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Classification and Comprehension of Software Requirements Using Ensemble Learning

Computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad Syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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Multilingual Sentiment Analysis for Investigating Perceptions of Globalization

Multilingual Sentiment Analysis for Investigating Perception...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Anagha Ani Erik Cambria College of Computing & Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

This paper aims to identify trends in perceptions on the topic of globalization by performing sentiment analysis on text data collected from social media posts. A novel methodology of extracting culture-informed sentiments is proposed and tested on a corpus containing text posts from the social media site Reddit in two languages: French and English. To do so, a graph convolutional network is used to train a polarity classification model by extracting commonsense culture-specific knowledge using the SenticNet knowledge base. A variety of sentiment analysis tasks including polarity classification, intensity ranking, toxicity spotting, wellbeing assessment, and personality classification are performed using Sentic APIs on two extracted subsets of the corpus and the resulting trends in the data are identified and analyzed.

关键词： Knowledge engineering Sentiment analysis Translation Social networking (online) Graph convolutional networks Globalization Market research Multilingual data mining Commonsense reasoning

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Application of Classification Machine Learning Models for Grouping Laboratory Samples into Kazakh Clans

Application of Classification Machine Learning Models for Gr...

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AITU: Digital Generation, IEEE

作者： Raziya Abutalipova Department of Computing and Data Science Astana IT University Astana Kazakhstan

ISBN: (数字)9798350364378

ISBN: (纸本)9798350364385

This research bridges traditional Kazakh heritage with modern science by developing a machine learning model to classify Kazakh clans using Y-chromosome data. This paper introduces an innovative approach for assigning Y-chromosomes to specific clans. Methodology leverages a dataset that includes 3,099 laboratory samples with clan affiliation and corresponding Short Tandem Repeat profiles. Utilizing a dataset, I employed several well-known machine learning algorithms (Random Forest, Decision tree, Support vector machine, Bayesian model) to predict clan affiliations, highlighting the potential of data analytics in enriching our understanding of cultural identities. The evaluation of the model's effectiveness involved rigorous testing against a diverse range of samples, ensuring a scalable and adaptable tool for future genomic research.

关键词： Support vector machines Analytical models Adaptation models Machine learning algorithms Accuracy Sociology data models

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ECSeg: Edge-Cloud Switched Image Segmentation for Autonomous Vehicles

ECSeg: Edge-Cloud Switched Image Segmentation for Autonomous...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Siyuan Zhou Duc Van Le Rui Tan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

Existing autonomous vehicles have not utilized the cloud computing for execution of their deep learning-based driving tasks due to the long vehicle-to-cloud communication latency. Meanwhile, the vehicles are in general equipped with the resource-constrained edge computing devices which may be unable to execute the compute-intensive deep learning models in real time. The increasing data transmission speed of the commercial mobile networks sheds light upon the feasibility of using the cloud computing for autonomous driving. Our city-scale real-world measurements show that the vehicles can partially use the cloud computing via the fifth generation (5G) mobile network with the low data transmission latency. In this paper, we present the design and implementation of ECSeg, an edge-cloud switched image segmentation system that dynamically switches between the edge and cloud for executing the deep learning-based semantic segmentation models to understand the vehicle's visual scenes in real time. The switching decision-making is challenging due to the intricate interdependencies among various factors including the dynamic wireless channel condition, vehicle's movement and visual scene change. To this end, we employ deep reinforcement learning to learn an optimal switching policy. Extensive evaluation based on both real-world experiments and trace-driven simulations demonstrates that ECSeg achieves superior image segmentation accuracy for autonomous vehicles, compared with four baseline approaches.

