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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Abdu Saif Nor Shahida Mohd Shah Ali Ameen Aiman Alnoamani Majjed Al-Qatf Saeed Hamood Alsamhi Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Computer Science Lincoln University College Kuala Lumpur Malaysia Insight Center for Data Analytics University of Galway Galway Ireland

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Energy consumption is critical in self-driving cars and is set to change how transportation works. In this paper, we address the challenges of vehicle energy consumption by examining how the location of self-driving cars affects their energy use over time, looking at two-and three-dimensional aspects. Using models and real-world data, we examine the energy usage patterns of vehicle systems within MATLAB to reveal insights into their design and operation. In this paper, the power dynamics of cars that significantly contribute to the discussions of practical and sustainable self-driving transportation are introduced. The main goal is to demonstrate how power management may be revolutionized for self-driving car systems, ensuring maximum performance and dependability in today’s dynamic mobility scene. The findings show that enhancing autonomous vehicles’ design and operational strategies ensures their reliability and optimizes performance in today’s rapidly evolving transportation landscape.

关键词： Energy consumption Three-dimensional displays Transportation Reliability engineering data models Autonomous automobiles Automobiles Vehicle dynamics Sustainable development MATLAB

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Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation

arXiv

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arXiv 2023年

作者： Peng, Bo Burns, Ben Chen, Ziqi Parthasarathy, Srinivasan Ning, Xia Department of Computer Science and Engineering The Ohio State University Columbus United States Department of Biomedical Informatics The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

In recent years, with large language models (LLMs) achieving state-of-the-art performance in context understanding, increasing efforts have been dedicated to developing LLM-enhanced sequential recommendation (SR) methods. Considering that most existing LLMs are not specifically optimized for recommendation tasks, adapting them for SR becomes a critical step in LLM-enhanced SR methods. Though numerous adaptation methods have been developed, it still remains a significant challenge to adapt LLMs for SR both efficiently and effectively. To address this challenge, in this paper, we introduce a novel side sequential network adaptation method, denoted as SSNA, for LLM-enhanced SR. SSNA features three key designs to allow both efficient and effective LLM adaptation. First, SSNA learns adapters separate from LLMs, while fixing all the pre-trained parameters within LLMs to allow efficient adaptation. In addition, SSNA adapts the top-a layers of LLMs jointly, and integrates adapters sequentially for enhanced effectiveness (i.e., recommendation performance). We compare SSNA against five state-of-the-art baseline methods on five benchmark datasets using three LLMs. The experimental results demonstrate that SSNA significantly outperforms all the baseline methods in terms of recommendation performance, and achieves substantial improvement over the best-performing baseline methods at both run-time and memory efficiency during training. Our analysis shows the effectiveness of integrating adapters in a sequential manner. Our parameter study demonstrates the effectiveness of jointly adapting the top-a layers of LLMs. © 2023, CC BY.

关键词： Computational linguistics

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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Research on Traffic Sign Detection and Recognition System Using Deep Ensemble Learning

Research on Traffic Sign Detection and Recognition System Us...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Lung-Jen Wang Taweelap Suwattanapunkul Jarinya Thalauy Parinya Jansengrat Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan International Master Program in Information Technology and Applications International College National Pingtung University Pingtung Taiwan Department of Big Data Management and Analytics Rajamangala University of Technology Thanyaburi Pathum Thani Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.

关键词： YOLO Roads Prediction algorithms Inference algorithms Internet Autonomous automobiles Ensemble learning

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Suggestion Mining for Generating New Services and New Product Ideas from YELP Reviews 5th

Suggestion Mining for Generating New Services and New Produc...

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5th Brazilian Technology Symposium, BTSym 2019

作者： Collucci, Daniel Junqueira, Alexandre Forster, Carlos Itaú-Unibanco Data Science Team São Paulo Brazil Itaú-Unibanco Advanced Analytics Team São Paulo Brazil Computer Science Department Aerospace Technology Institute São Paulo Brazil

ISBN: (纸本)9783030575472

YELP reviews strongly influence the business’s reputation and revenue as well as customers and new-users attitudes toward the acquisition of products and services. Due to the overwhelming number of customer reviews available on Web sites, usually, the decision-making process of customers, new-users, and business owners, is not associated with the daunting task of reading reviews online. Text mining can address the issue of summarizing huge volumes of unstructured data;furthermore, text mining can extract emotions, sentiments, and insights from texts written by customers. This study proposes a text mining approach to unravel suggestions from YELP reviews. This study shows that suggestion mining differentiates reviews with suggestions from reviews without suggestions and therefore can identify new services and new product ideas from customer reviews. © 2021, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Adaptive computer Vision Solutions for Individuals with Special Needs

Adaptive Computer Vision Solutions for Individuals with Spec...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Issam Jebreen Shadi Nashwan Ahmad Al-Qerem Ali Mohd Ali Mohammad A Al Khaldy Mohammed Rajab Faculty of Information Technology Middle East University Jordan Computer Science Department Zarka University Jordan College of Engineering Technology Al-Balqa Applied University Jordan Business Intelligence Data Analytics University of Petra Amman Jordan University Headquarter University Of Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

With the rapid evolution of artificial intelligence (AI) and computer vision, assistive technologies have become increasingly adept at supporting individuals with special needs. This paper provides a comprehensive examination of two critical areas: AI-powered visual assistance systems for the visually impaired and sign language recognition and generation frameworks for the Deaf and hard-of-hearing communities. We review the latest research trends, algorithms, and tools, highlighting emerging deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers in both domains. We then propose a unified framework that leverages edge computing, multimodal learning, and advanced human-computer interaction (HCI) techniques to address challenges such as real-time object recognition, complex gesture capture, data scarcity, and cultural variations in sign languages. We further detail an experimental design and set of results that demonstrate the feasibility and benefits of these approaches, followed by a discussion of ethical considerations, limitations, and future directions. Our goal is to illustrate a path toward more inclusive AI systems that offer robust solutions for accessibility, ultimately bridging communication gaps and fostering independence for individuals with special needs [1]–[11].

