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Beyond Data Collection: Safeguarding User Privacy in Social Robotics 25

Beyond Data Collection: Safeguarding User Privacy in Social ...

引用

25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Dorafshanian, Mahboobeh Aitsam, Muhammad Mejri, Mohamed Di Nuovo, Alessandro Laval University Department Of Computer Science & Software Engineering QC Canada Sheffield Hallam University Department Of Computing Sheffield United Kingdom

ISBN: (纸本)9798350340266

In an era marked by the advanced capabilities of social robots in personal and public spaces, the issue of pervasive data collection by these entities becomes increasingly pertinent. Social robots, deployed by government entities, hospitals, and corporations, are at the forefront of gathering sensitive personal data, necessitating careful consideration of privacy concerns. The vast amounts of data collected by these robots, while beneficial for decision-making and fostering research, also pose significant privacy risks. In particular, the challenge intensifies when robots collect and potentially share data that includes sensitive personal information. This paper presents a user-friendly Differential Privacy (DP) library that addresses this challenge. The library incorporates a risk threshold and evaluates the potential impact of data disclosure to accurately quantify privacy levels. Designed for nontechnical users, it enables the secure release of statistical data without the risk of privacy breaches. With privacy breaches and re-identification becoming increasingly common, this library offers a robust solution for safeguarding individuals' privacy while facilitating the sharing of valuable insights. © 2024 IEEE.

关键词： Sensitive data

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CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models

CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for ...

引用

2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Zhang, Qin Cai, Sihan Zhao, Jiaxu Pechenizkiy, Mykola Fang, Meng College of Computer Science and Software Engineering Shenzhen University China Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9798891761681

Ambiguity is an inherent feature of language, whose management is crucial for effective communication and collaboration. This is particularly true for Chinese, a language with extensive lexical-morphemic ambiguity. Despite the wide use of large language models (LLMs) in numerous domains and their growing proficiency in Chinese, there is a notable lack of datasets to thoroughly evaluate LLMs' ability to handle ambiguity in Chinese. To bridge this gap, we introduce the CHAmbi dataset, a specialized Chinese multi-label disambiguation dataset formatted in Natural Language Inference. It comprises 4,991 pairs of premises and hypotheses, including 824 examples featuring a wide range of ambiguities. In addition to the dataset, we develop a series of tests and conduct an extensive evaluation of pre-trained LLMs' proficiency in identifying and resolving ambiguity in the Chinese language. Our findings reveal that GPT-4 consistently delivers commendable performance across various evaluative measures, albeit with limitations in robustness. The performances of other LLMs, however, demonstrate variability in handling ambiguity-related tasks, underscoring the complexity of such tasks in the context of Chinese. The overall results highlight the challenge of ambiguity handling for current LLMs and underscore the imperative need for further enhancement in LLM capabilities for effective ambiguity resolution in the Chinese language. © 2024 Association for Computational Linguistics.

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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Enhancing Skin Lesion Classification with Ensemble Data Augmentation and Convolutional Neural Networks 2nd

Enhancing Skin Lesion Classification with Ensemble Data Augm...

引用

2nd International Congress of Electrical and computer engineering, ICECENG 2023

作者： Onan, Aytug Bulut, Vahide Ezgi, Ahmet İzmir Katip Celebi University Department of Computer Engineering Izmir Turkey İzmir Katip Celebi University Division of Software Engineering Izmir Turkey İzmir Katip Celebi University Department of Engineering Sciences Izmir Turkey

