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Sustainable Power and Energy Conference (iSPEC)

作者： Noelle Y. L. Wong Dipti Srinivasan Eng Yeow Cheu Zhonglin Chiam Data Science & Analytics Sembcorp Utilities Pte Ltd Singapore Department of Electrical & Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350395075

ISBN: (纸本)9798350395082

This study underscores the potential of variational representation as a tool for uncertainty estimation in the energy market, which would help Independent System Operators (ISO) in risk assessment and demand response. Forecasts of future supply curves are also very valuable to energy suppliers seeking to optimize bidding strategies in an oligopolistic power market characterized by information privacy and volatility of individual bidding behaviors and variability in renewable energy generation. However, traditional deterministic modelling methods may not adequately capture the inherent uncertainty and volatility of the electricity market. Our variational encoding technique produces a resultant embedding space that is stochastic and exhibits substantial interpretability, which we exploit to demonstrate the model's transparency. By using the learned posterior probability distribution of the embedded aggregated supply curves (ASCs) to determine the statistical dispersion, we identify and study periods of high uncertainty in the Singapore electricity market between December 2021 and March 2023. We seek to provide insights into the origins of these uncertainties, showing that the method can capture changes in the market useful for risk modelling in decision-making.

关键词： Renewable energy sources Uncertainty ISO Decision making Stochastic processes Predictive models Electricity supply industry Probabilistic logic Encoding Risk management

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Revolutionizing Legal Case Analysis through Advanced Event Extraction

Revolutionizing Legal Case Analysis through Advanced Event E...

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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： S Shivananda Lincy Meera Mathews Sanvi Nikkam Prashanth Kambli Rajashree D Ingale Prashant Koparde Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India K S School of Engineering and Management Artificial Intelligence & Data Science Bangalore India K S School of Engineering and Management Computer Science & Business Systems Bangalore India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

In India manually evaluating court records was time-consuming and prone to errors, leading to delays in justice administration. Legal Sarathi, an automated event extraction tool was proposed to address this issue, it utilized Large Language Models coupled with other cutting-edge Natural Language Processing (NLP) techniques. Legal Sarathi analyzes unstructured legal case documents to extract time frames, significant participants and major events to improve speed and accuracy. It featured state-of-the-art Natural Language Processing (NLP) capabilities for prompt and precise case analysis whose aims included speeding up the analysis of court proceedings and real-time information retrieval, while simultaneously providing critical data for informed decision-making. The tools deliverables comprised an automated event extraction system, a highly preprocessed data corpus, a sophisticated NLP model, a user-friendly interface and thorough validation results. Ultimately, this established an improved and equitable legal system in India to ensure that all parties received quick and fair access to the courts and fostered public trust.

关键词： Productivity Visualization Text analysis Law Feature extraction Transformers Solids Natural language processing Libraries User experience

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Hybrid Deep Learning Approach for Nonfunctional Software Requirements Classifications 3

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Rahman, Kiramat Ghani, Anwar Ahmad, Rashid Sajjad, Syed Haseeb International Islamic University Dept. of Software Engineering Islamabad Pakistan Big Data Research Center Jeju National University Jeju Korea Republic of Inernational Islamic University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts. © 2023 IEEE.

关键词： Requirements engineering

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A Modified U-Net for Semantic Segmentation of Liver and Liver Tumors From CT Scans 1st

A Modified U-Net for Semantic Segmentation of Liver and Li...

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1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Medical image analysis algorithms have been quite popular for automating the segmentation of the liver and liver tumours in recent years. A system like this would also lessen radiologists’ workload and subjective interpretations. Deep learning models are commonly used in this field due to their performance improvement over conventional methods of medical image processing. However, deep learning models’ capacity for class imbalance adaptation is rarely studied, particularly in the instance of segmenting liver tumours given their tiny size. Therefore, in the current study, a straightforward Convolutional Neural Network (CNN) model based on the well-known U-Net architecture is suggested in order to capture more contextual information and solve the class imbalance issue. Additionally, a combination of the loss function is intended to guarantee precise segmentation of liver tumours. A detailed qualitative and quantitative analysis is carried out on LiTs dataset to validate the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer Aided Diagnostic Systems CT scan Deep Learning Liver Tumor Semantic Segmentation

