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7th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Crossling, Cleveland J. Vadapalli, Hima Van Der Haar, Dustin Academy of Computer Science and Software Engineering University of Johannesburg GP Johannesburg South Africa

ISBN: (纸本)9798400717437

The integration of artificial intelligence in agriculture has revolutionized farming practices, enhancing crop yields and resource efficiency. However, existing machine learning systems primarily focus on livestock, overlooking the impact of wildlife on agricultural productivity. This study addresses the gap by introducing two classification pipelines utilizing VGG16 and ResNet50 models for identifying five major classes of wildlife (foxes, rats, pigeons, raccoons, and squirrels) affecting agricultural sectors. A comprehensive dataset, comprising existing datasets and trail-camera footage, is compiled for training and validation. The results show that models trained on clear images struggle to generalize to real-world environments (the best accuracy being 48.19%). Grey-scaled models perform worse with a 2-6% decrease. Training on the training dataset and evaluating the validation set yields better average results for the weaker-performing model. This study contributes to invasive animal control in agriculture, providing a foundation for effective wildlife management with computer vision. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Livestock

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CEMOP: Enhancing MOBA match outcome predictions by understanding teammates and opponents effects 3

CEMOP: Enhancing MOBA match outcome predictions by understan...

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3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Zhao, Jiangfeng College of Computer Science and Software Engineering Shenzhen University Shenzhen518057 China

ISBN: (纸本)9781510681316

Multiplayer Online Battle Arena (MOBA) games currently dominate the esports landscape, offering a concrete and vivid embodiment for team comparisons, where accurately predicting the winning team is both important and challenging. Recent studies often rely on the static features of team members to evaluate a team’s capabilities and predict their likelihood of winning. However, such approaches fail to consider the critical game-contextual influences of interactions with teammates and opponents on individuals. During a match, an individual’s performance is consistently influenced by teammates and opponents, leading to variations in individual features across different games. Moreover, the measurement of team capabilities also needs improvement, as the single scalar rating employed is too simplistic for sophisticated MOBA games. To this end, we propose a novel Context-Enhanced Match Outcome Prediction model (CEMOP), which incorporates the effect of both teammates and opponents for a better measurement of team capabilities. Specifically, we designed a match context encoder for MOBA games to evaluates the impact of teammates and opponents on individuals separately and embeds the information into the individual representations. After that the single team member’s representations which contain the influence of teammates and opponents are aggregated based on their importance to create a comprehensive representation of the team capabilities, enables CEMOP to adaptively adjust team evaluations. Experimental results from three real-world MOBA games clearly verify the effectiveness of CEMOP compared with several state-of-the-art methods. © 2024 SPIE.

关键词： Data mining

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Identification of software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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An Evaluation System for Large Language Models based on Open-Ended Questions 11

An Evaluation System for Large Language Models based on Open...

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11th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Cao, Zhiyuan Ma, Zeyu Chen, Mingang Computer and Information Engineering College Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Mathematical and Science College Shanghai Normal University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China

ISBN: (纸本)9798350376982

We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with evaluation reports. Currently, the evaluation of large-scale language models often exists with two prominent limitations: (1) The evaluation methods are often single-minded, resulting in less credible results. (2) Most evaluations are based on datasets with closed-ended questions, treating generative large language models as discriminative models, which fails to adequately reflect the high output flexibility characteristic of these models. For these two limitations, we proposed an evaluation system for LLMs based on open-ended questions. Our experiments on the adapted open-source datasets demonstrated the effectiveness of this system. © 2024 IEEE.

关键词： Natural language processing systems

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Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

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computer Modeling in engineering & Sciences 2024年第6期139卷 3011-3027页

作者： Rui Wang Yao Zhou Guangchun Luo Peng Chen Dezhong Peng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer Science Sichuan UniversityChengdu610065China National Innovation Center for UHD Video Technology Chengdu610095China

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

关键词： Time series anomaly detection unsupervised feature learning feature fusion

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Striking a Chord through a Mixed-Methods Study of Music-Based Learning to Leverage Music and Creativity to Bridge the Gender Gap in computer Science 2024

Striking a Chord through a Mixed-Methods Study of Music-Base...

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55th ACM Technical Symposium on computer Science Education, SIGCSE 2024

作者： Jamshidi, Fatemeh Bigonah, Maryam Marghitu, Daniela Computer Science and Software Engineering Auburn University AuburnAL United States

ISBN: (纸本)9798400704246

The persistent gender gap in technical fields, particularly in computer science (CS), remains a complex challenge. This paper draws upon recognized gender differences in aptitude, interest, and the educational environment. It builds upon established theoretical foundations, including Bandura's Social Cognitive Theory [9] and Eccles's Expectancy-Value Theory [3][2], to explore innovative strategies for engaging underrepresented groups, specifically female students, in CS. Central to our approach is an innovative curriculum that integrates the realms of math, computing, and music. Recognizing the intrinsic connection between music and mathematical patterns, our pedagogical model simplifies complex CS concepts, transforming topics like looping, algorithms, and conditional statements into engaging, relatable lessons. Additionally, our curriculum incorporates foundational elements of robotics, enhancing its practical applicability and real-world relevance. This groundbreaking educational paradigm not only simplifies technical concepts but also leverages students' innate passion for music, creating an intuitive and enjoyable learning environment. By blending artistic creativity with scientific precision, this approach sets a new standard for curriculum design and offers educators a pioneering roadmap to address the gender gap in CS. Our study aligns with SIGCSE's mission, marking a fresh, inclusive trajectory for the future of technology education. © 2024 Owner/Author.