ISBN: (纸本)9783031527593

Convolutional neural networks (CNNs) have gained considerable recognition due to their achievements in image classification tasks, notably in the domain of medical imaging. However, the scarcity of accurately labeled datasets, especially concerning skin lesion images, poses significant challenges. The manual annotation of medical images, such as those related to skin lesions, is a labor-intensive process requiring expertise. To overcome the limited availability of labeled data, data augmentation emerges as a crucial solution. This technique involves applying various transformations to the original images, enriching the training dataset and improving the model’s generalization. This chapter provides a comprehensive examination of both traditional and ensemble data augmentation methods tailored specifically for skin lesion classification. Traditional methods encompass geometric and color-space transformations, while ensemble methods combine multiple augmentation strategies. Additionally, besides focusing on data augmentation, this study employs four prominent CNN architectures: ResNet50, MobileNetV2, EfficientNet, and DenseNet. Through empirical analysis, this research underscores the significant impact of data augmentation on model accuracy and robustness, especially when dealing with limited annotated data. These findings contribute to the advancement of automated skin lesion identification, offering potential applications in clinical settings by addressing the challenges posed by data scarcity and annotation complexity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image classification

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Opinion Spam Detection with Attention-Based LSTM Networks 20th

Opinion Spam Detection with Attention-Based LSTM Networks

引用

20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Sedighi, Zeinab Ebrahimpour-Komleh, Hossein Bagheri, Ayoub Kosseim, Leila Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Computer Engineering University of Kashan Kashan Iran Department of Methodology and Statistics Utrecht University Utrecht Netherlands

ISBN: (纸本)9783031243394

Today, online reviews have a great influence on consumers’ purchasing decisions. As a result, spam attacks, consisting of the malicious inclusion of fake online reviews, can be detrimental to both customers as well as organizations. Several methods have been proposed to automatically detect fake opinions;however, the majority of these methods focus on feature learning techniques based on a large number of handcrafted features. Deep learning and attention mechanisms have recently been shown to improve the performance of many classification tasks as they enable the model to focus on the most the important features. This paper describes our approach to apply LSTM and attention-based mechanisms for detecting deceptive reviews. Experiments with the Three-domain data set [15] show that a BiLSTM model coupled with Multi-Headed Self Attention improves the F-measure from 81.49% to 87.59% in detecting fake reviews. © 2023, Springer Nature Switzerland AG.

关键词： Long short-term memory

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Customer Sentiments Towards Delivery Services in Bangladesh: A Machine Learning-Based Sentiment Analysis

Customer Sentiments Towards Delivery Services in Bangladesh:...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Bitto, Abu Kowshir Arman, Md. Shohel Saha, Rakhi Moni Jahan, Hasnur Mahmud, Imran Das, Aka Daffodil International University Department of Software Engineering Dhaka1207 Bangladesh Premier University Department of Computer Science Chattogram4203 Bangladesh

ISBN: (纸本)9798350350289

Bangladesh has a large population, which is causing the delivery system to grow up, day by day. Therefore, several companies that provide these delivery services usually referred to as 'Currier Service', are growing gradually. In this paper, we focus on the top delivery service companies in Bangladesh. We analyze customer sentiment based on reviews and comments collected from their social media pages. We collected data of customers' comments from the verified social media pages of Sundorban Currier Services, Redx Currier Services, and Pathao Delivery System. We used a combination of Natural Language Processing and machine learning models to categorize Bangla sentiment reviews based on N-gram features. We used the Unigram, Bigram, and Trigram methods to identify the number of negative and positive reviews on real-life social media platforms and to predict whether new reviews were negative or positive. We have found that the Bigram feature is the best for this analysis because it has the highest accuracy of 90.72%. Using the Bangla NLP approach, the machine learning models are categorized in negative and positive reviews with the sentiment analysis method. © 2024 IEEE.

关键词： Sentiment analysis

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Towards Artist Recognition Based on Material Rendering. A Case Study for Recognition of Rembrandt and Van Dyck

Towards Artist Recognition Based on Material Rendering. A Ca...

引用

IS and T International Symposium on Electronic Imaging 2024: computer Vision and Image Analysis of Art 2024

作者： Huang, Jing Elgammal, Ahmed Department of Big Data and Software Engineering Chongqing University Chongqing China Department of Computer Science Rutgers University NJ United States

Can artists be recognized from the way they render certain materials, such as fabric, skin, or hair? In this paper, we study this problem with a focus on recognizing works by Rembrandt, Van Dyck, and other Dutch and Flemish artists from the same era. This paper proposes a novel material-based approach based on Swin Transformer and Cascade Mask R-CNN to address artist recognition task. We report the performance on a dataset of 644 images. Additionally, the model’s robustness to image variations is studied. © 2024, Society for Imaging Science and Technology.

