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Dark Patterns Detection on E-Commerce Websites

Dark Patterns Detection on E-Commerce Websites

引用

automation and Computation (AUTOCOM), international conference on

作者： Aarya Sawant Amrisha Gamane Shraddha Sonawane Omkaresh Kulkarni Rutuja Rajendra Patil Gagandeep Kaur Dept. of Computer Science Engineering (Artificial Intelligence and Machine Learning) Vishwakarma Institute of Information Technology Pune India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Dark patterns are techniques employed on e-commerce website interfaces for the purpose of influencing users' actions to engage in undesirable behavior such as making unintentional purchases or submitting personal information when they did not want to. This is where this study seeks to address through identification of such patterns using a machine learning method. To cover false and genuine content, a dataset with 10,000 textual samples, obtained from several e-commerce sites, was compiled. Thus, for analysis, the easily interpretable classification model, Logistic Regression, was used. The results showed that the detection accuracy of the model is 92%, where precision and Recall metrics' point to the model's abilities to detect manipulative language. The results point to the ability of machine learning methods in detecting and combating fraud in cyberspace and thus promoting an ethical e-commerce market.

关键词： Measurement Logistic regression Accuracy Computational modeling Mathematical models Fraud Electronic commerce Problem-solving Protection Random forests

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Design Method for Finite Control Set Model Predictive Controller of Bilinear Converters with Stability Guarantees

Design Method for Finite Control Set Model Predictive Contro...

引用

Smart Grid and Artificial Intelligence (SGAI), international conference on

作者： Weiguang Hu Yuan Cao Chunsheng Wang Feiliang Li School of Automation Central South University Changsha China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

In recent years, Finite Control Set Model Predictive Control (FCS-MPC) has attracted considerable attention in the field of power electronic converter control, resulting in numerous research achievements. However, the stability issues of FCS-MPC systems have not been thoroughly investigated to date, and traditional FCS-MPC controller design methods struggle to provide stability guarantees for the system. To address this issue, this paper proposes an FCS-MPC controller design method that ensures practical stability for a class of power electronic converters that can be modeled as bilinear systems. The proposed method theoretically guarantees system stability and steady-state performance. Therefore, compared to traditional design methods, it achieves good performance without the need for cumbersome parameter tuning. To verify the correctness of the theoretical results, simulations were conducted based on the mathematical model of a boost converter.

关键词： Uncertainty Design methodology Power system stability Predictive models Stability analysis Mathematical models Power electronics Nonlinear systems Tuning Predictive control

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robotics for Personalized Motor Skills Instruction 25

Robotics for Personalized Motor Skills Instruction

引用

ACM/IEEE international conference on Human-Robot Interaction (HRI)

作者： Megha Srivastava Computer Science Department Stanford University Stanford USA

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Despite their ubiquitous role in humans' daily activities and occupations, motor control tasks, such as driving or sports, are rarely taught in a uniform way due to their high complexity and variance. My work focuses on the problem of personalized instruction of motor control tasks, and seeks to leverage robotic technology to improve human motor learning. This includes designing methods for more effective modeling of student learning dynamics, leveraging skill-discovery algorithms to construct personalized training curricula, and training language models to provide students rich and constructive feedback.

关键词： Training Motor drives Heuristic algorithms Human-robot interaction Motors Complexity theory Cognitive science Robots Sports

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Research on the Comprehensive Evaluation of the Power Market Trading Mechanism Based on the Fuzzy Hierarchy Analysis Method

Research on the Comprehensive Evaluation of the Power Market...

引用

Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Chao Wang Jianyu Chen Xianwei Zhang Wentao Feng Xujun Zhang Zongyao Zhu Zefan Zhou Qianyu Cao Jihui Zhang Electricity market sector Gansu power trading Center Co. LTD Gansu China Electric power Research Institute State grid Gansu Electric Power Company Gansu China School of Economics and Management North China Electric Power University Beijing China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

This paper aims to explore the comprehensive evaluation method of the trading mechanism of Chinese electricity market, and design a multi-level evaluation standard framework by fuzzy level analysis. The framework is based on five core considerations: liquidity, stability, economy, development coordination, and transparency, and is deeply developed into 12 secondary indicators and 24 specific assessments. The paper applies the evaluation model to the evaluation of trading mechanism of Gansu power market, and combined with the actual operation of Gansu power market, the results show that the overall evaluation level of trading mechanism of Gansu power market is “good”. Finally, this study highlights the importance of continuous optimization of the evaluation index system and suggests a future focus on the application of more diversified evaluation methods and technologies to accommodate the changing and evolving demands of the electricity market.

