Defect prediction has been an active research area for over four decades. Despite numerous studies on defect prediction, the potential value of defect prediction in practice remains unclear. To ...address this issue, we performed a mixed qualitative and quantitative study to investigate what practitioners think, behave and expect in contrast to research findings when it comes to defect prediction. We collected hypotheses from open-ended interviews and a literature review of defect prediction papers that were published at ICSE, ESEC/FSE, ASE, TSE and TOSEM in the last 6 years (2012-2017). We then conducted a validation survey where the hypotheses became statements or options of our survey questions. We received 395 responses from practitioners from over 33 countries across five continents. Some of our key findings include: 1) Over 90 percent of respondents are willing to adopt defect prediction techniques. 2) There exists a disconnect between practitioners' perceptions and well supported research evidence regarding defect density distribution and the relationship between file size and defectiveness. 3) 7.2 percent of the respondents reveal an inconsistency between their behavior and perception regarding defect prediction. 4) Defect prediction at the feature level is the most preferred level of granularity by practitioners. 5) During bug fixing, more than 40 percent of the respondents acknowledged that they would make a "work-around" fix rather than correct the actual error-causing code. Through a qualitative analysis of free-form text responses, we identified reasons why practitioners are reluctant to adopt defect prediction tools. We also noted features that practitioners expect defect prediction tools to deliver. Based on our findings, we highlight future research directions and provide recommendations for practitioners.
Interstitial lung disease (ILD) is frequent in patients with rheumatoid arthritis (RA) and is a potentially life-threatening complication with significant morbidity and mortality. This meta-analysis ...aims to systematically determine the factors associated with the development of rheumatoid arthritis-related interstitial lung disease (RA-ILD).
All primary studies which reported the factors associated with of RA-ILD were eligible for the review except case reports. The Cochrane Library, PubMed, Embase, Web of Science, Chinese Biological Medicine Database (CBM), China National Knowledge Infrastructure (CNKI), and WANFANG electronic databases were searched through to December 30, 2022, for studies investigating the factors associated with RA-ILD. The methodological quality assessment of the eligible studies was performed using the Newcastle-Ottawa Scale (NOS). 2 reviewers extracted relevant data independently. Then, weighed mean differences (WMDs) or pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were obtained for the relationships between the factors and RA-ILD. The statistical meta-analysis, subgroup and sensitivity analyses were performed using the Review Manager 5.3, and publication bias with Egger's test were performed using the Stata12.0 software.
A total of 22 articles were screened for a meta-analysis which involved 1887 RA-ILD patients and 8066 RA without ILD patients. Some identified factors that were associated with an increased risk of RA-ILD included male sex (OR = 1.92, 95% CI: 1.54-2.39; P < 0.00001), older age (WMD = 5.77 years, 95% CI: 3.50-8.04; P < 0.00001), longer duration of RA (WMD = 0.80 years, 95% CI 0.12-1.47; P = 0.02), older age at onset of RA (WMD = 6.41 years, 95% CI: 3.17-9.64; P = 0.0001), smoking (OR = 1.69, 95% CI: 1.30-2.18; P < 0.0001). Five factors of laboratory items associated with the development of RA-ILD were evaluated in the meta-analysis. Compared with RA without ILD patients, positive rheumatoid factor (RF) (OR = 1.72, 95% CI: 1.47-2.01; P < 0.00001) and positive anti-citrullinated protein antibodies (ACPA) (OR = 1.58, 95% CI: 1.31-1.90; P < 0.00001) increased the risk of RA-ILD. Meanwhile, RF titer (WMD = 183.62 (IU/mL), 95% CI: 66.94-300.30; P = 0.002) and ACPA titer (WMD = 194.18 (IU/mL), 95% CI: 115.89-272.47; P < 0.00001) were significantly associated with increased risk of RA-ILD. Elevated erythrocyte sedimentation rate (ESR) (WMD = 7.41 (mm/h), 95% CI: 2.21-12.61; P = 0.005) and C-reactive protein (CRP) (WMD = 4.98 (mg/L), 95% CI: 0.76-9.20; P = 0.02) were also significantly associated with the development of the RA-ILD, whereas antinuclear antibody (ANA) positive status was not significantly associated with increased risk of RA-ILD (OR = 1.27, 95% CI: 1.00-1.60; P = 0.05).