关键词： Rendering (computer graphics)

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Revolutionizing software Defect Prediction Through Deep Learning 7

Revolutionizing Software Defect Prediction Through Deep Lear...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Selvin Jose, G. Charles, J. Noorul Islam for Higher Education Department of Computer Science India Noorul Islam Centre for Higher Education Department of Software Engineering India

ISBN: (纸本)9798350372816

This study aims to revolutionize software defect prediction by leveraging deep learning (DL) techniques, specifically focusing on Convolutional Neural Networks (CNN) and Stack Sparse Autoencoders (SSAE). The research involves training these models on datasets from the NASA Metrics Data Program, using metrics such as accuracy, precision, detection rate, and True Negative Rate (TNR) to evaluate their performance. The proposed methodology includes normalization of data, application of neural network architectures, and extensive experimentation with varying parameters. Results demonstrate that CNN outperforms SSAE, achieving a higher accuracy range of 0.84 to 0.93 compared to SSAE's 0.80 to 0.90, particularly excelling on the PC1 dataset with an accuracy of 0.93. Both models, however, show strong capabilities in predicting software defects, with CNN consistently delivering better performance across diverse datasets. The study concludes that DL models, especially CNN, significantly enhance the efficiency and accuracy of software defect prediction, suggesting future research to explore additional DL techniques and larger datasets for further advancements in software quality assessment. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Optimization Algorithm for Data Collection with Unknown Objective Function 8

Research on Optimization Algorithm for Data Collection with ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Zhang, Chong Yadav, Amit Tangyaling, Tangyaling Khan, Asif Chengdu Neusoft University Software Engineering Department Chengdu China Charles Darwin University College of Engineering It and Environment NT Australia Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350376425

In wireless sensor networks (WSN), data collection is a crucial and complex issue. Especially when the objective function is unknown, designing an effective data collection optimization algorithm has become a huge challenge. In response to the above issues, this article investigates an algorithm designed for optimizing data collection when the objective function is unknown. An optimization method based on data priority transmission scheme and adaptive adjustment of sensor node duty cycle is proposed to address the issues of data transmission and energy consumption in wireless sensor networks (WSN). On this basis, a distributed stochastic free gradient optimization algorithm was designed, which can approximate the optimal solution through iterative optimization process when the objective function is unknown. © 2024 IEEE.

关键词： Optimization algorithms

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Intelligent Sorting System 7

Intelligent Sorting System

引用

7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Mao, Yanghong Chen, Chuixin Pan, Zhengjun Guangzhou Institute of Science and Technology Department of Computer Science and Engineering Guangzhou China Institute of Guangzhou Department of Software Engineering Software Engineering Guangzhou China Department of Software Engineering Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (纸本)9781665487115

In order to achieve the goal that the manipulator can automatically obtain any position within its working range, and can adjust the end actuator for grasping, a machine vision sorting system is designed. The system passes through image processing, target recognition and positioning, and finally realizes the sorting effect through trajectory planning. Aiming at the problem that the deep learning algorithm takes a long time, The traditional image processing algorithm and deep learning target detection algorithm are organically combined to reduce the use delay of image processing in the system. Because the training of deep learning model requires a large number of data sets, but the amount of data in manufacturing factories is small, and data labeling is time-consuming and laborious, YOLO v3 algorithm is used to improve the efficiency of training model under a small number of labeled data sets, SIFT algorithm is used to extract image features, and K-D tree algorithm is used to match image features. In the algorithm of position, the centroid is extracted as the positioning reference coordinate, the kinematics model of the manipulator is established. trajectory planning, straight-line and arc interpolation algorithms are used. The experimental results show that the system meets the design requirements. © 2022 IEEE.

关键词： Deep learning Training Trajectory planning Target recognition Object detection Feature extraction Manipulators

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