Accurately predicting the future motions of traffic agents is essential for autonomous systems. Despite the significant success of existing motion forecasting methods based on supervised learning, they still exhibit t...
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As the economic value of cryptocurrencies continues to ascend, an increasing number of cybercriminals exploit malicious browser scripts to commandeer the system and network resources of victims for unauthorized crypto...
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In the fast-evolving field of medical image analysis, deep learning (DL)-based methods have achieved tremendous success. However, these methods require plaintext data for training and inference stages, raising privacy...
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6G native artificial intelligence (AI) networks are expected to support diverse vertical industries and offer countless emerging AI services. To satisfy stringent requirements of diversified services, network slicing ...
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data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augment...
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During software maintenance and evolution, developers spend more than half of their time on code comprehension activities. In order to understand an unfamiliar code base, they would naturally ask different types of qu...
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During software maintenance and evolution, developers spend more than half of their time on code comprehension activities. In order to understand an unfamiliar code base, they would naturally ask different types of questions related to code snippets and try to find the answers. In this paper, we conduct an initial work to explore the possibility of automatic question generation for program comprehension. We construct a large-scale data set containing pairs of source code and questions that are automatically transformed from inline comments based on dependency analysis and semantic role labeling. We also build a comprehensive taxonomy of question types so as to generate questions concerning different aspects of code snippets, such as purpose, implementation details and so on. Then, we propose a deep learning-based prototype CodeQG to automatically generates multiple types of questions for code snippets. We evaluate CodeQG by using both typical performance metrics and manual evaluation. The results show that (1) we can achieve a value of 42.02 on BLEU4 and 60.81 on ROUGE-L for the generated questions;(2) overall, the questions are very correct in grammatical, semantic and format;(3) the questions are related to the corresponding code snippet and are helpful for developers in source code comprehension activities. Our work gives insights into automatically generating multiple types of questions for code comprehension. We expect this exploration will improve the applicability and generality of machine code comprehension.
The proceedings contain 38 papers. The topics discussed include: developing IT strategic transformation of smart village concept for Indonesian village model;social media analysis on aquaculture supplychain management...
ISBN:
(纸本)9781665463874
The proceedings contain 38 papers. The topics discussed include: developing IT strategic transformation of smart village concept for Indonesian village model;social media analysis on aquaculture supplychain management: a case study on freshwater lobsters;selection of the best color space for image steganography with the least significant bit method;defense-in-depth security strategy in Log4j vulnerability analysis;exploration of ECG-based real-time arrhythmia detection: a systematic literature review;prediction model of mortality with respiratory rate, oxygen saturation and heart rate using logistic regression;application of reverse engineering and simulation in the design of a pontoon drum for coconut shell combustion;optimization of Hijaiyah letter handwriting recognition model based on deep learning;and event-driven architecture to improve performance and scalability in microservices-based systems.
The training efficacy of finger vein models is influenced by varied device image acquisition characteristics, collector gestures, and contact modes. However, conventional approaches relying on vast data volumes face c...
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The proceedings contain 20 papers. The special focus in this conference is on data Analytics and learning. The topics include: Nitrogen Deficiency and Yield Estimation in Paddy Field;medical Image Compression Using Hu...
ISBN:
(纸本)9789819963454
The proceedings contain 20 papers. The special focus in this conference is on data Analytics and learning. The topics include: Nitrogen Deficiency and Yield Estimation in Paddy Field;medical Image Compression Using Huffman Coding for Tiff Images;Efficient Wavelet Based Denoising Technique Combined with Features of Cyclespinning and BM3D for Grayscale and Color Images;PBRAMEC: Prioritized Buffer Based Resource Allocation for Mobile Edge Computing Devices;surface Water Quality Analysis Using IoT;children Facial Growth Pattern Analysis Using Deep Convolutional Neural Networks;classification of Forged Logo Images;detection, Classification and Counting of Moving Vehicles from Videos;two-Stage Word Spotting Scheme for Historical Handwritten Devanagari Documents;face Recognition Using Sketch Images;3D Object Detection in Point Cloud Using Key Point Detection Network;IRIS and Face-Based Multimodal Biometrics Systems;a Survey on the Detection of Diseases in Plants Using the Computer Vision-Based Model;an Approach to Conserve Wildlife Habitat by Predicting Forest Fire Using machinelearning Technique;A Method to Detect Phishing Websites Using Distinctive URL Characteristics by Employing machinelearning Technique;aquaculture Monitoring System: A Prescriptive Model;machinelearning-Based Pattern Recognition Models for Image Recognition and Classification;a Review of Silk Farming Automation Using Artificial Intelligence, machinelearning, and Cloud-Based Solutions.
This paper describes a deep learning method such as the long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (Bi-GRU) to design for forecasting in...
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