关键词： Economics automation Statistical analysis Stability criteria Evaluation models Electricity supply industry Power markets Indexes Optimization Standards

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Integrating Regression models and Deep Learning for Lung Cancer Risk Prediction

Integrating Regression Models and Deep Learning for Lung Can...

引用

automation and Computation (AUTOCOM), international conference on

作者： Divya Sharma Sunil. MP Dhananjay Kumar Yadav Raghunathan S Preetjot Singh Soumen Nayak Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Early diagnosis is vital for successful treatment of lung cancer, as the disease remains one of the most common causes of death by cancer worldwide. Historically, the prediction of lung cancer has been based on regression models and disemboweled by the fact that it cannot optimally handle complex data. In contrast, deep learning techniques have exhibited great potential in developing precise predictive models of disease based on more complicated forms of data; nevertheless, these methods have been less rigorously applied specifically for predicting lung cancer risks. This project aims to combine conventional regression models and deep learning techniques to achieve better performance with more generalizable accuracy for lung cancer risk prediction. A hybrid framework will be developed, exploiting the strengths of regression models in being interpretable and deep learning for its superior prediction. Our proposed methodology will also tackle the issue of high-dimensional imaging data needed for lung cancer risk prediction. These advances in notably conjunctional regression models and deep learning may contribute to better prediction models for lung cancer risk by enhancing the accuracy and translation ability of the developed scores. It will help improve the identification of people at high risk for lung cancer and, on that note, also target the population likely to benefit, thereby ensuring early controls with better outcomes.

关键词： Deep learning Translation Accuracy High dimensional data Computational modeling Noise Neural networks Lung cancer Predictive models Reliability

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Semi-supervised Video Anomaly Detection With Compact Deformable 3D Convolution

Semi-supervised Video Anomaly Detection With Compact Deforma...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shibo Gao Peipei Yang Linlin Huang Beijing Jiaotong University State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semi-supervised video anomaly detection (SVAD) is a challenging computer vision task due to the diversity, randomness, and rarity of abnormal events in videos. A series of important SVAD methods follow the frame prediction strategy, where the model is trained only on normal videos and thus frames with higher prediction error are regarded as anomalies. However, these methods often suffer from inadequate sensitivity to anomalies because the prediction error of anomalous frames is insufficiently distinguishable from the error of normal frames. In this paper, we propose a compact deformable 3D convolution (CD3D) for feature extraction in SVAD models, which effectively enhances the discriminating ability between normal and anomalous frames. In CD3D, the offsets of sampling locations are predicted by applying a set of extra separable 3D convolutions to multiple frames, so that the context information is better utilized with reduced computational costs. Our proposed method achieves the best performance among the methods without resorting to additional supervised information. It can also be conveniently applied to methods that utilize extra supervised information, which further enhances their performances.

关键词： Deformable models Solid modeling Adaptation models Three-dimensional displays Convolution Shape Computational modeling Predictive models Speech processing Anomaly detection

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Behavioral and Hybrid Anomaly Detection Techniques for Enhanced Fraud Prevention in E-Commerce Transactions

Behavioral and Hybrid Anomaly Detection Techniques for Enhan...

引用

Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： Sumanth R Praveenkumar S Challagidad L Lakshmaiah Shilpashree S Surekha R Sakhare Jyothi V Kulkarni Dept. of Computer Science Engineering (Data Science) Nagarjuna College Of Engineering and Technnology Bangalore India Dept. of Computer Science Engineering Nagarjuna College Of Engineering and Technnology Bangalore India Dept. of Electronics and Telecommunication Engineering A G Patil Institute Of Technnology Solapur India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper is designed to develop an automated fraud detection system for e-commerce by leveraging user behavior analysis and hybrid anomaly detection models. As e-commerce grows rapidly, fraud, which poses a huge financial risk and damages brand reputation, is also increasing. Modern fraud detection methods are often code-based or business-centric, limiting their ability to transform fraud strategies. This paper proposes a Behavior and Hybrid Anomaly Detection Model (BHADM) which integrates various data, including usage patterns, transaction details, and historical data, to increase accuracy. The BHAD model that combines vulnerability detection, formality testing, and risk scoring is then used to assess the probability of fraud for each transaction. Based on the risk score calculation, the system triggers alerts and suspicious transactions can be flagged or blocked. In addition, a study is ongoing to monitor performance and update the model to adapt to new fraud models. This method not only improves detection capability, but also maintains the performance of the operation, making it suitable for urgent applications in high-frequency e-commerce environments. The application process provides a unique, flexible and effective way to reduce e-commerce fraud transactions.

