture complexity and has more robust performance. We introduce downsampling to the Conformer Encoder, and to exploit the information in the speech features, we incorporate an additional downsampling module to enhance the efficiency and …accuracy of our model. Additionally, we propose a novel and adaptable attention mechanism called multi-group attention, effectively reducing the attention complexity from O ( n 2 d ) to O ( n 2 d ⋅ f / g ) . We performed experiments on the AISHELL-1 corpora, our 13.3 million-parameter CTC model demonstrates a 3.0%/2.6% relative reduction in character error rate (CER) on the dev/test sets, all without the utilization of a language model (LM). Additionally, the model exhibits a 30% improvement in inference compared to our CTC Conformer baseline and trains 27% faster. Show more
Keywords: Speech recognition, conformer, attention mechanism, complexity reduction
DOI: 10.3233/IDA-230612
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Liu, Xiaoyang | Wu, Yudie | Fiumara, Giacomo | De Meo, Pasquale
Article Type: Research Article
Abstract: Traditional community detection models either ignore the feature space information and require a large amount of domain knowledge to define the meta-paths manually, or fail to distinguish the importance of different meta-paths. To overcome these limitations, we propose a novel heterogeneous graph community detection method (called KGNN_HCD, heterogeneous graph Community Detection method based on K -nearest neighbor Graph Neural Network). Firstly, the similarity matrix is generated to construct the topological structure of K -nearest neighbor graph; secondly, the meta-path information matrix is generated using a meta-path transformation layer (Mp-Trans Layer) by adding weighted convolution; finally, a …graph convolutional network (GCN) is used to learn high-quality node representation, and the k -means algorithm is adopted on node embeddings to detect the community structure. We perform extensive experiments and on three heterogeneous datasets, ACM, DBLP and IMDB, and we consider as competitors 11 community detection methods such as CP-GNN and GTN. The experimental results show that the proposed KGNN_HCD method improves 2.54% and 2.56% on the ACM dataset, 2.59% and 1.47% on the DBLP dataset, and 1.22% and 1.67% on the IMDB dataset for both NMI and ARI. Experiments findings suggest that the proposed KGNN_HCD method is reasonable and effective, and KGNN_HCD can be applied to complex network classification and clustering tasks. Show more
Keywords: Heterogeneous graph, meta-path, K-nearest neighbor graph, graph neural network, community detection
DOI: 10.3233/IDA-230356
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Yuan, Wei | Zhao, Shiyu | Wang, Li | Cai, Lijia | Zhang, Yong
Article Type: Research Article
Abstract: In the post-epidemic era, online learning has gained increasing attention due to the advancements in information and big data technology, leading to large-scale online course data with various student behaviors. Online data mining has become a popular and important way of extracting valuable insights from large amounts of data. However, previous online course analysis methods often focused on individual aspects of the data and neglected the correlation among the large-scale learning behavior data, which can lead to an incomplete understanding of the overall learning behavior and patterns within the online course. To solve the problems, this paper proposes an online …course evaluation model based on a graph auto-encoder. In our method, the features of collected online course data are used to construct K-Nearest Neighbor(KNN) graphs to represent the association among the courses. Then the variational graph auto-encoder(VGAE) is introduced to learn the useful implicit features. Finally, we feed the learned implicit features into unsupervised and semi-supervised downstream tasks for online course evaluation, respectively. We conduct experiments on two datasets. In the clustering task, our method showed a more than tenfold increase in the Calinski-Harabasz index compared to unoptimized features, demonstrating significant structural distinction and group coherence. In the classification task, compared to traditional methods, our model exhibited an overall performance improvement of about 10%, indicating its effectiveness in handling complex network data. Show more
Keywords: Educational data mining, online course evaluation, deep learning, graph auto-encoder
DOI: 10.3233/IDA-230557
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: K, Subha | N, Bharathi
Article Type: Research Article
Abstract: In today’s digital era, the generation and sharing of information are rapidly expanding. The increased volume of complex data is big data. YouTube is the primary source of big data. The proliferation of the internet and smart devices has led to a significant increase in content creators on social media platforms, with YouTube being a prominent example. There has been a substantial increase in content creators across various social media platforms, with YouTube emerging as one of the foremost platforms for content generation and sharing. YouTubers face challenges in enhancing content strategies due to the growing number of comments, such …as big data on shared videos. Reading and finding viewers’ opinions of such a large amount of data through manual methods is time-consuming and challenging and makes it hard to understand people’s sentiments. To address this, spark-based machine learning algorithms have emerged as a transformative tool for content creators to understand the audience. The Improved Novel Ensemble Method (INEM) algorithm is designed to predict viewers’ sentiments and emotional responses based on the content they interact through the comments. The proposed results provide valuable insights for content creators, helping them refine the strategies to optimize the channel’s revenue and performance. Fit Tuber Channel is analyzed to perform the sentiment of user comments. Show more
Keywords: Big data, sentiment analysis, machine learning, social-media, spark
DOI: 10.3233/IDA-240198
Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
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