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SSRN 2022年

作者： Guo, Juncai Liu, Jin Wan, Yao Zhao, Yanjie Li, Li Liu, Kui Klein, Jacques Bissyandé, Tegawendé F. School of Computer Science Wuhan University Wuhan China Key Laboratory of Network Assessment Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Faculty of Information Technology Monash University VIC Australia Huawei Software Engineering Application Technology Lab Hangzhou China SnT Centre University of Luxembourg 1855 Luxembourg

Code comment generation, which aims to describe the functionality of source code in natural language, plays an important role in automatic software development. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on Abstract Syntax Trees (ASTs). However, they can not well capture both the long-distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present to the community a prototype tool called PYSCRIBE, which expands the Transformer model to a new encoder-decoder architecture to incorporate triplet positions into the node-level and edge-level features of AST towards automatically generating comments for Python code. To the best of our knowledge, this paper makes the first attempt to consider the edges of AST as an explicit component for better code representation, as to generate higher qualified comments. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. In the decoder, we perform a two-stage decoding process over the extracted node and edge features to generate comments. To evaluate the effectiveness of PYSCRIBE, we resort to a large dataset of code-comment pairs by mining Jupyter notebooks from GitHub, for which we have made it publicly available to support further studies. The evaluation results show that PYSCRIBE is indeed effective, outperforming the state-of-the-art by achieving an average BLEU score (i.e., av-BLEU) of ≈0.28. © 2022, The Authors. All rights reserved.

关键词： Large dataset

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Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020年第1期17卷 327-333页

作者： Wong, Ka-Chun Yan, Shankai Lin, Qiuzhen Li, Xiangtao Peng, Chengbin Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong518060 China Department of Computer Science and Information Technology Northeast Normal University Changchun Jilin130117 China Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo Zhejiang315211 China

Transcription factors (TFs) are the major components of human gene regulation. In particular, they bind onto specific DNA sequences and regulate neighborhood genes in different tissues at different developmental stages. Non-synonymous single nucleotide polymorphisms on its protein-coding sequences could result in undesired consequences in human. Therefore, it is necessary to develop methods for predicting any abnormality among those non-synonymous single nucleotide polymorphisms. To address it, we have developed and compared different strategies to predict deleterious non-synonymous single nucleotide polymorphisms (also known as missense mutations) on the protein-coding sequences of human TFs. Taking advantage of evolutionary conservation signals, we have developed and compared different classifiers with different feature sets as computed from different evolutionarily related sequence collections. The results indicate that the classic ensemble algorithm, Adaboost with decision stumps, with orthologous sequence collection, has performed the best (namely, TFmedic). We have further compared TFmedic with other state-of-the-arts methods (i.e., PolyPhen-2 and SIFT) on PolyPhen-2's own datasets, demonstrating that TFmedic can outperform the others. As applications, we have further applied TFmedic to all possible missense mutations on all human transcription factors;the proteome-wide results reveal interesting insights, consistent with the existing physiochemical knowledge. A case study with the actual 3D structure is conducted, revealing how TFmedic can be contributed to protein-DNA binding complex studies. © 2004-2012 IEEE.

关键词： Data mining

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Automatic Topic Classification And Document Clustering Using Lda Based Machine Learning Techniques

Automatic Topic Classification And Document Clustering Using...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： K. Yuvaraj Nipun Setia Sangeetha S Prakhar Goyal Thomas P K Vishal A. Meshram Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India Adjunct Faculty School of Management - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Since more and more information is being stored in digital formats (such as databases, journals, libraries, and papers), it is crucial that this information be put to good use. However, it might be challenging to extract meaningful insights from the data because of its poor organisation, ambiguity, and lack of precision. Data may be correctly categorised to allow for separate research on different types of data, which will help with this problem. Because of this, researchers in disciplines like mathematics and computer science have been trying to figure out how to fix the issue. Material mining is a branch of machine learning that helps cope with unstructured and disorganised data by sifting through it for useful insights. Text mining is a subfield of data mining that focuses on discovering patterns and extracting structured attributes from unstructured text. To successfully analyse such data, supervised learning is combined with unsupervised learning. It has become common practise to employ Latent Dirichlet Allocation (LDA), a probabilistic approach to topic modelling, when classifying documents. As an unsupervised method, however, traditional LDA's findings may shift depending on which articles come first in the analysis. In this study, we provide a comprehensive strategy for avoiding the order effect as well as the unsupervised probabilistic character of the problem. The proposed framework achieved a success rate of 95.56% & 95.23% when evaluated on two separate datasets, demonstrating its better classification capabilities. Furthermore, the subjects that were extracted from the classified clusters are highly relevant to the domain classifications.

