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2nd International Conference on Electrical, Electronics, and information Engineering, EEIE 2023

作者： Deng, Xiaobin School of Computer Science Guangdong University of Science and Technology Guangdong Dongguan523668 China School of Information Technology Nanchang Vocational University Jiangxi Nanchang330500 China AIoT Edge Computing Engineering Technology Research Center of Dongguan City Guangdong University of Science and Technology Guangdong Dongguan523668 China

ISBN: (纸本)9781510672840

Recently, there is a popular type of social network called Event-based Social Networks (EBSN), such as Meetup, Plancast and Douban. In EBSN, users often participate in events as group members. Therefore, studying recommending events to groups is a very meaningful thing. In order to reflect the different weights of different factors and consider the influence of the internal features both of the groups and events, we propose Dual Granularity Multi-layer Attention Networks (DGMAN) for group event recommendation in EBSN. Firstly, we construct an attention network layer to learn the vector representation both of the groups and events through coarse-grained learning. Secondly, we build a two-layer attention network to further learn the vectors both of groups and events through fine-grained learning, which considers the influence of the internal features both of the groups and events. Finally, high-quality feature representations of groups and events are generated. We evaluate performance of DGMAN on three real-world datasets, and the extensive experimental results show that DGMAN outperforms the state-of-the-art approaches. © 2024 SPIE.

关键词： Convolutional neural networks

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Leveraging Machine Learning and Drone technology for Effective Insect Pest Management in Agriculture

Leveraging Machine Learning and Drone Technology for Effecti...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Shaik, Mohammed Ali Parveen, Masrath Qureshi, Imran SR University School of Computer Science & Artificial Intelligence Telangana State Warangal India Vidya Jyothi Institute of Technology Department of Information Technology Telangana State Hyderabad India AlMusanna College of Technology College of Computing and Information Sciences Oman

ISBN: (纸本)9798350361537

Insect pests pose a serious threat to food security and agricultural profitability, as they cause direct damage to crops and transmit plant diseases. Manual monitoring methods are inadequate to detect and prevent infestations, especially in large-scale farming operations. This article explores the potential of using drone technology and machine learning algorithms to enhance insect pest management in agriculture. Drones can capture high-resolution images of fields, while machine learning can analyze the data and identify signs of insect activity. However, this approach faces several challenges, such as the diversity and complexity of insect species, their behavior, and their impact on crops. Therefore, a multidisciplinary collaboration among researchers, farmers, technologists, and entomologists is essential to develop effective and robust solutions. This article aims to provide an overview of the current state and future prospects of using drone technology and machine learning for insect pest management in agriculture. © 2024 IEEE.

关键词： Plant diseases

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Forecasting New Mobile Site Location Suitability Using Machine Learning 8

Forecasting New Mobile Site Location Suitability Using Machi...

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8th International Conference on Data science and Machine Learning Applications, CDMA 2025

作者： Rabbani, Zeeshan Ebrahim Hosseini, Seyed Pervez Chattha, Shahbaz Alandjani, Gasim Abosaq, Nasser Abdul Majid, Mazlina School of Information Technology Whitecliffe College Auckland New Zealand School of Information Technology Whitecliffe College Christchurch New Zealand School of Information Technology Whitecliffe College Wellington New Zealand Yanbu Industrial College Computer Science and Engineering Yanbu Saudi Arabia Universi Malaysia Pahang Al-Sultan Faculty of Computing Abdullah Pekan Malaysia

ISBN: (纸本)9798331539696

The increasing density of mobile networks has made the identification of optimal new coverage sites a critical challenge for mobile operators. Traditional methods relying on manual inspections and council plans are labour-intensive and insufficient. This study addresses this issue by leveraging data science and machine learning techniques to predict the suitability of new mobile cell sites. Utilizing data from Radio Spectrum Management New Zealand and Stats New Zealand, the study employs Random Forest as Machine learning model. Key factors considered include existing operator information and population density. The methodology integrates data cleaning, feature engineering, and model training, with validation performed using Power BI. Results demonstrate an accuracy of 84.62% for the Random Forest model, highlighting the importance of geographical location and population density in site suitability predictions. This approach will streamline the site selection process and enhance service quality, positioning operators as leaders in underserved areas. © 2025 IEEE.

