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IEEE International Conference on Big Data

作者： Katarzyna Prokop Dawid Połap Gautam Srivastava Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland Dept. of Math and Computer Science Brandon University Brandon MB Canada

ISBN: (纸本)9781665480468

Automated guided vehicles (AGV) allow for the automation of operations in warehouse environments. From an application point of view, vehicles can use sensors to move and perform a variety of tasks, including moving objects. In this paper, we focus on the analysis of the environment and the preparation of data for vehicle navigation. The proposed solution is based on two paths of action. In the first, the image of the room is processed by heuristics to locate the robot’s target - the palette. The found pattern allows one to locate the destination as well as create a mask. The mask can be used to train the U-Net network. When a network is trained, the use of heuristics for pallet location can be omitted. Locating the target allows the image to be processed to obtain an AGV navigation map. The proposed solution based on heuristics and U-Net networks has been described and tested in simulations to indicate the potential of the proposed approach.

关键词： Remotely guided vehicles Navigation Heuristic algorithms Stars Big Data Time measurement Sensors

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Simheuristics: An Introductory Tutorial 22

Simheuristics: An Introductory Tutorial

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Proceedings of the Winter Simulation Conference

作者： Angel A. Juan Yuda Li Majsa Ammouriova Javier Panadero Javier Faulin Dept. of Applied Statistics and Operations Research Universitat Politècnica de València Alcoy SPAIN Dept. of Computer Science Universitat Oberta de Catalunya Barcelona SPAIN Institute of Smart Cities Dept. of Statistics Computer Science and Mathematics Public University of Navarre Pamplona SPAIN

Both manufacturing and service industries are subject to uncertainty. Probability techniques and simulation methods allow us to model and analyze complex systems in which stochastic uncertainty is present. When the goal is to optimize the performance of these stochastic systems, simulation by itself is not enough and it needs to be hybridized with optimization methods. Since many real-life optimization problems in the aforementioned industries are NP-hard and large scale, metaheuristic optimization algorithms are required. The simheuristics concept refers to the hybridization of simulation methods and metaheuristic algorithms. This paper provides an introductory tutorial to the concept of simheuristics, showing how it has been successfully employed in solving stochastic optimization problems in many application fields, from production logistics and transportation to telecommunication and insurance. Current research trends in the area of simheuristics, such as their combination with fuzzy logic techniques and machine learning methods, are also discussed.

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Optimizing Energy Consumption in NB-IoT Networks through Enhanced Cell Selection and Reselection Strategy

Optimizing Energy Consumption in NB-IoT Networks through Enh...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Jameel Ali Muhammad Abbas Giuseppe Caso Anas Al-Selwi Karl-Johan Grinnemo Foivos Michelinakis Simula Metropolitan Centre for Digital Engineering Oslo Norway Oslo Metropolitan University Oslo Norway Dept. of Math. and Computer Science Karlstad University Karlstad Sweden

ISBN: (数字)9798331538323

ISBN: (纸本)9798331538330

Cellular Internet of Things (IoT) offers extensive connectivity today and is poised for further growth in the 5G era, especially after the upcoming sunsetting of 2G and 3G networks. It facilitates crucial IoT applications, such as smart metering to reduce energy consumption, smart logistics to enhance distribution efficiency, and smart environmental monitoring to address urban pollution. To support this expansion, leading mobile operators, global vendors, and developers are deploying NB-IoT networks as part of their long-term 5G IoT strategies. A key goal of NB-IoT is to optimize the battery life of IoT devices. While NB-IoT includes several power-saving features, the cell selection and re-selection processes result in significant energy consumption. We conducted a measurement campaign across three locations in two countries, Norway and Sweden, to investigate this issue based on an NB-IoT commercial network. Our findings reveal that cell re-selection frequently occurs even when the IoT device is stationary. Additionally, the reliance on Reference Signal Received Power (RSRP) for cell selection often leads to oscillations between the nearby cells or prolonged attach procedure. To address this challenge, we propose a cell reselection framework that considers RSRP while also considering historical information on transmission reliability. Evaluations of our proposed framework demonstrate energy savings of over 50% compared to legacy RSRP-based cell selection methods.