关键词： Image segmentation Cloud computing Visualization Image edge detection Switches Deep reinforcement learning Real-time systems data communication Vehicle dynamics Autonomous vehicles

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A Survey on Evaluation of Multimodal Large Language Models

arXiv

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arXiv 2024年

作者： Huang, Jiaxing Zhang, Jingyi College of Computing and Data Science Nanyang Technological University Singapore

Multimodal Large Language Models (MLLMs) mimic human perception and reasoning system by integrating powerful Large Language Models (LLMs) with various modality encoders (e.g., vision, audio), positioning LLMs as the "brain" and various modality encoders as sensory organs. This framework endows MLLMs with human-like capabilities, and suggests a potential pathway towards achieving artificial general intelligence (AGI). With the emergence of all-round MLLMs like GPT-4V and Gemini, a multitude of evaluation methods have been developed to assess their capabilities across different dimensions. This paper presents a systematic and comprehensive review of MLLM evaluation methods, covering the following key aspects: (1) the background of MLLMs and their evaluation;(2) "what to evaluate" that reviews and categorizes existing MLLM evaluation tasks based on the capabilities assessed, including general multimodal recognition, perception, reasoning and trustworthiness, and domain-specific applications such as socioeconomic, natural sciences and engineering, medical usage, AI agent, remote sensing, video and audio processing, 3D point cloud analysis, and others;(3) "where to evaluate" that summarizes MLLM evaluation benchmarks into general and specific benchmarks;(4) "how to evaluate" that reviews and illustrates MLLM evaluation steps and metrics;Our overarching goal is to provide valuable insights for researchers in the field of MLLM evaluation, thereby facilitating the development of more capable and reliable MLLMs. We emphasize that evaluation should be regarded as a critical discipline, essential for advancing the field of MLLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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Open-Vocabulary Object Detection via Language Hierarchy

arXiv

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arXiv 2024年

作者： Huang, Jiaxing Zhang, Jingyi Jiang, Kai Lu, Shijian College of Computing and Data Science Nanyang Technological University Singapore

Recent studies on generalizable object detection have attracted increasing attention with additional weak supervision from large-scale datasets with image-level labels. However, weakly-supervised detection learning often suffers from image-to-box label mismatch, i.e., image-level labels do not convey precise object information. We design Language Hierarchical Self-training (LHST) that introduces language hierarchy into weakly-supervised detector training for learning more generalizable detectors. LHST expands the image-level labels with language hierarchy and enables co-regularization between the expanded labels and self-training. Specifically, the expanded labels regularize self-training by providing richer supervision and mitigating the image-to-box label mismatch, while self-training allows assessing and selecting the expanded labels according to the predicted reliability. In addition, we design language hierarchical prompt generation that introduces language hierarchy into prompt generation which helps bridge the vocabulary gaps between training and testing. Extensive experiments show that the proposed techniques achieve superior generalization performance consistently across 14 widely studied object detection datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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FluE: A Resource Fluid Equilibrium Strategy for AIGC Within Evolving computing Power Networks

FluE: A Resource Fluid Equilibrium Strategy for AIGC Within ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Zejun Qiu, Chao Ren, Xiaoxu Wang, Xiaofei Xiong, Zehui Yao, Haipeng Niyato, Dusit Tianjin University College of Intelligence and Computing Tianjin China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China Singapore University of Technology and Design Singapore Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350351255

The presence of Artificial Intelligence Generated Content (AIGC) has garnered widespread interest. AIGC enables content creation by analyzing big data, leveraging the capabilities of extensive AI models, and substantial AI computing. computing power networks (CPNs) represent an excellent approach for offering pervasive AI computing resources to AIGC. However, these characteristics have posed unprecedented challenges to the CPNs helped AIGC, including the uncertainty of prompts' information value, the inability to model the continuity of computing resources, and the incapacity to represent complex multi-dimensional spaces. In this paper, we propose a computing resources equilibrium strategy based on the fluid model for AIGC helped by CPNs, namely FluE. This mechanism obtains information entropy by constructing an AIGC prompt tree to measure the information value of AIGC prompts. In addition, we model the continuity of computing resources by the fluid model. A fluid-stopping equilibrium strategy is formulated to obtain the average fluid level of computing resources based on the Laplace-Stieltjes transform. To solve the equilibrium strategy, we develop a diffusion-based algorithm for FluE to adjust the fluid policy dynamically to maximize resource rewards. Finally, the evaluations demonstrate improvements in average social welfare. © 2024 IEEE.

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Finding Sets of Pareto Sets in Real-World Scenarios – A Multitask Multiobjective Perspective

Finding Sets of Pareto Sets in Real-World Scenarios – A Mul...

