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23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Sghaier, Feriel Mejri, Mohamed Manai, Elyes Ghayoula, Ridha Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuébecQCG1V 0A6 Canada Ecole Nationale d'Ingénieurs de Carthage Université de Carthage Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

The Tamazight civilization stands as a significant cultural entity, marked by its linguistic diversity, historical legacy, and scriptural traditions, which collectively enrich the cultural tapestry of North Africa. Among these traditions, the Tamazight handwritten script assumes particular importance, embodying centuries of cultural identity and artistic expression. Recognizing the imperative of safeguarding this cultural heritage, our study focuses on Tamazight handwritten character recognition. Leveraging the strategic application of Transfer Learning, we explore its efficacy in this domain. Transfer Learning presents a robust framework wherein pre-existing models are adapted for specific tasks despite limited data availability. Our research employs three prominent Transfer Learning architectures: VGG, ResNet, and MobileNet. Through a rigorous comparative analysis, we discern the efficacy of these methodologies in the context of Tamazight handwritten character recognition. Our findings underscore the potential of Transfer Learning to significantly augment the accuracy and efficiency of script recognition systems, thereby advancing the overarching objective of preserving and propagating the Tamazight cultural heritage. © 2024 IOS Press. All rights reserved.

关键词： Transfer learning

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Federated Graph Learning for Cross-Domain Recommendation 38

Federated Graph Learning for Cross-Domain Recommendation

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Yang, Ziqi Peng, Zhaopeng Wang, Zihui Qi, Jianzhong Chen, Chaochao Pan, Weike Wen, Chenglu Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computing and Information Systems The University of Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods. We open source the code at https://***/LafinHana/FedGCDR. © 2024 Neural information processing systems foundation. All rights reserved.

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Migrating Birds Optimization-Based Feature Selection for Text Classification

arXiv

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arXiv 2024年

作者： Kaya, Cem Kilimci, Zeynep Hilal Uysal, Mitat Kaya, Murat Dogus University Turkey Department of Information Systems Engineering Kocaeli University Turkey Department of Software Engineering Dogus University Turkey Department of Computer Programming Acibadem University Turkey

This research introduces a novel approach, MBO-NB, that leverages Migrating Birds Optimization (MBO) coupled with Naive Bayes as an internal classifier to address feature selection challenges in text classification having large number of features. Focusing on computational efficiency, we preprocess raw data using the Information Gain algorithm, strategically reducing the feature count from an average of 62221 to 2089. Our experiments demonstrate MBO-NB's superior effectiveness in feature reduction compared to other existing techniques, emphasizing an increased classification accuracy. The successful integration of Naive Bayes within MBO presents a well-rounded solution. In individual comparisons with Particle Swarm Optimization (PSO), MBO-NB consistently outperforms by an average of 6.9% across four setups. This research offers valuable insights into enhancing feature selection methods, providing a scalable and effective solution for text classification. © 2024, CC BY-NC-ND.

关键词： Feature Selection

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Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

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computers, Materials & Continua 2022年第12期73卷 4641-4656页

作者： Mubashar Hussain Toqir A.Rana Aksam Iftikhar M.Usman Ashraf Muhammad Waseem Iqbal Ahmed Alshaflut Abdullah Alourani School of Systems and Technology University of Management and TechnologyLahore54770Pakistan Department of Computer Science and It The University of LahoreLahore54000Pakistan School of Computer Sciences Universiti Sains Malaysia(USM)Penang11800Malaysia Department of Computer Science Comsats University IslamabadLahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Software Engineering The Superior UniversityLahore53700Pakistan Faculty of Computing and It Albaha UniversityAlbahaSaudi Arabia Department of Computer Science Majmaah UniversitySaudi Arabia

In the field of sentiment analysis,extracting aspects or opinion targets fromuser reviews about a product is a key *** the polarity of an opinion is much more useful if we also know the targeted Aspect or *** based approaches,like dependency-based rules,are quite popular and effective for this ***,they are heavily dependent on the authenticity of the employed parts-of-speech(POS)tagger and dependency *** popular rule based approach is to use sequential rules,wherein the rules formulated by learning from the user’s ***,in general,the sequential rule-based approaches have poor generalization ***,existing approaches mostly consider an aspect as a noun or noun phrase,so these approaches are unable to extract verb *** this article,we have proposed a multi-layered rule-based(ML-RB)technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun ***,after rigorous analysis,we have also constructed rules for the extraction of verb *** verb rules primarily based on the association between verb and opinion *** proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review *** F1 score for both the datasets are 0.90 and 0.88,respectively,which are better than existing *** improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

关键词： Explicit aspect aspect extraction opinion mining rule-based verb aspects

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Development of a prototype of an intelligent system for predicting the quality of dairy manufacture 11

Development of a prototype of an intelligent system for pred...

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11th IEEE International Conference on Intelligent systems, IS 2022

作者： Smitiukh, Yaroslav Samoilenko, Yuliia Kostiuk, Yuliia Kryvoruchko, Olena Stepashkina, Kateryna National University of Food Technologies Department of Automation and Computer Technologies of Control Systems Kyiv Ukraine State University of Trade and Economics Department of Software Engineering and Cyber Security Kyiv Ukraine

ISBN: (纸本)9781665456562

The technological process of the churning process in continuous butter manufacture were considered. The qualitative indicator of the water content of butter was modeled on the basis of a set of industrial data using an artificial neural network (ANN). The prototype of an intelligent system for predicting of the water content of butter allows to increase the information support of the operator-technologist. The proposed model predicts the water content of butter with an error of less than 2%. The input variables of the forecast model prototype are cream fat content, cream ripening temperature, cream supply temperature, frequency of revolutions of the stirrer of the whipping device, and consumption of the normalizing component. Further research is aimed at the development of a decision support information system for dairy manufacture and should be integrated into the subsystem of automated control of the technological process to ensure proper functioning in real time. © 2022 IEEE.

