International Journal of Computer and Applications

Integration of advanced nanogenerator technology with conventional textile processes fosters the emergence of textile-based nanogenerators (NGs), which will inevitably promote the rapid development and widespread applications of next-generation wearable electronics and multifaceted artificial intelligence systems. NGs endow smart textiles with mechanical energy harvesting and multifunctional self-powered sensing capabilities, while textiles provide a versatile flexible design carrier and extensive wearable application platform for their development. However, due to the lack of an effective interactive platform and communication channel between researchers specializing in NGs and those good at textiles, it is rather difficult to achieve fiber/fabric-based NGs with both excellent electrical output properties and outstanding textile-related performances. To this end, a critical review is presented on the current state of the arts of wearable fiber/fabric-based piezoelectric nanogenerators and triboelectric nanogenerators with respect to basic classifications, material selections, fabrication techniques, structural designs, and working principles, as well as potential applications. Furthermore, the potential difficulties and tough challenges that can impede their large-scale commercial applications are summarized and discussed. It is hoped that this review will not only deepen the ties between smart textiles and wearable NGs, but also push forward further research and applications of future wearable fiber/fabric-based NGs.

Fiber/Fabric-Based Piezoelectric and Triboelectric Nanogenerators for Flexible/Stretchable and Wearable Electronics and Artificial Intelligence.

In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.This article1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

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Search-based software engineering: Trends, techniques and applications

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products; in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with.

/pdf/vuldeepecker-a-deep-learning-based-system-for-vulnerability-3b0bpu5rbk.pdf

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

Despite a decade of active research, there has been a marked lack in clone detection techniques that scale to large repositories for detecting near-miss clones. In this paper, we present a token-based clone detector, SourcererCC, that can detect both exact and near-miss clones from large inter-project repositories using a standard workstation. It exploits an optimized inverted-index to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone. We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks: (1) a big benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (25K projects, 250MLOC) using a standard workstation.

SourcererCC: scaling code clone detection to big-code

Context Reusing software by means of copy and paste is a frequent activity in software development. The duplicated code is known as a software clone and the activity is known as code cloning. Software clones may lead to bug propagation and serious maintenance problems. Objective This study reports an extensive systematic literature review of software clones in general and software clone detection in particular. Method We used the standard systematic literature review method based on a comprehensive set of 213 articles from a total of 2039 articles published in 11 leading journals and 37 premier conferences and workshops. Results Existing literature about software clones is classified broadly into different categories. The importance of semantic clone detection and model based clone detection led to different classifications. Empirical evaluation of clone detection tools/techniques is presented. Clone management, its benefits and cross cutting nature is reported. Number of studies pertaining to nine different types of clones is reported. Thirteen intermediate representations and 24 match detection techniques are reported. Conclusion We call for an increased awareness of the potential benefits of software clone management, and identify the need to develop semantic and model clone detection techniques. Recommendations are given for future research.

http://romisatriawahono.net/lecture/rm/survey/software%20engineering/Software%20Construction/Rattan%20-%20Software%20Clone%20Detection%20-%202013.pdf

Software clone detection: A systematic review

The discovery of antibiotics in the early part of 20th century and their spectacular success in combating infections and deaths created a complacency during the 1960s and 1970s. Over the past six decades, these ‘wonder drugs’ have played a critical role in reducing the global burden of communicable diseases. This success was however, overshadowed by the rapid backlash by the microbes resulting in a “relentless and dizzying rise of antimicrobial resistance”1. We seem to have come a full circle from the pre-antibiotic phase through a stage of euphoria to a seemingly frightening era of patients infected with multidrug resistant bacteria desperate for the elusive magic bullet2. In the recent past, especially, the emergence and spread of resistance in several microorganisms have rendered the management of many infectious diseases difficult.

The growing challenge of antimicrobial resistance in the South-East Asia Region - Are we losing the battle?

Performance of any search engine relies heavily on its Web crawler. Web crawlers are the programs that get webpages from the Web by following hyperlinks. These webpages are indexed by a search engine and can be retrieved by a user query. In the area of Web crawling, we still lack an exhaustive study that covers all crawling techniques. This study follows the guidelines of systematic literature review and applies it to the field of Web crawling. We used the standard procedure of carrying out a systematic literature review on 248 studies from a total of 1488 articles published in 12 leading journals and other premier conferences and workshops. Existing literature about the Web crawler is classified into different key subareas. Each subarea is further divided according to the techniques being used. We analyzed the distribution of various articles using multiple criteria and depicted conclusions. Various studies that use open source Web crawlers are also reported. We have highlighted future areas of research. We call for an increased awareness in various fields of the Web crawler and identify how techniques from other domains can be used for crawling the Web. Limitations and recommendations for future are also discussed. WIREs Data Mining Knowl Discov 2017, 7:e1218. doi: 10.1002/widm.1218

A survey of Web crawlers for information retrieval

Recognition of an object from an image or image sequences is an important task in computer vision. It is an important low-level image processing operation and plays a crucial role in many real-world applications. The challenges involved in object recognition are multi-model, multi-pose, complicated background, and depth variations. Recently developed methods have dealt with these challenges and have reported remarkable results for 3D objects. In this paper, a comprehensive overview of recent advances in 3D object recognition using Convolutional Neural Networks (CNN) has been presented. Along with the latest progress in 3D images, general overview of object recognition of 2D, 2.5D, and 3D images is presented.

Rajesh Bhatia

Papers

Software clone detection: A systematic review

The growing challenge of antimicrobial resistance in the South-East Asia Region - Are we losing the battle?

A survey of Web crawlers for information retrieval

3D convolutional neural network for object recognition: a review

Keyword query based focused Web crawler