Research

18 pages, 2869 KiB

Open AccessArticle

Automatic Code Review by Learning the Structure Information of Code Graph

by Ying Yin, Yuhai Zhao, Yiming Sun and Chen Chen

Sensors 2023, 23(5), 2551; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052551 - 24 Feb 2023

Cited by 1 | Viewed by 3295

At present, the explosive growth of software code volume and quantity makes the code review process very labor-intensive and time-consuming. An automated code review model can assist in improving the efficiency of the process. Tufano et al., designed two automated tasks to help [...] Read more.

At present, the explosive growth of software code volume and quantity makes the code review process very labor-intensive and time-consuming. An automated code review model can assist in improving the efficiency of the process. Tufano et al., designed two automated tasks to help improve the efficiency of code review based on the deep learning approach, from two different perspectives, namely, the developer submitting the code and the code reviewer. However, they only used code sequence information and did not explore the logical structure information with a richer meaning of the code. To improve the learning of code structure information, a program dependency graph serialization algorithm PDG2Seq algorithm is proposed, which converts the program dependency graph into a unique graph code sequence in a lossless manner, while retaining the program structure information and semantic information. We then designed an automated code review model based on the pre-trained model CodeBERT architecture, which strengthens the learning of code information by fusing program structure information and code sequence information, and then fine-tuned the model according to the code review activity scene to complete the automatic modification of the code. To verify the efficiency of the algorithm, the two tasks in the experiment were compared with the best Algorithm 1-encoder/2-encoder. The experimental results show that the model we proposed has a significant improvement under the BLEU, Lewinshtein distance and ROUGE-L metrics. Full article

(This article belongs to the Special Issue Big Data Analytics and Intelligent Computation to Advance Novel Applications)

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19 pages, 2845 KiB

Open AccessArticle

A PID-Based kNN Query Processing Algorithm for Spatial Data

by Baiyou Qiao, Ling Ma, Linlin Chen and Bing Hu

Sensors 2022, 22(19), 7651; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197651 - 09 Oct 2022

Cited by 2 | Viewed by 1270

Abstract

As a popular spatial operation, the k-Nearest Neighbors (kNN) query is widely used in various spatial application systems. How to efficiently process a kNN query on spatial big data has always been an important research topic in the field of spatial data management. [...] Read more.

As a popular spatial operation, the k-Nearest Neighbors (kNN) query is widely used in various spatial application systems. How to efficiently process a kNN query on spatial big data has always been an important research topic in the field of spatial data management. The centralized solutions are not suitable for spatial big data due to their poor scalability, while the existing distributed solutions are not efficient enough to meet the high real-time requirements of some spatial applications. Therefore, we introduce the Proportional Integral Derivative (PID) control technology into kNN query processing and propose a PID-based kNN query processing algorithm (PIDKNN) for spatial big data based on Spark. In this algorithm, the whole data space is divided into grid cells of the same size using the grid partition method, and the grid-based index is constructed. On this basis, the grid-based density peak clustering algorithm is used to cluster spatial data, and the corresponding PID parameters are set for each cluster. When performing kNN queries, the PID algorithm is used to estimate the radius growth step size of kNN queries, thereby realizing kNN query processing with a variable query radius growth step based on a feedback mechanism, which greatly improves the efficiency of kNN query processing. A series of experimental results show that the PIDKNN algorithm has good performance and scalability and is superior to the existing parallel kNN query processing methods. Full article

(This article belongs to the Special Issue Big Data Analytics and Intelligent Computation to Advance Novel Applications)

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11 pages, 2299 KiB

Open AccessArticle

Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images

by Yiwen Liu, Tao Wen, Wei Sun, Zhenyu Liu, Xiaoying Song, Xuan He, Shuo Zhang and Zhenning Wu

Sensors 2022, 22(15), 5666; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155666 - 28 Jul 2022

Cited by 2 | Viewed by 1155

Abstract

Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to [...] Read more.

Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to the powerful learning ability with the help of the advanced hardware technology. Unfortunately, CNNs have significant overhead on memory usage and computational resources and are labeled ‘black-box’ by scholars for their complex underlying structures. To this end, an interpretable graph-based method has been proposed for motion artifacts detection from head CT images in this paper. From a topological perspective, the artifacts detection problem has been reformulated as a complex network classification problem based on the network topological characteristics of the corresponding complex networks. A motion artifacts detection method based on complex networks (MADM-CN) has been proposed. Firstly, the graph of each CT image is constructed based on the theory of complex networks. Secondly, slice-to-slice relationship has been explored by multiple graph construction. In addition, network topological characteristics are investigated locally and globally, consistent topological characteristics including average degree, average clustering coefficient have been utilized for classification. The experimental results have demonstrated that the proposed MADM-CN has achieved better performance over conventional machine learning and deep learning methods on a real CT dataset, reaching up to 98% of the accuracy and 97% of the sensitivity. Full article

(This article belongs to the Special Issue Big Data Analytics and Intelligent Computation to Advance Novel Applications)

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17 pages, 437 KiB

Open AccessArticle

SURF: Direction-Optimizing Breadth-First Search Using Workload State on GPUs

by Daegun Yoon and Sangyoon Oh

Sensors 2022, 22(13), 4899; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134899 - 29 Jun 2022

Cited by 1 | Viewed by 1797

Abstract

Graph data structures have been used in a wide range of applications including scientific and social network applications. Engineers and scientists analyze graph data to discover knowledge and insights by using various graph algorithms. A breadth-first search (BFS) is one of the fundamental [...] Read more.

Graph data structures have been used in a wide range of applications including scientific and social network applications. Engineers and scientists analyze graph data to discover knowledge and insights by using various graph algorithms. A breadth-first search (BFS) is one of the fundamental building blocks of complex graph algorithms and its implementation is included in graph libraries for large-scale graph processing. In this paper, we propose a novel direction selection method, SURF (Selecting directions Upon Recent workload of Frontiers) to enhance the performance of BFS on GPU. A direction optimization that selects the proper traversal direction of a BFS execution between the push and pull phases is crucial to the performance as well as for efficient handling of the varying workloads of the frontiers. However, existing works select the direction using condition statements based on predefined thresholds without considering the changing workload state. To solve this drawback, we define several metrics that describe the state of the workload and analyze their impact on the BFS performance. To show that SURF selects the appropriate direction, we implement the direction selection method with a deep neural network model that adopts those metrics as the input features. Experimental results indicate that SURF achieves a higher direction prediction accuracy and reduced execution time in comparison with existing state-of-the-art methods that support a direction-optimizing BFS. SURF yields up to a

5.62 \times

and

3.15 \times

speedup over the state-of-the-art graph processing frameworks Gunrock and Enterprise, respectively. Full article

(This article belongs to the Special Issue Big Data Analytics and Intelligent Computation to Advance Novel Applications)

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23 pages, 7395 KiB

Open AccessArticle

Detecting Trivariate Associations in High-Dimensional Datasets

by Chuanlu Liu, Shuliang Wang, Hanning Yuan, Yingxu Dang and Xiaojia Liu

Sensors 2022, 22(7), 2806; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072806 - 06 Apr 2022

Viewed by 1482

Abstract

Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect [...] Read more.

Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC. Full article

(This article belongs to the Special Issue Big Data Analytics and Intelligent Computation to Advance Novel Applications)

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12 pages, 4005 KiB

Open AccessArticle

Dietary Patterns Associated with Diabetes in an Older Population from Southern Italy Using an Unsupervised Learning Approach

by Rossella Tatoli, Luisa Lampignano, Ilaria Bortone, Rossella Donghia, Fabio Castellana, Roberta Zupo, Sarah Tirelli, Sara De Nucci, Annamaria Sila, Annalidia Natuzzi, Madia Lozupone, Chiara Griseta, Sabrina Sciarra, Simona Aresta, Giovanni De Pergola, Paolo Sorino, Domenico Lofù, Francesco Panza, Tommaso Di Noia and Rodolfo Sardone

Sensors 2022, 22(6), 2193; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062193 - 11 Mar 2022

Cited by 10 | Viewed by 2749

Abstract

Dietary behaviour is a core element in diabetes self-management. There are no remarkable differences between nutritional guidelines for people with type 2 diabetes and healthy eating recommendations for the general public. This study aimed to evaluate dietary differences between subjects with and without [...] Read more.