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Fusion of Natural Language and Knowledge Graph for Multi-hop Reasoning 19th

Fusion of Natural Language and Knowledge Graph for Multi-hop...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Lu, Xun Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Multi-hop reasoning has been widely studied for its important application values in the domain of intelligent search and question answering. Real-world applications are often dominated by natural language input, and it is difficult to directly model the relation semantics in natural language to fit the multi-hop reasoning model. In addition, the extremely large scale and complex structure of knowledge graphs increase the challenge. We propose a natural language and knowledge graph fusion model (NLKGF) for multi-hop reasoning. NLKGF embeds knowledge graph by fusing natural language semantics during the graph propagation process and adds a relation attention mechanism to enable entity representations to perceive the contribution of different relations. A relation path encoder is designed to encode the relation path by an improved recurrent neural network, the reasoning entity is obtained by calculating the correlation score with the natural language. We tested the performance of the NLKGF model on two datasets requiring multi-hop reasoning. The experimental results show that NLKGF beats advanced benchmark models in multi-hop reasoning tasks, which proves superiority of our model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

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Deep Wavelet Frequency Laplacian Pyramid Network for Vehicle Logo Super-Resolution 5

Deep Wavelet Frequency Laplacian Pyramid Network for Vehicle...

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Yu, Yue She, Kun Kang, Jian Jackson, Jehoiada Shaukat, Hayat Li, Yang University of Electronic Science and Technology of China Information and Software Engineering Chengdu China Sichuan Provincial Big Data Technology Services Center Chengdu China Iqra National University Department of Computer Science Peshawar Pakistan

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

In intelligent transportation systems, generated high-resolution images promote high-level version tasks compared to captured low-resolution vehicle logo images, which lack adequate details. However, the resolution of vehicle logo images is restricted by factors like hardware. Few attempts have been made with the Laplacian pyramid structure, while images can be represented on multi-scale in the wavelet domain. This paper proposes a progressive reconstruction network with a Laplacian pyramid (WLapsrn) structure to predict the sub-bands of wavelet transformed images. Firstly, we adopted a suitable network architecture for feature extraction using residual blocks for the advanced reconstruction network in the wavelet domain. Then, the Texture-Charbonnier loss function is used to achieve highfrequency sub-bands reconstruction. Ultimately, the inverse 2D discrete stationary wavelet transform is applied to generate predicting HR images from mapped coefficients. Extensive objective and subjective evaluations using benchmark datasets demonstrate that the proposed algorithm achieves superior visual effects compared with the state-of-the-art Laplacian-based algorithms. © 2022 IEEE.

关键词： Wavelet transforms

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Building Consensus in Group Decision-Making with Intuitionistic Reciprocal Preference Relations: An Analysis of Various Protocols of Information Granularity Distribution

Building Consensus in Group Decision-Making with Intuitionis...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Juan Carlos González-Quesada Francisco Javier Cabrerizo Enrique Herrera-Viedma Witold Pedrycz Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada Spain Department of Electrical & Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

On the one hand, to model experts' preferences in group decision-making, intuitionistic reciprocal preference relations have widely been used because they allow for accommodating hesitation degrees, which are inherent to all decision-making processes. On the other hand, an optimization of information granularity distribution has recently been applied to establish consensus during group decision-making processes. Concretely, a symmetric and uniform distribution of information granularity has been considered for intuitionistic reciprocal preference relations. However, there exist other protocols of information granularity distribution that could be used. Therefore, we aim to analyze all the information granularity distribution protocols and determine their effectiveness in building consensus through intuitionistic reciprocal preference relations. The performance of the different protocols is discussed by conducting some numerical experiments that help provide insights into the effectiveness of the protocols to build consensus.