关键词： Wireless communication Performance evaluation Energy consumption 5G mobile communication Urban pollution Switches Batteries Pollution measurement Internet of Things Reliability

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Blocking Sparse Matrices to Leverage Dense-Specific Multiplication

Blocking Sparse Matrices to Leverage Dense-Specific Multipli...

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Workshop on Irregular Applications: Architecture and Algorithms (IA3)

作者： Paolo Sylos Labini Massimo Bernaschi Werner Nutt Francesco Silvestri Flavio Vella Dept. of Computer Science Free University of Bozen Italy Institute for applied mathematics National Research Council Italy Dept. of Engineering and Computer Science University of Padova Italy Dept. of Engineering and Computer Science University of Trento Italy

ISBN: (纸本)9781665475075

Research to accelerate matrix multiplication, pushed by the growing computational demands of deep learning, has sprouted many efficient architectural solutions, such as NVIDIA's Tensor Cores. These accelerators are designed to process efficiently a high volume of small dense matrix products in parallel. However, it is not obvious how to leverage these accelerators for sparse matrix multiplication. A natural way to adapt the accelerators to this problem is to divide the matrix into small blocks, and then multiply only the nonzero blocks. In this paper, we investigate ways to reorder the rows of a sparse matrix to reduce the number of nonzero blocks and cluster the nonzero elements into a few dense blocks. While this pre-processing can be computationally expensive, we show that the high speed-up provided by the accelerators can easily repay the cost, especially when several multiplications follow one reordering.

关键词： Deep learning Tensors Costs Conferences Focusing computer architecture Software

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Enhancing Printed Circuit Board Defect Detection through Ensemble Learning

Enhancing Printed Circuit Board Defect Detection through Ens...

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Future Intelligent Technologies for Young Researchers (FITYR), IEEE International Workshop on

作者： Ka Nam Canaan Law Mingshuo Yu Lianglei Zhang Yiyi Zhang Peng Xu Jerry Gao Jun Liu Department of Applied Data Science San José State University San José USA Dept. of ISE Virginia Tech Blacksburg USA Dept. of Computer Engineering San José State University San José USA

ISBN: (数字)9798331539757

ISBN: (纸本)9798331539764

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-the-art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes.

关键词： YOLO Electric potential Accuracy Printed circuits Process control Quality control Inspection Ensemble learning Integrated circuit modeling Defect detection

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The Effect of Data Augmentation Techniques on Persian Sentiment Analysis

The Effect of Data Augmentation Techniques on Persian Sentim...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Ali Nazarizadeh Minoo Sayyadpour Touraj Banirostam Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Dept. Math and Computer Science Kharazmi University Tehran Iran

The growth of the Internet and increased use of social media have led to the production of a large amount of unstructured data. These data have many variations, such as text, image, video, and audio. Among them, textual data has the largest share. Considerably textual data comprises the viewpoints of users regarding a wide range of services and products. In order to achieve high accuracy in sentiment classification, Data Augmentation techniques have been used in this paper, which has significantly increased the volume and variety of data. Seventeen different Deep learning models have been trained on the final dataset. The results show that using the Data Augmentation approach has increased the accuracy by more than 6% in Persian sentiment analysis. Methods used in this paper include translating sentences from Farsi to English and vice versa, sentence displacement, replacing synonyms, and data balancing. Also, in this paper, two new Lexicons have been manually created for Persian sentiment analysis. The first is a sentiment lexicon containing 660 positive and negative words, and the second is a lexicon of 390 stop words. Finally, the accuracy obtained using the Data Augmentation approach has reached 96.73%, which is sufficient compared to previous research.

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Enhancing Multiple Sclerosis Lesion Segmentation in Multimodal MRI Scans with Diffusion Models

Enhancing Multiple Sclerosis Lesion Segmentation in Multimod...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Alessia Rondinella Francesco Guarnera Oliver Giudice Alessandro Ortis Giulia Russo Elena Crispino Francesco Pappalardo Sebastiano Battiato Dept. of Math and Computer Science University of Catania Catania Italy Dept. of Drug and Health Sciences University of Catania Catania Italy Dept. of Biomedical and Biotechnological Sciences University of Catania Catania Italy

Accurate segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) scans is crucial for clinical diagnosis and effective treatment planning. In this work, we investigate the effectiveness of Diffusion Models (DM) in achieving pixel-wise segmentation of MS lesions. DM significantly improves segmentation sensitivity, especially in regions with subtle abnormalities. We conducted extensive experiments using the magnetic resonance volumes from a public dataset, encompassing various imaging modalities. Our analysis demonstrated how DM can achieve performance levels that are on par with state-of-the-art techniques, as evidenced by a mean Dice coefficient comparable to the best existing methods. Furthermore, some variants of standard DM exhibits robustness across various imaging modalities, showcasing its versatility in clinical settings.