关键词： Text mining Machine learning algorithms Databases Machine learning Feature extraction Probabilistic logic Mathematics Libraries Data mining Unsupervised learning

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Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

arXiv

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Knowledge Graph Curation from Text Via Ontologies

Knowledge Graph Curation from Text Via Ontologies

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Vaibhava R Lakshmi Gerard Deepak A Santhanavijayan S Radha Department of Computer Technology Madras Institute of Technology Chennai India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirapalli India School of Advanced Sciences Vellore Institute of Technology Chennai India

ISBN: (纸本)9781665489638

Huge-scale knowledge graphs have become widespread on the web and are useful for a plethora of tasks. Knowledge graphs which are powered by machine learning use Natural Language Processing to build an extensive view of nodes, edges and labels by semantic enrichment in order to automate tasks like data structuring, text analysis and merging of data models. This research study put forth a semantically-driven and knowledge-centric strategy, which integrates machine learning and semantic inference for curating knowledge graphs from text using ontologies. The proposed framework amalgamates knowledge from the sources: Wikidata, DBpedia and Mediawiki to instill heterogeneity. The proffered framework transcends the baseline models by yielding an accuracy value of 98.22Cumulative Gain (NDCG) value of 0.97.

关键词： Knowledge engineering Text analysis Semantics Merging Machine learning Ontologies Fish

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Image Classification for Potato Plant Leaf Disease Detection using Deep Learning

Image Classification for Potato Plant Leaf Disease Detection...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： S. Durai T. Sujithra M. Mohamed Iqbal Dept. of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of CSE Manipal Institute of Technology Manipal Academy of Higher Education Manipal Senior Grade 1 School of Computer Science and Engineering VIT-AP University Amaravati

Identifying potato leaf diseases at an early stage is a difficult task due to the variability in crop species, crop disease symptoms, and environmental factors. To overcome this challenge, machine learning techniques have been developed. However, current models are limited to specific regions and cannot detect diseases in various crop species. This research proposes a multi-level deep learning model to recognize potato leaf diseases. The model uses a unique convolutional neural network to detect early blight and late blight potato infections from leaf images after extracting potato leaves from plant images using ResNet50 image segmentation. The model is trained and tested using a potato leaf disease dataset, achieving 99.75 percent accuracy. Furthermore, it outperforms state-of-the-art models in terms of accuracy and computational cost.

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Image Denoising for Smart Laser Osteotomy Using Deep Learning-based Fast Optical Coherence Tomography (OCT)

Image Denoising for Smart Laser Osteotomy Using Deep Learnin...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Shubhangi N. Ghate D. Kalpanadevi K Amudha Priya Velayutham Balu S Mohd Asif Shah Department of Electronics and Telecommunication Engineering Ramrao Adik Institute of Technology D. Y. Patil Deemed to be University Nerul Navi Mumbai Maharashtra India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Koil Srivilliputhur Tamil Nadu India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Thottiyam Trichy Tamilnadu India Department of Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tiruchengode Namakkal Tamilnadu India Adjunct Faculty University School of Business Chandigarh University Gharuan Mohali Punjab India

With laser osteotomy, patients may expect clean incisions and minimal damage to bone structure. With the smart laser osteotomy method, this study has suggested using Optical Coherence Tomography (OCT) to track the ablation as it occurred. The OCT image is useful for distinguishing different types of tissue and giving the ablation laser guidance on where to focus its energy to avoid damaging vital structures like bone marrow and nerves. Furthermore, the application relies on the quality of the OCT picture to determine the tissue classifier’s exactness. Because of this, establishing a reliable feedback system depends heavily on picture denoising. The frame averaging approach is widely utilized as a means of picture denoising in Optical Coherence Tomography (OCT). The more photographs that are used, the better the final image will seem. However, this method requires more time to acquire data and makes the system more vulnerable to motion abnormalities. To get around these restrictions, a deep learning denoising algorithm is used to simulate the frame-averaging approach. The generated image was of higher quality than traditional digital filtering techniques while maintaining the same level of detail as when using a frame-averaging approach. This study also analyzes how the proposed strategy impacts the precision of the tissue classifier model to inform the ablation laser’s adjustments. After applying image denoising, a significant improvement has been observed in the tissue classifier’s performance. It is also evident that the classifier trained with deep learning denoised images was as accurate as the classifier trained with frame-averaged images.