关键词： Hands Protocols Decision making Buildings Resource management Optimization Cybernetics

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Navigating Complex Multiclass Classification in High-Dimensional Spaces: A Hybrid Approach

Navigating Complex Multiclass Classification in High-Dimensi...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Praneeth Nemani Venkata Surya Sundar Vadali Prathistith Raj Medi Ashish Marisetty Satyanarayana Vollala Department of Computer Science and Engineering IIIT Naya Raipur India Department of Data Science and Artificial Intelligence IIIT Naya Raipur India

Large-scale tabular data classification is a critical task and the complexity arises from the vast amount of structured data generated in these fields, coupled with the challenges of high dimensionality and limited sample sizes. To address these challenges, advanced machine learning algorithms are required to analyze and categorize instances within these datasets effectively. In this work, we propose the usage of a state-of-the-art classifier called XBNet, which combines the strengths of tree-based classifiers and neural networks to tackle large tabular datasets. Our methodology is validated on a dataset with 64 dimensions and 11 classes, showcasing the model's capability to detect patterns and extract relevant features automatically. Furthermore, we employ K-Fold cross-validation to assess the model's performance, achieving an impressive training accuracy of 61.9% and a validation accuracy of 31.7%. These results surpass those of competing algorithms, confirming the superiority of our proposed methodology in handling large-scale tabular data classification tasks.

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DDoS Classification Through Web Application: Leveraging Algorithms for Cybersecurity

DDoS Classification Through Web Application: Leveraging Algo...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Bezawada Sasaank Mukesh Chinta Basireddy Duggireddy Redipalli Lalitha Karthik Paturi Sai Sri Vatsa Department of Artificial Intelligence & Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Andhra Pradesh Department of Computer Sciience and Engineering Velagapudi Ramakrishna Siddhartha

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This study and project proposes a method for classifying Distributed Denial of Service (DDoS) attacks using the Random Forest Algorithm, with a focus on determining measurement metrics for distinguishing between DDoS attacks and benign network activity. DDoS attacks represent a significant threat to network availability and security, necessitating robust classification mechanisms. The Random Forest Algorithm is employed due to its effectiveness in handling large pattern datasets and its ability to detect complex attack patterns. By analyzing various measurement metrics such as packet rate, packet size distribution, and traffic volume, features indicative of DDoS attacks are identified. These features are then used to train the Random Forest model to classify incoming network traffic as either DDoS or Benign. Evaluation of the classification performance is conducted using metrics such as accuracy, precision, recall, and F1 score, providing insights into the effectiveness of the proposed approach. Experimental results demonstrate the ability of the Random Forest Algorithm to accurately classify DDoS attacks while minimizing false positives, thereby enhancing network security and mitigating potential disruptions to web services.

关键词： Support vector machines Accuracy Web services Volume measurement Telecommunication traffic Denial-of-service attack Prediction algorithms Classification algorithms computer crime Random forests

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How to Select Pre-Trained Code Models for Reuse? A Learning Perspective

arXiv

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arXiv 2025年

作者： Bi, Zhangqian Wan, Yao Chu, Zhaoyang Hu, Yufei Zhang, Junyi Zhang, Hongyu Xu, Guandong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Technology Sydney Sydney Australia

Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detection. However, pre-training language models on a large-scale code corpus is computationally expensive. Fortunately, many off-the-shelf Pre-trained Code Models (PCMs), such as CodeBERT, CodeT5, CodeGen, and Code Llama, have been released publicly. These models acquire general code understanding and generation capability during pre-training, which enhances their performance on downstream code intelligence tasks. With an increasing number of these public pre-trained models, selecting the most suitable one to reuse for a specific task is essential. In this paper, we systematically investigate the reusability of PCMs. We first explore three intuitive model selection methods that select by size, training data, or brute-force fine-tuning. Experimental results show that these straightforward techniques either perform poorly or suffer high costs. Motivated by these findings, we explore learning-based model selection strategies that utilize pre-trained models without altering their parameters. Specifically, we train proxy models to gauge the performance of pre-trained models, and measure the distribution deviation between a model's latent features and the task's labels, using their closeness as an indicator of model transferability. We conduct experiments on 100 widely-used open-source PCMs for code intelligence tasks, with sizes ranging from 42.5 million to 3 billion parameters. The results demonstrate that learning-based selection methods reduce selection time to 100 seconds, compared to 2,700 hours with brute-force fine-tuning, with less than 6% performance degradation across related tasks. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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