关键词： Random forests

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The Past, Present, and Future of Storage Technologies (Part 1 of 2)

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ACM TRANSACTIONS ON STORAGE 2025年第1期21卷 1-3页

作者： Kuenning, Geoff Won, Youjip Zhao, Ming Computer Science Harvey Mudd College Claremont United States Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea (the Republic of) School of Computing and Information Sciences Arizona State University Tempe United States Computer Science Stony Brook University Stony Brook United States

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In-Context Compositional Generalization for Large Vision-Language Models

In-Context Compositional Generalization for Large Vision-Lan...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Chuanhao Jing, Chenchen Li, Zhen Zhai, Mingliang Wu, Yuwei Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China School of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9798891761643

Recent work has revealed that in-context learning for large language models exhibits compositional generalization capacity, which can be enhanced by selecting in-context demonstrations similar to test cases to provide contextual information. However, how to exhibit in-context compositional generalization (ICCG) of large vision-language models (LVLMs) is non-trival. Due to the inherent asymmetry between visual and linguistic modalities, ICCG in LVLMs faces an inevitable challenge-redundant information on the visual modality. The redundant information affects in-context learning from two aspects: (1) Similarity calculation may be dominated by redundant information, resulting in sub-optimal demonstration selection. (2) Redundant information in in-context demonstrations brings misleading contextual information to in-context learning. To alleviate these problems, we propose a demonstration selection method to achieve ICCG for LVLMs, by considering two key factors of demonstrations: content and structure, from a multimodal perspective. Specifically, we design a diversity-coverage-based matching score to select demonstrations with maximum coverage, and avoid selecting demonstrations with redundant information via their content redundancy and structural complexity. We build a GQA-ICCG dataset to simulate the ICCG setting, and conduct experiments on GQA-ICCG and the VQA v2 dataset. Experimental results demonstrate the effectiveness of our method. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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A Privacy Preserving Federated Learning System for IoT Devices Using Blockchain and Optimization

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Journal of Computer and Communications 2024年第9期12卷 78-102页

作者： Yang Han School of Computing Nanjing University of Information Science & Technology Nanjing China

In this study, a blockchain based federated learning system using an enhanced weighted mean vector optimization algorithm, known as EINFO, is proposed. The proposed EINFO addresses the limitations of federated averaging during global update and model training, where data is unevenly distributed among devices and there are variations in the number of data samples. Using a well-defined structure and updating the vector positions by local searching, vector combining, and updating rules, the EINFO algorithm maximizes the shared model parameters. In order to increase the exploration and exploitation capabilities, the model convergence rate is improved and new vectors are generated through the use of a weighted mean vector based on the inverse square law. To choose validators, miners, and to propagate new blocks, a delegated proof of stake based on the reliability of blockchain nodes is suggested. Federated learning is included into the blockchain to protect nodes from both external and internal threats. To determine how well the suggested system performs in relation to current models in the literature, extensive simulations are run. The simulation results show that the proposed system outperforms existing schemes in terms of accuracy, sensitivity and specificity.

关键词： Blockchain Credibility Status Federated Learning IoT Privacy Weighted Mean of Vectors

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Profanity Detection from Audio Recordings Using Natural Language Processing Techniques

Profanity Detection from Audio Recordings Using Natural Lang...

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Patil, Swapnil Chandre, Pankaj R. Vanarote, Viresh Pathan, Shafi Nimbalkar, Madhukar Shinde, Gitanjali Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhwa India

ISBN: (纸本)9789819998104

Profanity detection has become increasingly important in various industries, including media and online content moderation. In this paper, we propose a novel approach to identifying profane words from audio using natural language processing (NLP) techniques. We first convert the audio files into textual transcripts using automatic speech recognition (ASR) tools. We then apply pre-processing techniques such as tokenization, stemming, and stop-word removal to the transcripts. Finally, we train various machine learning models, such as Naive Bayes, Support Vector Machines (SVMs), and Random Forests, to classify the transcripts as either containing profane language or not. The proposed approach can be integrated into various applications, such as content moderation tools, online chat systems, and automatic transcription services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audio recordings