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InterACT: Enhancing Human-AI Interaction

InterACT: Enhancing Human-AI Interaction

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Jithendra Katta Nikhil Reddy Kodumuru Swathi Katta School of Engineering and Applied Sciences The George Washington University Washington D.C USA Computer Science & Engineering dept. Vignana Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid advancement of large language models and computer vision systems has opened new frontiers in artificial intelligence. This paper introduces InterACT, a novel cross-modal system that integrates leading language and vision models to enable more intuitive and context-aware human AI interactions. By leveraging the strengths of both modalities, InterACT processes natural language queries to guide object detection in visual environments, bridging the gap between linguistic and visual understanding. We evaluate the performance of three language models of varying sizes in conjunction with an advanced object detection model, exploring the balance between computational efficiency and interaction quality. Our experiments demonstrate InterACT's potential across diverse real-world scenarios, revealing promising results in maintaining contextual relevance and adaptability. This research contributes to the growing field of cross-modal AI systems and lays the groundwork for more sophisticated human AI interactions, with implications for applications ranging from assistive technologies to educational tools.

关键词： Visualization computer vision Adaptation models Computational modeling Large language models Object detection Machine learning Computational efficiency Artificial intelligence Context modeling

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EVALUATION OF LARGE LANGUAGE MODELS: STEM EDUCATION AND GENDER STEREOTYPES

arXiv

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arXiv 2024年

作者： Due, Smilla Das, Sneha Andersen, Marianne López, Berta Plandolit Nexø, Sniff Andersen Clemmensen, Line Dept. of Applied Mathematics and Computer Science Technical University of Denmark Denmark High5Girls Helsingør Denmark Danish Data Science Academy Kgs. Lyngby Denmark

Large Language Models (LLMs) have an increasing impact on our lives with use cases such as chatbots, study support, coding support, ideation, writing assistance, and more. Previous studies have revealed linguistic biases in pronouns used to describe professions or adjectives used to describe men vs women. These issues have to some degree been addressed in updated LLM versions, at least to pass existing tests. However, biases may still be present in the models, and repeated use of gender stereotypical language may reinforce the underlying assumptions and are therefore important to examine further. This paper investigates gender biases in LLMs in relation to educational choices through an open-ended, true to user-case experimental design and a quantitative analysis. We investigate the biases in the context of four different cultures, languages, and educational systems (English/US/UK, Danish/DK, Catalan/ES, and Hindi/IN) for ages ranging from 10 to 16 years, corresponding to important educational transition points in the different countries. We find that there are significant and large differences in the ratio of STEM to non-STEM suggested education paths provided by chatGPT when using typical girl vs boy names to prompt lists of suggested things to become. There are generally fewer STEM suggestions in the Danish, Spanish, and Indian context compared to the English. We also find subtle differences in the suggested professions, which we categorise and report. © 2024, CC BY.

关键词： Computational linguistics

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Opinion mining using Double Channel CNN for Recommender System

arXiv

引用

arXiv 2023年

作者： Sayyadpour, Minoo Nazarizadeh, Ali Dept. Math and Computer Science Kharazmi University Tehran Iran Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran

Much unstructured data has been produced with the growth of the Internet and social media. A significant volume of textual data includes users' opinions about products in online stores and social media. By exploring and categorizing them, helpful information can be acquired, including customer satisfaction, user feedback about a particular event, predicting the sale of a specific product, and other similar cases. In this paper, we present an approach for sentiment analysis with a deep learning model and use it to recommend products. A two-channel convolutional neural network model has been used for opinion mining, which has five layers and extracts essential features from the data. We increased the number of comments by applying the SMOTE algorithm to the initial dataset and balanced the data. Then we proceed to cluster the aspects. We also assign a weight to each cluster using tensor decomposition algorithms that improve the recommender system's performance. Our proposed method has reached 91.6% accuracy, significantly improved compared to previous aspect-based approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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