关键词： Music

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Energy Harvesting Techniques for Long-Term Self-Powered Deep Brain Stimulation Systems: A Comprehensive Analysis

Energy Harvesting Techniques for Long-Term Self-Powered Deep...

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2024 IEEE Sustainable Power and Energy Conference, iSPEC 2024

作者： Wijekoon, Ravindu Kularatna-Abeywardana, Dulsha University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350395075

Deep brain stimulation (DBS) systems have emerged as promising therapy for various neurological disorders, providing means to deliver electrical stimulation directly to targeted regions of the brain. However, the long-term sustainability of these systems is a significant challenge, as the implanted power sources have limited lifetime. Self-energy harvesting through the human body and ambient environment shows promising solutions for this issue. Therefore, this paper explores alternative power sources for DBS systems through a comprehensive analysis of different energy harvesting techniques. Harvesting approach, power output, biocompatibility, limitations, and promising future research avenues are explored while proposing solutions to enhance energy harvesting efficiency for long-term, self-powered DBS systems. This comprehensive analysis paves the way for advancements in self-powered DBS technology, promoting a more patient-centric and sustainable approach to treating neurological conditions. © 2024 IEEE.

关键词： Neurons

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Rainfall Process Similarity Algorithm Research Based on Deep Learning by Using Precipitation Images 7

Rainfall Process Similarity Algorithm Research Based on Deep...

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7th International Conference on computer Information Science and Application Technology, CISAT 2024

作者： Yu, Yufeng Wu, Panqi Cai, Difei Liu, Weifu Wang, Xuhang Hohai University College of Computer Science and Software Engineering Nanjing210098 China

ISBN: (纸本)9798350375107

Rainfall is the main cause of flood disasters, and analyzing its features plays a crucial role in preventing flood disasters. How to extract rainfall process features and conduct rainfall similarity analysis is a challenging work. Based on the similarity search method for daily precipitation images, this paper proposes a rainfall process similarity search method, which extracts rainfall duration, cumulative precipitation, temporal distribution, spatial distribution and center movement trajectory as the features of rainfall process, and defines a set-weighting method based on the Grey Wolf Optimization algorithm (IGWO) to weight and fuse the five extracted features as the similarity measure of rainfall process. The similarity search results of rainfall process images on the Jialing Basin illustrate that the method proposed in this paper can efficiently and accurately identify similar rainfall processes in historical scenes, thereby providing a new technical support for intelligent rainfall-flood forecasting. © 2024 IEEE.

关键词： Rain

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Role-Specific Physiological Responses and Quality of Experience in Collaborative Virtual Reality Tasks: A Comparative Study of Leaders and Followers 10

Role-Specific Physiological Responses and Quality of Experie...

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10th International Conference on Virtual Reality, ICVR 2024

作者： Moharana, Bhagyabati Keighrey, Conor Murray, Niall Technological University of the Shannon Dept of Computer and Software Engineering Midlands Athlone Ireland

ISBN: (纸本)9798350364231

The emergence of Collaborative Virtual Reality (CVR) technology has transformed team-based interactions across diverse fields, offering immersive and interactive environments that enhance collaborative efforts. However, a critical gap remains in understanding the Quality of Experience (QoE) from a task role-specific perspective within these environments. This study addresses this gap by evaluating the QoE differences between leaders and followers in CVR settings, using both subjective assessments and objective physiological measures. The research employs advanced biosensors, including eye-tracking and physiological responses, to examine how distinct roles influence cognitive load, emotional states, and overall user satisfaction. Participants engaged in a collaborative 'pick and place' task within a VR environment, with data collected on electrodermal activity (EDA), heart rate (HR), blood volume pulse (BVP), inter-beat interval (IBI), skin temperature, and pupil dilation (PD). The NASA Task Load Index (NASA-TLX) was also used to measure perceived workload. Findings reveal significant differences in physiological responses between leaders and followers, with leaders experiencing higher cognitive and temporal demands. The results suggest that role-specific QoE evaluations can inform the design of more equitable and effective CVR systems, enhancing user satisfaction and collaboration outcomes. This study advances the field by integrating multimodal data acquisition and providing insights into CVR environments for diverse collaborative roles. © 2024 IEEE.

关键词： Helmet mounted displays

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Self-knowledge distillation with dimensional history knowledge

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Science China(Information Sciences) 2025年

作者： Wenke HUANG Mang YE Zekun SHI He LI Bo DU National Engineering Research Center for Multimedia Software School of Computer ScienceWuhan University

Existing self-knowledge distillation (Self-KD) solutions usually focus on transferring historical predictions of individual instances to the current network. However, this approach tends to create overconfidence for easy instances and underconfidence for hard instances. The widely used temperature-based strategies to smooth or sharpen the predicted distributions can lead to inconsistencies across instances, causing sensitivity issues. To address this, our approach views a queue of instances as an ensemble rather than treating each instance independently. We propose a novel method that distills historical knowledge from a dimensional perspective, utilizing intra class characteristics and interclass relationships within each ensemble. First, we align each dimension distribution from the current network to the historical output. Second, we ensure each dimension is closer to similar dimensions than dissimilar ones, maintaining consistent attitudes from present and historical perspectives. Our insights reveal that distilling historical knowledge from a dimensional perspective is more effective than the traditional instance-based approach, with potential applications in related tasks. Empirical results on three famous datasets and various network architectures demonstrate the superiority of our proposed method. Our code is available at https://***/WenkeHuang/DimSelfKD.

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