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Improving Network Efficiency in Clustered Tree Topology through PSO Optimization in IEEE 802.15.4-DSME based IoT Networks 18

Improving Network Efficiency in Clustered Tree Topology thro...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Anand, Sonali Choudhury, Nikumani Ojha, Tamoghna Hazarika, Anakhi Dave, Jay Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Pilani Hyderabad India Indian Institute of Technology Indian School of Mines Dept. of Mathematics & Computing Dhanbad India

ISBN: (纸本)9798350391725

The IEEE 802.15.4 standard is designed for low-rate wireless personal area networks (LR-WPANs). Deterministic and Synchronous Multi-channel Extension (DSME) is one of the key Medium Access Control (MAC) modes of the IEEE 802.15.4 standard that was introduced to address the need for higher reliability, lower latency, and better scalability in network communication in Internet of Things (IoT) applications. However, in dynamic network conditions (topology and channel traffic), the network suffers from several limitations, such as low packet delivery ratio, high latency and packet collisions, and finally, higher power consumption. This is because of sub-optimal parameter settings. The standard does not define any mechanism to adapt the parameters based on network conditions. In this paper, we propose a mechanism to adapt/fine-tune the network parameters DSME along with the optimization of QoS using an algorithm based on Particle Swarm Optimization(PSO) for dense and dynamic DSME-based IoT networks. Through simulations, the proposed mechanisms' performance is analyzed in terms of energy efficiency, transmission overhead, throughput, and latency and is shown to outperform the existing DSME MAC scheme. © 2024 IEEE.

关键词： Internet of things

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Towards Cross-Domain Few-Shot Graph Anomaly Detection 24

Towards Cross-Domain Few-Shot Graph Anomaly Detection

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Chen, Jiazhen Fu, Sichao Zhang, Zhibin Ma, Zheng Feng, Mingbin Wirjanto, Tony S. Peng, Qinmu University of Waterloo Department of Statistic and Actuarial Science Canada School of Electronic Information and Communications Huazhong University of Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China Cheriton School of Computer Science University of Waterloo Canada

ISBN: (纸本)9798331506681

Few-shot graph anomaly detection (GAD) has recently garnered increasing attention, which aims to discern anomalous patterns among abundant unlabeled test nodes under the guidance of a limited number of labeled training nodes. Existing few-shot GAD approaches typically adopt meta-training methods trained on richly labeled auxiliary networks to facilitate rapid adaptation to target networks that possess sparse labels. However, these proposed methods often assume that the auxiliary and target networks exist in the same data distributions-an assumption rarely holds in practical settings. This paper explores a more prevalent and complex scenario of cross-domain few-shot GAD, where the goal is to identify anomalies within sparsely labeled target graphs using auxiliary graphs from a related, yet distinct domain. The challenge here is nontrivial owing to inherent data distribution discrepancies between the source and target domains, compounded by the uncertainties of sparse labeling in the target domain. In this paper, we propose a simple and effective framework, termed CDFS-GAD, specifically designed to tackle the aforementioned challenges. CDFS-GAD first introduces a domain-adaptive graph contrastive learning module, which is aimed at enhancing cross-domain feature alignment. Then, a prompt tuning module is further designed to extract domain-specific features tailored to each domain. Moreover, a domain-adaptive hypersphere classification loss is proposed to enhance the discrimination between normal and anomalous instances under minimal supervision, utilizing domain-sensitive norms. Lastly, a self-training strategy is introduced to further refine the predicted scores, enhancing its reliability in few-shot settings. Extensive experiments on twelve real-world cross-domain data pairs demonstrate the effectiveness of the proposed CDFS-GAD framework in comparison to various existing GAD methods including unsupervised, semi-supervised, few-shot and cross-domain GAD methods. © 20

关键词： Contrastive Learning

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Developing Engagement in the Learning Management System Supported by Learning Analytics

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Computer Systems science & Engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

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