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A SURVEY OF AUTOMATIC GENERATION OF SOURCE CODE COMMENTS: ALGORITHMS AND TECHNIQUES

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arXiv 2019年

作者： Song, Xiaotao Sun, Hailong Wang, Xu Yan, Jiafei School of Software Taiyuan University of Technology China SKLSDE Lab School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

As an integral part of source code files, code comments help improve program readability and comprehension. However, developers sometimes do not comment on their program code adequately due to the incurred extra efforts, lack of relevant knowledge, unawareness of the importance of code commenting or some other factors. As a result, code comments can be inadequate, absent or even mismatched with source code, which affects the understanding, reusing and the maintenance of software. To solve these problems of code comments, researchers have been concerned with generating code comments automatically. In this work, we aim at conducting a survey of automatic code commenting researches. First, we generally analyze the challenges and research framework of automatic generation of program comments. Second, we present the classification of representative algorithms, the design principles, strengths and weaknesses of each category of algorithms. Meanwhile, we also provide an overview of the quality assessment of the generated comments. Finally, we summarize some future directions for advancing the techniques of automatic generation of code comments and the quality assessment of comments. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

DeepPurpose: A deep learning based drug repurposing toolkit

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arXiv 2020年

作者： Huang, Kexin Fu, Tianfan Xiao, Cao Glass, Lucas M. Sun, Jimeng Health Data Science Harvard T.H. Chan School of Public Health Harvard University BostonMA02115 United States College of Computing Georgia Institute of Technology AtlantaGA30332 United States Analytic Center of Excellence IQVIA CambridgeMA02139 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We present DeepPurpose, a deep learning toolkit for simple and efficient drug repurposing. With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug/target encodings and 50+ novel architectures. We demonstrated DeepPurpose using case studies, including repurposing for COVID-19 where promising candidates under trials are ranked high in our results. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Quantifying fermionic nonlinearity of quantum circuits

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arXiv 2021年

作者： Hakkaku, Shigeo Tashima, Yuichiro Mitarai, Kosuke Mizukami, Wataru Fujii, Keisuke Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Osaka Toyonaka560-8531 Japan NTT Computer and Data Science Laboratories NTT Corporation 3-9-11 Midoricho Musashino180-8585 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka560-0043 Japan JST PRESTO 4-1-8 Honcho Saitama Kawaguchi332-0012 Japan Hirosawa 2-1 Saitama Wako351-0198 Japan Fujitsu Quantum Computing Joint Research Division QIQB Osaka University 1-2 Machikaneyama Toyonaka560-0043 Japan

Variational quantum algorithms (VQAs) have been proposed as one of the most promising approaches to demonstrate quantum advantage on noisy intermediate-scale quantum (NISQ) devices. However, it has been unclear whether VQAs can maintain quantum advantage under the intrinsic noise of the NISQ devices, which deteriorates the quantumness. Here we propose a measure, called fermionic nonlinearity, to quantify the classical simulatability of quantum circuits designed for simulating fermionic Hamiltonians. Specifically, we construct a Monte Carlo type classical algorithm based on the classical simulatability of fermionic linear optics, whose sampling overhead is characterized by the fermionic nonlinearity. As a demonstration of these techniques, we calculate the upper bound of the fermionic nonlinearity of a rotation gate generated by four fermionic modes under the dephasing noise. Moreover, we estimate the sampling costs of the unitary coupled cluster singles and doubles quantum circuits for hydrogen chains subject to the dephasing noise. We find that, depending on the error probability and atomic spacing, there are regions where the fermionic nonlinearity becomes very small or unity, and hence the circuits are classically simulatable. We believe that our method and results help to design quantum circuits for fermionic systems with potential quantum advantages. Copyright © 2021, The Authors. All rights reserved.