Dietary behaviour is a core element in diabetes self-management. There are no remarkable differences between nutritional guidelines for people with type 2 diabetes and healthy eating recommendations for the general public. This study aimed to evaluate dietary differences between subjects with and without diabetes and to describe any emerging dietary patterns characterizing diabetic subjects. In this cross-sectional study conducted on older adults from Southern Italy, eating habits in the “Diabetic” and “Not Diabetic” groups were assessed with FFQ, and dietary patterns were derived using an unsupervised learning algorithm: principal component analysis. Diabetic subjects (n = 187) were more likely to be male, slightly older, and with a slightly lower level of education than subjects without diabetes. The diet of diabetic subjects reflected a high-frequency intake of dairy products, eggs, vegetables and greens, fresh fruit and nuts, and olive oil. On the other hand, the consumption of sweets and sugary foods was reduced compared to non-diabetics (23.74 ± 35.81 vs. 16.52 ± 22.87; 11.08 ± 21.85 vs. 7.22 ± 15.96). The subjects without diabetes had a higher consumption of red meat, processed meat, ready-to-eat dishes, alcoholic drinks, and lower vegetable consumption. The present study demonstrated that, in areas around the Mediterranean Sea, older subjects with diabetes had a healthier diet than their non-diabetic counterparts. Full article

(This article belongs to the Special Issue Big Data Analytics and Intelligent Computation to Advance Novel Applications)

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15 pages, 3277 KiB

Open AccessArticle

FRDD-Net: Automated Carotid Plaque Ultrasound Images Segmentation Using Feature Remapping and Dense Decoding

by Yanhan Li, Lian Zou, Li Xiong, Fen Yu, Hao Jiang, Cien Fan, Mofan Cheng and Qi Li

Sensors 2022, 22(3), 887; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030887 - 24 Jan 2022

Cited by 7 | Viewed by 2907

Abstract

Automated segmentation and evaluation of carotid plaques ultrasound images is of great significance for the diagnosis and early intervention of high-risk groups of cardiovascular and cerebrovascular diseases. However, it remains challenging to develop such solutions due to the relatively low quality of ultrasound [...] Read more.

Automated segmentation and evaluation of carotid plaques ultrasound images is of great significance for the diagnosis and early intervention of high-risk groups of cardiovascular and cerebrovascular diseases. However, it remains challenging to develop such solutions due to the relatively low quality of ultrasound images and heterogenous characteristics of carotid plaques. To address those problems, in this paper, we propose a novel deep convolutional neural network, FRDD-Net, with an encoder–decoder architecture to automatically segment carotid plaques. We propose the feature remapping modules (FRMs) and incorporate them into the encoding and decoding blocks to ameliorate the reliability of acquired features. We also propose a new dense decoding mechanism as part of the decoder, thus promoting the utilization efficiency of encoded features. Additionally, we construct a compound loss function to train our network to further enhance its robustness in the face of numerous cases. We train and test our network in multiple carotid plaque ultrasound datasets and our method yields the best performance compared to other state-of-the-art methods. Further ablation studies consistently show the advancement of our proposed architecture. Full article

(This article belongs to the Special Issue Big Data Analytics and Intelligent Computation to Advance Novel Applications)

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16 pages, 6080 KiB

Open AccessFeature PaperArticle

Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation

by Dongqian Li, Cien Fan, Lian Zou, Qi Zuo, Hao Jiang and Yifeng Liu

Sensors 2021, 21(23), 7844; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237844 - 25 Nov 2021

Cited by 1 | Viewed by 1439

Abstract

Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple [...] Read more.

Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve this problem, we propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) to guide the inter-level feature fusion towards a more balanced and effective direction. Our overall network architecture is based on the encoder–decoder architecture. In the encoder, we use a relatively deep convolution network to extract rich semantic information. In the decoder, skip-connections are added to connect and fuse low-level spatial features to restore a clearer boundary expression gradually. We add an inter-level feature balanced fusion module to each skip connection. Additionally, to better capture the boundary information, we added a shallower spatial information stream to supplement more spatial information details. Experiments have proved the effectiveness of our module. Our IFBFNet achieved a competitive performance on the Cityscapes dataset with only finely annotated data used for training and has been greatly improved on the baseline network. Full article

(This article belongs to the Special Issue Big Data Analytics and Intelligent Computation to Advance Novel Applications)

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