关键词： Sign language computer vision Visualization Ethics Visual impairment Assistive technologies Transformers Real-time systems Object recognition Artificial intelligence

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A hybrid estimation of distribution algorithm for joint stratification and sample allocation

arXiv

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arXiv 2022年

作者： O'Luing, Mervyn Prestwich, Steven Tarim, S. Armagan Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland Cork University Business School University College Cork Ireland

In this study we propose a hybrid estimation of distribution algorithm (HEDA) to solve the joint stratification and sample allocation problem. This is a complex problem in which each the quality of each stratification from the set of all possible stratifications is measured its optimal sample allocation. EDAs are stochastic black-box optimization algorithms which can be used to estimate, build and sample probability models in the search for an optimal stratification. In this paper we enhance the exploitation properties of the EDA by adding a simulated annealing algorithm to make it a hybrid EDA. Results of empirical comparisons for atomic and continuous strata show that the HEDA attains the bests results found so far when compared to benchmark tests on the same data using a grouping genetic algorithm, simulated annealing algorithm or hill-climbing algorithm. However, the execution times and total execution are, in general, higher for the HEDA. © 2022, CC BY.

关键词： Simulated annealing

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Intelligent shifting cues: Increasing the awareness of multi-device interaction opportunities 21

Intelligent shifting cues: Increasing the awareness of multi...

引用

29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Nguyen, Ngoc Thi Zuniga, Agustin Flores, Huber Lee, Hyowon Perrault, Simon Tangi Nurmi, Petteri Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Computer Science University of Helsinki Helsinki Finland Institute of Computer Science University of Tartu Tartu Estonia Insight Centre for Data Analytics Dublin City University Dublin Ireland

ISBN: (纸本)9781450383660

The ever-increasing ubiquity of smart devices is creating new opportunities for people to interact and engage with digital information using multiple devices. In the simplest case this can refer to choosing which device to use for a particular task (e.g., phone, laptop or smartwatch), whereas a more complex example is simultaneously taking advantage of the capabilities of different devices (e.g., laptop and smart TV). Despite these types of opportunities becoming increasing available, currently the full potential of multi-device interactions is not being realized as people struggle to take advantage of them. As our first contribution, we study people's willingness to engage with multi-device interactions and rank the factors that mediate this response through an online survey (N = 60). Our results show that users are strongly in favour of using multiple devices, but lack the awareness or information to engage with them, or feel that establishing the interactions is too laborious and would disrupt the fluidity of the interactions. Motivated by this result, as our second contribution we design and evaluate intelligent shifting cues, visualizations that offer information about available interaction opportunities and how to establish them, and study how they influence users willingness to engage in multi-device interactions. Results of our study show that the cues can be effective in helping people to engage with multiple devices, but that the suitability of the proposed device and fit with task are important mediating factors. We end the paper by deriving design implications for intelligent systems that can support people in engaging with multi-device interactions. © 2021 ACM.

关键词： Intelligent systems

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A Novel Framework for Detection of Digital Face Video Manipulation using Deep Learning

A Novel Framework for Detection of Digital Face Video Manipu...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Sanskriti Chandra Shivam Saxena Santosh Kumar Mithilesh Kumar Chaube Srinivas K G Saeed Hamood Alsamhi Edward Curry Abdu Saif Department of Data Science and Artificial Intelligence IIIT-Naya Raipur Chhattisgarh India Department of Computer Science and Engineering IIIT-Naya Raipur Chhattisgarh India Department of Mathematical Sciences IIIT-Naya Raipur Chhattisgarh India Insight SFI Research Centre for Data Analytics University of Galway Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. Researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security.

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Model Comparisons: XNet Outperforms KAN

arXiv

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arXiv 2024年

作者： Li, Xin Xia, Zhihong Zheng, Xiaotao Department of Computer Science Northwestern University EvanstonIL United States Mathematical Modelling and Data Analytics Center Oxford Suzhou Centre for Advanced Research Suzhou China School of Natural Science Great Bay University Guangdong China Department of Mathematics Northwestern University EvanstonIL United States Department of Mathematics Soochow University Suzhou China

In the fields of computational mathematics and artificial intelligence, the need for precise data modeling is crucial, especially for predictive machine learning tasks. This paper explores further XNet, a novel algorithm that employs the complex-valued Cauchy integral formula, offering a superior network architecture that surpasses traditional Multi-Layer Perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs). XNet significant improves speed and accuracy across various tasks in both low and high-dimensional spaces, redefining the scope of data-driven model development and providing substantial improvements over established time series models like LSTMs. © 2024, CC BY.

关键词： Choquet integral

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