关键词： Timing circuits

Green Channel and Top Hat based Image Enhancement for Diabetic Retinopathy Screening

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Journal of Physics: Conference Series 2021年第1期1997卷

作者： N A M Sharif A S M Azhar N H Harun J A Bakar A A Abdullah Y F Chong Data Science Res Lab School of Computing Universiti Utara Malaysia Kedah Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Sports Engineering Research Centre (SERC) Universiti Malaysia Perlis 02600 Arau Perlis Malaysia.

Diabetic Retinopathy remains one of the most feared diabetes complications that could lead to blindness. Image processing techniques have been widely used all around the world for early detection of diabetic retinopathy. However, most techniques used do not focus on the low visual quality problems in the fundus image. Low visual quality of fundus image may lead to difficulty in evaluation by ophthalmologist before reading it out to the patients. Hence, Automated Screening for Diabetic Retinopathy was created to focus on image enhancement of the fundus image. In this study, two main algorithms for image processing have been used which are green channel conversion and top-hat filters. Green channel in fundus image is selected due to better contrast of the features and background compared to the red and blue channel. While Top-hat filter used to details out small features in the fundus image. The evaluation result of the techniques is compared by using Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Entropy calculations to measure quality of the enhanced fundus images. Results of image enhancement techniques implemented has proved that quality of the fundus image is improved.

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Weakly supervised person re-identification

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arXiv 2019年

作者： Meng, Jingke Wu, Sheng Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Accuvision Technology Co. Ltd

In the conventional person re-id setting, it is assumed that the labeled images are the person images within the bounding box for each individual;this labeling across multiple nonoverlapping camera views from raw video surveillance is costly and time-consuming. To overcome this difficulty, we consider weakly supervised person re-id modeling. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. Hence, for a video, there could be multiple video-level labels. We cast this weakly supervised person re-id challenge into a multi-instance multi-label learning (MIML) problem. In particular, we develop a Cross-View MIML (CV-MIML) method that is able to explore potential intraclass person images from all the camera views by incorporating the intra-bag alignment and the cross-view bag alignment. Finally, the CV-MIML method is embedded into an existing deep neural network for developing the Deep Cross-View MIML (Deep CV-MIML) model. We have performed extensive experiments to show the feasibility of the proposed weakly supervised setting and verify the effectiveness of our method compared to related methods on four weakly labeled datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Efficient sequence learning with group recurrent networks

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Efficient sequence learning with group recurrent networks

2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018

作者： Gao, Fei Wu, Lijun Zhao, Li Qin, Tao Cheng, Xueqi Liu, Tie-Yan Institute of Computing Technology Chinese Academy of Sciences China Microsoft Research School of Data and Computer Science Sun Yat-sen University China

ISBN: (纸本)9781948087278

Recurrent neural networks have achieved state-of-The-Art results in many artificial intelligence tasks, such as language modeling, neural machine translation, speech recognition and so on. One of the key factors to these successes is big models. However, training such big models usually takes days or even weeks of time even if using tens of GPU cards. In this paper, we propose an efficient architecture to improve the efficiency of such RNN model training, which adopts the group strategy for recurrent layers, while exploiting the representation rearrangement strategy between layers as well as time steps. To demonstrate the advantages of our models, we conduct experiments on several datasets and tasks. The results show that our architecture achieves comparable or better accuracy comparing with baselines, with a much smaller number of parameters and at a much lower computational cost. © 2018 The Association for Computational Linguistics.

关键词： Speech recognition

MalScan: Fast Market-Wide Mobile Malware Scanning by Social-Network Centrality Analysis

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MalScan: Fast Market-Wide Mobile Malware Scanning by Social-...