关键词： Deep learning Time-frequency analysis Optical coherence tomography Noise reduction Bones Real-time systems Time measurement

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Fuzzy Comprehensive Evaluation of Machining Process Planning for Green Manufacturing

Fuzzy Comprehensive Evaluation of Machining Process Planning...

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Chinese Automation Congress (CAC)

作者： Zhaoming Chen Jinsong Zou Wei Wang Chongqing University Chongqing School University of Chinese Academy of Sciences Chongqing China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

Given the variety of planning schemes and the elements that influence green manufacturing, a multi-level assessment index system based on five features is developed: machining time, cost of manufacturing, processing quality, resource consumption, and environmental conservation. After that, a fuzzy evaluation approach based on the Hierarchical Entropy Weight is constructed for evaluating process planning schemes. Using modified fuzzy operation principles, the full assessment of multi-process planning schemes is done, and the optimal one is finally determined. Finally, planning plans for vehicle parts are employed in order to validate their feasibility and efficacy of this approach. The results indicate that it is closer in accordance with the actual production and may give the most cost-effective and ecologically friendly processing scheme.

关键词： Green manufacturing Costs Process planning Green products Machining Entropy Planning

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Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles

Data Analytics for Core Temperature Estimation in Battery Ma...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Ahilya Chhetri Sumukh Surya Mohan Krishna S Vidya Rao Engineering and Technology Cactus Communications Mumbai India Bosch Global Software Technologies Pvt. Limited Bengaluru India Department of Electrical and Electronics Engineering ACED Alliance University Bengaluru India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Compared to conventional means of transport thatuse fossil fuels, electric vehicles are known to reduce pollution levels as they take power from renewable sources of energy stored in energy storage devices like batteries and fuel cells. Core temperature estimation of batteriesis extremely important inbattery management systems for preventing thermal runaway and ensuring safe operation. In this study, core temperature is estimated using a Kalman filter for two different battery chemistries viz; lithium polymer and lithium iron phosphate using a second-order thermal model. Further, a linear regression model is applied to verify the prediction over the trained and tested dataset. The lithium iron phosphate prediction curvehad a fit of approximately 82-83%, whilelithium polymerhad a fit of 72-82% during the charging and discharging of OCV-SOC variation.

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Multi-level Cross-modal Feature Alignment via Contrastive Learning towards Zero-shot Classification of Remote Sensing Image Scenes

arXiv

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arXiv 2023年

作者： Liu, Chun Ma, Suqiang Li, Zheng Yang, Wei Han, Zhigang School of Computer and Information Engineering Henan Key Laboratory of Big Data Analysis and Processing Henan Engineering Laboratory of Spatial Information Processing Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China College of Geography and Environmental Science Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

Zero-shot classification of image scenes which can recognize the image scenes that are not seen in the training stage holds great promise of lowering the dependence on large numbers of labeled samples. To address the zero-shot image scene classification, the cross-modal feature alignment methods have been proposed in recent years. These methods mainly focus on matching the visual features of each image scene with their corresponding semantic descriptors in the latent space. Less attention has been paid to the contrastive relationships between different image scenes and different semantic descriptors. In light of the challenge of large intra-class difference and inter-class similarity among image scenes and the potential noisy samples, these methods are susceptible to the influence of the instances which are far from these of the same classes and close to these of other classes. In this work, we propose a multi-level cross-modal feature alignment method via contrastive learning for zero-shot classification of remote sensing image scenes. While promoting the single-instance level positive alignment between each image scene with their corresponding semantic descriptors, the proposed method takes the cross-instance contrastive relationships into consideration, and learns to keep the visual and semantic features of different classes in the latent space apart from each other. Extensive experiments have been done to evaluate the performance of the proposed method. The results show that our proposed method outperforms state of the art methods for zero-shot remote sensing image scene classification. All the code and data are available at github https://***/masuqiang/MCFA-Pytorch. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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