This meta-analysis showed that male gender, older age, longer duration of RA, older age at onset of RA, smoking, positive RF, positive ACPA, elevated RF titer, elevated ACPA titer, higher ESR and higher CRP were associated with RA-ILD.
Convolutional Neural Network (CNN) has gained attractions in image analytics and speech recognition in recent years. However, employing CNN for classification of graphs remains to be challenging. ...This paper presents the Ngram graph-block based convolutional neural network model for classification of graphs. Our Ngram deep learning framework consists of three novel components. First, we introduce the concept of n-gram block to transform each raw graph object into a sequence of n-gram blocks connected through overlapping regions. Second, we introduce a diagonal convolution step to extract local patterns and connectivity features hidden in these n-gram blocks by performing n-gram normalization. Finally, we develop deeper global patterns based on the local patterns and the ways that they respond to overlapping regions by building a n-gram deep learning model using convolutional neural network. We evaluate the effectiveness of our approach by comparing it with the existing state of art methods using five real graph repositories from bioinformatics and social networks domains. Our results show that the Ngram approach outperforms existing methods with high accuracy and comparable performance.
•Spatial distribution and driving factors of RRS growth are explored using GDM.•Spatial heterogeneity exists among regions with different socio-economic features.•Air pollution and some ...socio-economic factors are more important in RRS promotion.•BEP can be an alternative incentive tool for fiscal subsidy to promote RRS growth.•Synergistic effect between factors is crucial for RRS growth and future development.
The growth of Residential rooftop solar (RRS) in some western countries has predominantly been driven by individual or market behaviour and has been extensively studied. However, the development landscape of RRS in China differs, and its driving mechanisms remain unclear. To address this research gap, we investigate the spatial distribution pattern and driving factors of RRS growth using city-level data on RRS installation. Employing the Geographical Detector Model, we calculate indicators to identify the contributions of various socio-economic factors to RRS growth and the strength of their interactions. Our key findings include: 1) significant spatial heterogeneity in RRS growth across regions with different natural and socio-economic characteristics, which impact RRS growth in two patterns: one-way and inverted U-shaped; 2) although solar radiation abundancy is important, air pollution and certain socio-economic factors appear more influential, be it comparing between the eastern and western China, or north and south; 3) the significance of fiscal subsidies has diminished, but benchmark electricity prices (BEP) could serve as a useful alternative; 4) substantial synergistic effects exist between different factors, with environmental and demographic factors displaying particularly strong synergies with others, suggesting that they are essential considerations for future RRS planning. Our findings contribute to a comprehensive understanding of RRS development in China and hold critical implications for future policy design.
The recent emergence of mobile cloud computing has enabled mobile users to offload computing tasks from mobile devices to nearby cloudlets, so as to reduce energy consumption and improve application ...performance. In this paper, we consider the problem of maximizing the profit of the cloudlets' managing platform that receives computing requests from mobile users and fulfils these requests by leveraging computing service of participating cloudlets. However, it is very challenging to maximize the operating profit for such a managing platform, due to unpredictable arrival of user requests, dynamic participation of mobile cloudlets, and complexity in computing resource allocations. Based on the Lyapunov optimization technique combined with the technique of weight perturbation, we introduce a new stochastic control algorithm that makes online decisions on computing request admission and dispatching, computing service purchasing, and computing resource allocation. Different from traditional techniques, this algorithm does not require any statistical knowledge of relevant system dynamics, and is efficient for implementation in practice. Theoretical analysis and simulation results have demonstrated both the profit optimality and the system stability achieved by the proposed control algorithm.