IEEE International Conference on Automated Software Engineering (ASE)

作者： Yueming Wu Xiaodi Li Deqing Zou Wei Yang Xin Zhang Hai Jin Cluster and Grid Computing Lab Services Computing Technology and System Lab National Engineering Research Center for Big Data Technology and System School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Texas at Dallas Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use either syntax-based features which can be evaded by transformation attacks or semantic-based features which are usually extracted by performing expensive program analysis. Therefor, in this paper, we propose a lightweight graph-based approach to perform Android malware detection. Instead of traditional heavyweight static analysis, we treat function call graphs of apps as social networks and perform social-network-based centrality analysis to represent the semantic features of the graphs. Our key insight is that centrality provides a succinct and fault-tolerant representation of graph semantics, especially for graphs with certain amount of inaccurate information (e.g., inaccurate call graphs). We implement a prototype system, MalScan, and evaluate it on datasets of 15,285 benign samples and 15,430 malicious samples. Experimental results show that MalScan is capable of detecting Android malware with up to 98% accuracy under one second which is more than 100 times faster than two state-of-the-art approaches, namely MaMaDroid and Drebin. We also demonstrate the feasibility of MalScan on market-wide malware scanning by performing a statistical study on over 3 million apps. Finally, in a corpus of dataset collected from Google-Play app market, MalScan is able to identify 18 zero-day malware including malware samples that can evade detection of existing tools.

关键词： Malware Social networking (online) Feature extraction Semantics Analysis of variance Static analysis Robustness

FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery

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arXiv 2021年

作者： Sun, Xian Wang, Peijin Yan, Zhiyuan Wang, Cheng Diao, Wenhui Chen, Jin Li, Jihao Feng, Yingchao Xu, Tao Weinmann, Martin Hinz, Stefan Fu, Kun Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Information Science and Engineering Xiamen University Xiamen361005 China Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou350003 China Beijing Remote Sensing Information Institute Beijing100011 China Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe Germany

With the rapid development of deep learning, many deep learning based approaches have made great achievements in object detection task. It is generally known that deep learning is a data-driven method. data directly impact the performance of object detectors to some extent. Although existing datasets have included common objects in remote sensing images, they still have some limitations in terms of scale, categories, and images. Therefore, there is a strong requirement for establishing a large-scale benchmark on object detection in high-resolution remote sensing images. In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15,000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery which is named as FAIR1M. We collected remote sensing images with a resolution of 0.3m to 0.8m from different platforms, which are spread across many countries and regions. All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes. Compared with existing detection datasets dedicated to object detection, the FAIR1M dataset has 4 particular characteristics: (1) it is much larger than other existing object detection datasets both in terms of the quantity of instances and the quantity of images, (2) it provides more rich fine-grained category information for objects in remote sensing images, (3) it contains geographic information such as latitude, longitude and resolution, (4) it provides better image quality owing to a careful data cleaning procedure. To establish a baseline for fine-grained object recognition, we propose a novel evaluation method and benchmark fine-grained object detection tasks and a visual classification task using several State-Of-The-Art (SOTA) deep learning based models on our FAIR1M dataset. Experimental results strongly indicate that the FAIR1M dataset is closer to practical application and it is considerably more challeng

关键词： Object recognition

LET'S PLAY POLO: DIRECT OPTIMIZATION OF THE PROBABILITY OF LESION ORIGIN IN PROTON TREATMENT PLANNING FOR LOW-GRADE GLIOMA PATIENTS

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International Journal of Particle Therapy

International Journal of Particle Therapy 2024年 12卷

作者： Tim Ortkamp Habiba Sallem Semi Harrabi Martin Frank Oliver Jäkel Julia Bauer Niklas Wahl Scientific Computing Center (scc) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Helmholtz Information and Data Science School for Health (HIDSS4Health) Karlsruhe Germany Medical Physics In Radiation Oncology (e040) German Cancer Research Center (DKFZ) Heidelberg Germany Department Of Radiation Oncology University Hospital Heidelberg Heidelberg Germany Heidelberg Institute For Radiation Oncology (hiro) National Center for Radiation Research in Oncology (NCRO) Heidelberg Germany Heidelberg Ion Beam Therapy Center (HIT) Heidelberg Germany