关键词： Intelligent systems

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Audio-Assisted Smart Home Security Monitoring with Few Samples

Audio-Assisted Smart Home Security Monitoring with Few Sampl...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Haotian Chi Qi Ma Yuxuan Zhang Chenglong Fu Yuwei Wang Haijun Geng Xiaojiang Du School of Automation and Software Engineering Shanxi University Taiyuan China Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Department of Electical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon.

关键词： Training Event detection Transfer learning Smart homes Acoustics Internet of Things Security Monitoring Anomaly detection Microphones

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Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Large Language Models are Few-Shot Summarizers: Multi-Intent...

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International Conference on software engineering (ICSE)

作者： Mingyang Geng Shangwen Wang Dezun Dong Haotian Wang Ge Li Zhi Jin Xiaoguang Mao Xiangke Liao College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Key Lab of High Confidence Software Technology Peking University Beijing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.

关键词： computer languages Codes Supervised learning Natural languages Semantics Task analysis software engineering

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Adaptive information fusion network for multi-modal personality recognition

Adaptive information fusion network for multi-modal personal...

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作者： Bao, Yongtang Liu, Xiang Qi, Yue Liu, Ruijun Li, Haojie School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Virtual Reality Research Institute Beihang University Qingdao Research Institute Qingdao China School of Software Beihang University Beijing China

Personality recognition is of great significance in deepening the understanding of social relations. While personality recognition methods have made significant strides in recent years, the challenge of heterogeneity between modalities during feature fusion still needs to be solved. This paper introduces an adaptive multi-modal information fusion network (AMIF-Net) capable of concurrently processing video, audio, and text data. First, utilizing the AMIF-Net encoder, we process the extracted audio and video features separately, effectively capturing long-term data relationships. Then, adding adaptive elements in the fusion network can alleviate the problem of heterogeneity between modes. Lastly, we concatenate audio-video and text features into a regression network to obtain Big Five personality trait scores. Furthermore, we introduce a novel loss function to address the problem of training inaccuracies, taking advantage of its unique property of exhibiting a peak at the critical mean. Our tests on the ChaLearn First Impressions V2 multi-modal dataset show partial performance surpassing state-of-the-art networks. © 2024 John Wiley & Sons Ltd.

关键词： Information fusion

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Data-informed Healthy Cell Clustering Technique for Second-life Applications of Retired Electric Vehicle Batteries

Data-informed Healthy Cell Clustering Technique for Second-l...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Akash Samanta Dominic Karnehm Antje Neve Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada Institute of Intelligent Distributed Systems University of the Bundeswehr Munich Neubiberg Germany

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

The electric vehicles (EVs) adoption rate has been significant in the last decade and keeps on increasing. Recently, the retired EV battery packs which no longer provide satisfactory performance to power an EV started appearing in the market. Typically, EV batteries are declared unfit for EV applications when the battery capacity is reduced to 70-80% of its nominal capacity. Various end-of-life (EOL) options are proposed by researchers before sending the retired batteries to recycle such as low-demanding e-mobility, grid-tired energy storage systems, storage of renewable energy, and home emergency power supplies. It is predicted that this may create new value pools in the energy and transportation sectors shortly. It is noticed in lithium-ion battery packs that all cells do not age similarly, thus clustering of healthy cells among the retired batteries is very crucial for safe and reliable operation in second-life applications. Thus, an efficient cell clustering method utilizing voltage relaxation curves powered by a data-driven BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm is proposed in this paper. The efficiency and efficacy of the proposed algorithm compared to the state-of-the-art cell sorting technique are also demonstrated in the paper.

关键词： Renewable energy sources Accuracy Clustering methods Clustering algorithms Transportation Voltage Electric vehicles

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Unconditional Generation of Scalable Chinese Characters as Bézier Curve Sequences

Unconditional Generation of Scalable Chinese Characters as B...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Rohan Nolan Lasrado V. Vijayasherly School of Computer Science and Engineering VIT University Vellore India Department of Software Systems VIT University Vellore India

Neural generative modelling of sketches has been an active research direction. SketchRNN set a milestone with their sequence-to-sequence variational autoencoder architecture being able to generate hand drawn sketches in various classes by modelling them as sequences of displacements between consecutive stroke points. The diversity and variety in the set of handwritten Chinese characters makes them a good candidate for such generative modelling enabling their unconditional generation. However, modelling them as sequences of points causes coarse looking strokes and much longer sequences, consequently requiring use of polygonal approximation algorithms to cut down on points. Instead, we propose and investigate the modelling of Chinese characters as sequences of Bézier curves using the SketchRNN architecture with a few modifications, to allow the model to directly generate smooth curves. This way the encoded representation is smaller while more of the stroke’s characteristics are retained and the generated characters are truly scalable. We also suggest the appropriate preprocessing strategy for the KanjiVG dataset to make it suitable for this purpose. Qualitative evaluation of the results suggests the model demonstrates generation of characters with mostly well-structured and ordered strokes. This was substantiated by quantitative evaluation based on the FrFréchetchet Inception Distance score.

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