In modern commerce, both frequent changes of custom demands and the specialization of the business process require the capacity of modeling business processes for enterprises effectively and ...efficiently. Traditional methods for improving business process modeling, such as workflow mining and process retrieval, still requires much manual work. To address this, based on the structure of a business process, a method called workflow recommendation technique is proposed in this paper to provide process designers with support for automatically constructing the new business process that is under consideration. In this paper, with the help of the minimum depth-first search (DFS) codes of business process graphs, we propose an efficient method for calculating the distance between process fragments and select candidate node sets for recommendation purpose. In addition, a recommendation system for improving the modeling efficiency and accuracy was implemented and its implementation details are discussed. At last, based on both synthetic and real-world datasets, we have conducted experiments to compare the proposed method with other methods and the experiment results proved its effectiveness for practical applications.
Films of regenerated silk fibroin (RSF) are usually brittle and weak, which prevents its wide application as a structural material. To improve the mechanical properties of RSF film, uniaxial ...extension under swollen conditions was employed to introduce preferred orientation of molecular chains of silk fibroin. Such a prestretching treatment resulted in the strain at break, ultimate stress, Young's modulus, and energy to break along the predrawn direction of the RSF film increasing from approximate 5%, 90 MPa, 2.7 GPa, and 2.1 kJ/kg to 35%, 169 MPa, 3.5 GPa, and 38.9 kJ/kg, respectively, which is an attractive combination of strength and toughness. The mechanism of these property enhancements was investigated using techniques such as small-angle X-ray scattering, wide-angle X-ray diffraction, atomic force microscopy, and dynamic mechanical analysis.
When segmenting massive amounts of remote sensing images collected from different satellites or geographic locations (cities), the pre-trained deep learning models cannot always output satisfactory ...predictions. To deal with this issue, domain adaptation has been widely utilized to enhance the generalization abilities of the segmentation models. Most of the existing domain adaptation methods, which based on image-to-image translation, firstly transfer the source images to the pseudo-target images, adapt the classifier from the source domain to the target domain. However, these unidirectional methods suffer from the following two limitations: (1) they do not consider the inverse procedure and they cannot fully take advantage of the information from the other domain, which is also beneficial, as confirmed by our experiments; (2) these methods may fail in the cases where transferring the source images to the pseudo-target images is difficult. In this paper, in order to solve these problems, we propose a novel framework BiFDANet for unsupervised bidirectional domain adaptation in the semantic segmentation of remote sensing images. It optimizes the segmentation models in two opposite directions. In the source-to-target direction, BiFDANet learns to transfer the source images to the pseudo-target images and adapts the classifier to the target domain. In the opposite direction, BiFDANet transfers the target images to the pseudo-source images and optimizes the source classifier. At test stage, we make the best of the source classifier and the target classifier, which complement each other with a simple linear combination method, further improving the performance of our BiFDANet. Furthermore, we propose a new bidirectional semantic consistency loss for our BiFDANet to maintain the semantic consistency during the bidirectional image-to-image translation process. The experiments on two datasets including satellite images and aerial images demonstrate the superiority of our method against existing unidirectional methods.
The increasing number of Internet of Thing (IoT) devices and services makes it convenient for people to sense the real world and makes optimal decisions or complete complex tasks with them. However, ...the latency brought by unstable wireless networks and computation failures caused by constrained resources limit the development of IoT. A popular approach to solve this problem is to establish an IoT service provision system based on a mobile edge computing (MEC) model. In the MEC model, plenty of edge servers are placed with access points via wireless networks. With the help of cached services on edge servers, the latency can be reduced, and the computation can be offloaded. The cache services must be carefully selected so that many requests can by satisfied without overloading resources in edge servers. This paper proposes an optimized service cache policy by taking advantage of the composability of services to improve the performance of service provision systems. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of these IoT services.
This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two ...phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.
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