关键词： Adaptation models Analytical models Accuracy Transforms Fraud Telecommunication computing Electronic commerce Security Anomaly detection Testing

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PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization

PromptTA: Prompt-driven Text Adapter for Source-free Domain ...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haoran Zhang Shuanghao Bai Wanqi Zhou Jingwen Fu Badong Chen Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Source-free domain generalization (SFDG) tackles the challenge of adapting models to unseen target domains without access to source domain data. To deal with this challenging task, recent advances in SFDG have primarily focused on leveraging the text modality of vision-language models such as CLIP. These methods involve developing a transferable linear classifier based on diverse style features extracted from the text and learned prompts or deriving domain-unified text representations from domain banks. However, both style features and domain banks have limitations in capturing comprehensive domain knowledge. In this work, we propose Prompt-Driven Text Adapter (PromptTA) method, which is designed to better capture the distribution of style features and employ resampling to ensure thorough coverage of domain knowledge. To further leverage this rich domain information, we introduce a text adapter that learns from these style features for efficient domain information storage. Extensive experiments conducted on four benchmark datasets demonstrate that PromptTA achieves state-of-the-art performance. The code is available at https://***/zhanghr2001/PromptTA.

关键词： Adaptation models Codes Benchmark testing Signal processing Feature extraction Data models Acoustics Speech processing

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Early Detection of Melanoma Through Deep Learning-Based Skin Lesion Classification Using VGG16 and InceptionV3

Early Detection of Melanoma Through Deep Learning-Based Skin...

引用

automation and Computation (AUTOCOM), international conference on

作者： Rajeev Kumar Bhaskar Balasubramaniam Kumaraswamy Department of CS & IT Kalinga University Raipur India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

This work aims at investigating classification of skin lesion in order to detect melanoma using deep learning models such as VGG16 and InceptionV3. Skin cancer especially the melanoma needs proper identification for cure to be administered on the affected tissues. The research which underpins this application makes use of convolutional neural networks (CNNs) in an effort to decide whether skin lesions are benign or malignant. In the present study, to assess the performance of the suggested models, the dermoscopic image data set in the public domain was used for training VGG16 and InceptionV3 models. The experimental evaluation demonstrated that, in comparison to the VGG16 network and other relevant algorithms, including ResNet50 and DenseNet121, InceptionV3 provides higher accuracy, precision, recall, F1-score, as well as the ROC-AUC value. InceptionV3 delivered 91.5 % classification accuracy and 92.1 % F1 score representing the enhanced melanoma diagnosis capability of the algorithm with fewer statistical errors between the true positive and true negative cases. The broad fractal dimension analysis feature of the proposed model means that variations in lesion size and texture do not pose a challenge to it. The deep learning models take more time in training than the traditional machine learning methods, although their outcomes justify their employment in clinical practices. The study identifies InceptionV3 as a viable tool for analysis of melanoma images with a view to assist the doctors in early identification of the disease hence enhancing the generality of patients prognosis.

关键词： Deep learning Training Accuracy Melanoma Medical services Skin Data models Classification algorithms Lesions Medical diagnostic imaging

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Visual Question Answering: In-Depth Analysis of methods, Datasets, and Emerging Trends

Visual Question Answering: In-Depth Analysis of Methods, Dat...

引用

automation and Computation (AUTOCOM), international conference on

作者： Pooja Singh Munish Mehta Gourav Bathla M. Batumalay Department of CAE GLA University Mathura India School of Mech. Engg Lovely Professional University Jalandhar India Faculty of IT INTI University Nilai Malaysia

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Visual Question Answering (VQA) is a multi-modal technique that merges computer vision and natural language processing to enable systems to answer questions about images. Recently, several improvements have been made to VQA, driven by its wide applications. In this paper, we analyze and compare key studies, focusing on traditional and modern approaches to VQA. Traditional techniques mostly depend on Convolution Neural Networks (CNN), which extract visual features, while Recurrent Neural Networks (RNN) are used for text-based features. Further, in advanced models transformers, self-attention, and pre-trained CNN models are applied for better accuracy. In this paper, traditional and modern techniques are examined in detail for a better understanding of future researchers. Further, popular VQA datasets like VizWiz, COCO-QA, etc. are discussed for their strengths and limitations. In spite of improvements, VQA system faces issues like handling complex reasoning and achieving generalization among various domains. These open research problems and issues are presented in this paper.

关键词： Training Visualization Computer vision Recurrent neural networks Transformers Market research Feature extraction Question answering (information retrieval) Cognition Long short term memory

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