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Congress on Evolutionary Computation

作者： Jiao Liu Yew Soon Ong Melvin Wong College of Computing & Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Recently, evolutionary multitasking has been employed to generate a “set of Pareto sets” (SOS) for machine learning models, addressing diverse task settings across heterogeneous environments. This involves creating a repository of compact, specialized solution models that are collectively tailored to each specific task setting and environment, enabling users to select the most suitable model based on particular specifications and preferences. In this paper, we further demonstrate the versatility and applicability of the SOS concept across diverse domains, focusing on three real-world problems: engineering design problems, inventory management problems, and hyperparameter optimization problems. Additionally, as evolutionary multitasking has proven effective in generating the SOS, we investigate the performance of current evolutionary multitasking methods on these real-world problems. Subsequently, we present visualizations of the generated SOS in both decision and objective spaces, complemented by the development of a measurement to gauge the similarity between different Pareto sets corresponding to diverse tasks. Finally, we show that by systematically examining the shifts in Pareto optimal designs across different task settings though the SOS solutions, users can gain deeper understandings on the dynamic interplay between design solutions and their performance in different settings or contexts.

关键词： Visualization Heuristic algorithms Focusing Pareto optimization Inventory management Multitasking Hyperparameter optimization

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Conversational Explanations: Discussing Explainable AI with Non-AI Experts 25

Conversational Explanations: Discussing Explainable AI with ...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Zhang, Tong Zhang, Mengao Low, Wei Yan Yang, X. Jessie Li, Boyang Albert College of Computing and Data Science Nanyang Technological University Singapore Singapore Nanyang Technological University Singapore Singapore College of Engineering University of Michigan Ann ArborMI United States

ISBN: (纸本)9798400713064

Explainable AI (XAI) aims to provide insights into the decisions made by AI models. To date, most XAI approaches provide only one-time, static explanations, which cannot cater to users' diverse knowledge levels and information needs. Conversational explanations have been proposed as an effective method to customize XAI explanations. However, building conversational explanation systems is hindered by the scarcity of training data. Training with synthetic data faces two main challenges: lack of data diversity and hallucination in the generated data. To alleviate these issues, we introduce a repetition penalty to promote data diversity and exploit a hallucination detector to filter out untruthful synthetic conversation turns. We conducted both automatic and human evaluations on the proposed system, fEw-shot Multi-round ConvErsational Explanation (EMCEE). For automatic evaluation, EMCEE achieves relative improvements of 81.6% in BLEU and 80.5% in ROUGE compared to the baselines. EMCEE also mitigates the degeneration of data quality caused by training on synthetic data. In human evaluations (N = 60), EMCEE outperforms baseline models and the control group in improving users' comprehension, acceptance, trust, and collaboration with static explanations by large margins. Through a fine-grained analysis of model responses, we further demonstrate that training on self-generated synthetic data improves the model's ability to generate more truthful and understandable answers, leading to better user interactions. To the best of our knowledge, this is the first conversational explanation method that can answer free-form user questions following static explanations. © 2025 Copyright held by the owner/author(s).

关键词： data assimilation

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Robust Kernel Sparse Subspace Clustering

Robust Kernel Sparse Subspace Clustering

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ivica Kopriva Division of Computing and Data Science Rudjer Boskovich Institute Zagreb Croatia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Kernel methods are widely used in pattern recognition, including subspace clustering (SC). They transform nonlinear problems in the input data space into linear ones in a high-dimensional feature space. This transformation, achieved through the kernel trick, makes computationally tractable nonlinear algorithms possible. However, kernelizing linear algorithms through the kernel trick is infeasible in case of gross sparse corruptions that are modeled by the ℓ 1 -norm of the error term. To address this, we propose, for the first time, a robust kernel sparse SC (RKSSC) algorithm for data with gross sparse corruptions. We validated the proposed approach on two well-known datasets, using the linear robust SSC algorithm and nonlinear (kernel-based) SSC algorithm as baseline models. According to the Wilcoxon test, the RKSSC's algorithm clustering performance is statistically significantly better than baselines.

关键词： Computational modeling Clustering algorithms Signal processing algorithms Transforms Signal processing Pattern recognition Sparse matrices Kernel Speech processing Optimization

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