Computer Sciences Commons^™

New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger

Theses

Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …

Deepcommenter: A Deep Code Comment Generation Tool With Hybrid Lexical And Syntactical Information, Boao Li, Meng Yan, Xin Xia, Xing Hu, Ge Li, David Lo

Research Collection School Of Computing and Information Systems

As the scale of software projects increases, the code comments are more and more important for program comprehension. Unfortunately, many code comments are missing, mismatched or outdated due to tight development schedule or other reasons. Automatic code comment generation is of great help for developers to comprehend source code and reduce their workload. Thus, we propose a code comment generation tool (DeepCommenter) to generate descriptive comments for Java methods. DeepCommenter formulates the comment generation task as a machine translation problem and exploits a deep neural network that combines the lexical and structural information of Java methods. We implement DeepCommenter in …

Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn

Research Collection School Of Computing and Information Systems

Android platform has dominated the smart phone market for years now and, consequently, gained a lot of attention from attackers. Malicious apps (malware) pose a serious threat to the security and privacy of Android smart phone users. Available approaches to detect mobile malware based on machine learning rely on features extracted with static analysis or dynamic analysis techniques. Dif- ferent types of machine learning classi ers (such as support vector machine and random forest) deep learning classi ers (based on deep neural networks) are then trained on extracted features, to produce models that can be used to detect mobile malware. …

Exploring The Efficacy Of Transfer Learning In Mining Image‑Based Software Artifacts, Natalie Best, Jordan Ott, Erik J. Linstead

Engineering Faculty Articles and Research

Background

Transfer learning allows us to train deep architectures requiring a large number of learned parameters, even if the amount of available data is limited, by leveraging existing models previously trained for another task. In previous attempts to classify image-based software artifacts in the absence of big data, it was noted that standard off-the-shelf deep architectures such as VGG could not be utilized due to their large parameter space and therefore had to be replaced by customized architectures with fewer layers. This proves to be challenging to empirical software engineers who would like to make use of existing architectures without …

Novel Deep Learning Methods Combined With Static Analysis For Source Code Processing, Duy Quoc Nghi Bui

Dissertations and Theses Collection (Open Access)

It is desirable to combine machine learning and program analysis so that one can leverage the best of both to increase the performance of software analytics. On one side, machine learning can analyze the source code of thousands of well-written software projects that can uncover patterns that partially characterize software that is reliable, easy to read, and easy to maintain. On the other side, the program analysis can be used to define rigorous and unique rules that are only available in programming languages, which enrich the representation of source code and help the machine learning to capture the patterns better. …

How Are Deep Learning Models Similar? An Empirical Study On Clone Analysis Of Deep Learning Software, Xiongfei Wu, Liangyu Qin, Bing Yu, Xiaofei Xie, Lei Ma, Yinxing Xue, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been successfully applied to many cutting-edge applications, e.g., image processing, speech recognition, and natural language processing. As more and more DL software is made open-sourced, publicly available, and organized in model repositories and stores (Model Zoo, ModelDepot), there comes a need to understand the relationships of these DL models regarding their maintenance and evolution tasks. Although clone analysis has been extensively studied for traditional software, up to the present, clone analysis has not been investigated for DL software. Since DL software adopts the data-driven development paradigm, it is still not clear whether and to what extent …

Psc2code: Denoising Code Extraction From Programming Screencasts, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Minghui Wu, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Programming screencasts have become a pervasive resource on the Internet, which help developers learn new programming technologies or skills. The source code in programming screencasts is an important and valuable information for developers. But the streaming nature of programming screencasts (i.e., a sequence of screen-captured images) limits the ways that developers can interact with the source code in the screencasts. Many studies use the Optical Character Recognition (OCR) technique to convert screen images (also referred to as video frames) into textual content, which can then be indexed and searched easily. However, noisy screen images significantly affect the quality of source …

Is Using Deep Learning Frameworks Free?: Characterizing Technical Debt In Deep Learning Frameworks, Jiakun Liu, Qiao Huang, Xin Xia, Emad Shihab, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Developers of deep learning applications (shortened as application developers) commonly use deep learning frameworks in their projects. However, due to time pressure, market competition, and cost reduction, developers of deep learning frameworks (shortened as framework developers) often have to sacrifice software quality to satisfy a shorter completion time. This practice leads to technical debt in deep learning frameworks, which results in the increasing burden to both the application developers and the framework developers in future development.In this paper, we analyze the comments indicating technical debt (self-admitted technical debt) in 7 of the most popular open-source deep learning frameworks. Although framework …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations & Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Towards Characterizing Adversarial Defects Of Deep Learning Software From The Lens Of Uncertainty, Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao, Meng Sun

Research Collection School Of Computing and Information Systems

Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers from quality issues, which raises great concern especially in the context of safety- and security-critical scenarios. Adversarial examples (AEs) represent a typical and important type of defects needed to be urgently addressed, on which a DL software makes incorrect decisions. Such defects occur through either intentional attack or physical-world noise perceived by input sensors, potentially hindering further industry deployment. The intrinsic uncertainty nature of deep learning decisions can be a fundamental reason for its incorrect behavior. …

Are The Code Snippets What We Are Searching For? A Benchmark And An Empirical Study On Code Search With Natural-Language Queries, Shuhan Yan, Hang Yu, Yuting Chen, Beijun Shen

Research Collection School Of Computing and Information Systems

Code search methods, especially those that allow programmers to raise queries in a natural language, plays an important role in software development. It helps to improve programmers' productivity by returning sample code snippets from the Internet and/or source-code repositories for their natural-language queries. Meanwhile, there are many code search methods in the literature that support natural-language queries. Difficulties exist in recognizing the strengths and weaknesses of each method and choosing the right one for different usage scenarios, because (1) the implementations of those methods and the datasets for evaluating them are usually not publicly available, and (2) some methods leverage …