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A New Multi-Source Light Detection Benchmark and Semi-Supervised Focal Light Detection 38

A New Multi-Source Light Detection Benchmark and Semi-Superv...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Baek, Jae-Yong Yoo, Yong-Sang Bae, Seung-Hwan Inha University Dept. of Electrical and Computer Engineering Vision and Learning Lab Korea Republic of

This paper addresses a multi-source light detection (LD) problem from vehicles, traffic signals, and streetlights under driving scenarios. Albeit it is crucial for autonomous driving and night vision, this problem has not been yet focused on as much as other object detection (OD). One of the main reasons is the absence of a public available LD benchmark dataset. Therefore, we construct a new large LD dataset consisting of different light sources via heavy annotation:YouTube Driving Light Detection dataset (YDLD). Compared to the existing LD datasets, our dataset has much more images and box annotations for multi-source lights. We also provide rigorous statistical analysis and transfer learning comparison of other well-known detection benchmark datasets to prove the generality of our YDLD. For the recent object detectors, we achieve the extensive comparison results on YDLD. However, they tend to yield the low mAP scores due to the intrinsic challenges of LD caused by very tiny size and similar appearance. To resolve those, we design a novel lightness focal loss which penalizes miss-classified samples more and a lightness spatial attention prior by reflecting a global scene context. In addition, we develop a semi-supervised focal light detection (SS-FLD) by embedding our lightness focal loss into the semi-supervised object detection (SSOD). We prove that our methods can consistently boost mAP to the variety of types of recent detectors on YDLD. We will open both YDLD and SS-FLD code at https://***/YDLD-dataset/YDLD. © 2024 Neural information processing systems foundation. All rights reserved.

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Detecting Conceptual Abstraction in LLMs 30

Detecting Conceptual Abstraction in LLMs

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Regneri, Michaela Abdelhalim, Alhassan Laue, Sören Universität Hamburg Dept. of Computer Science Machine Learning Research Group Hamburg Germany

ISBN: (纸本)9782493814104

We present a novel approach to detecting noun abstraction within a large language model (LLM). Starting from a psychologically motivated set of noun pairs in taxonomic relationships, we instantiate surface patterns indicating hypernymy and analyze the attention matrices produced by BERT. We compare the results to two sets of counterfactuals and show that we can detect hypernymy in the abstraction mechanism, which cannot solely be related to the distributional similarity of noun pairs. Our findings are a first step towards the explainability of conceptual abstraction in LLMs. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： abstraction attention explainability transformers

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Visual Coherence Loss for Coherent and Visually Grounded Story Generation 8

Visual Coherence Loss for Coherent and Visually Grounded Sto...

引用

8th Workshop on Representation learning for NLP, RepL4NLP 2023, co-located with ACL 2023

作者： Hong, Xudong Demberg, Vera Sayeed, Asad Zheng, Qiankun Schiele, Bernt Dept. of Computer Vision and Machine Learning MPI Informatics Germany Dept. of Language Science and Technology Dept. of Computer Science Saarland University Germany Dept. of Philosophy Linguistics and Theory of Science University of Gothenburg Sweden Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9781959429777

Local coherence is essential for text generation models. We identify two important aspects of local coherence within the visual storytelling task: (1) the model needs to represent re-occurrences of characters within the image sequence in order to mention them correctly in the story;(2) character representations should enable us to find instances of the same characters and distinguish different characters. In this paper, we propose a loss function inspired by a linguistic theory of coherence for learning image sequence representations. We further propose combining features from an object detector and a face detector to construct stronger character features. To evaluate visual grounding that current reference-based metrics do not measure, we propose a character matching metric to check whether the models generate referring expressions correctly for characters in input image sequences. Experiments on a visual story generation dataset show that our proposed features and loss function are effective for generating more coherent and visually grounded stories. Our code is available at https://***/vwprompt/vcl. © 2023 Association for Computational Linguistics.

关键词： Electric grounding

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Optimizing CNNs for Leaf Disease Detection: A Comparative Analysis of Algorithms, Activation functions, and Performance Metrics

Optimizing CNNs for Leaf Disease Detection: A Comparative An...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Nitisha, S. Mohith, N. Vinay, R. Agarwal, Monika Sinha, Aparajita Mokshitha, M. Dayananda Sagar University Dept. Artificial Intelligence and Machine Learning Bangalore India National Institute of Technology Dept. Computer Science Engineering Agartala India

ISBN: (纸本)9798331540821

Agriculture plays an important role in ensuring food security. However, the emergence of plant diseases causes significant economic losses. The routine method of detecting crop diseases is time consuming, requires technical skill and can be inaccurate, especially when new diseases are introduced. To solve these problems, we present a new and effective method for the early identification of leaf diseases using computer vision and machine learning. Extensive testing has been done on the widely used Plant Village dataset to ensure accurate findings. Our plans focus on disease diagnosis and classification. This study demonstrates the pros and drawbacks of this method. The testing process includes data collection, data training, feature extraction, model testing, and classification. The main processing techniques include image acquisition, image preprocessing, extraction and the use of neural networks. Combining computer vision and machine learning, our approach holds great promise for efficient and reliable detection of crop diseases. This has important implications for improving disease control, reducing economic losses and increasing food security. © 2024 IEEE.

关键词： Agriculture Classification CNN computer vision Plant diseases

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Understanding Red Wine Quality with Feature Importance in machine learning and Explainable AI 15

Understanding Red Wine Quality with Feature Importance in Ma...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： John, Ayain Damodar, Navya Mohith, N. Vijayan, Bharathy Dayananda Sagar University Dept. Artificial Intelligence and Machine Learning Bangalore India Dayananda Sagar University Dept. Computer Science Engineering Bangalore India

ISBN: (纸本)9798350370249

The research work investigates the factors that influence the quality of red wine and how they contribute to positive outcomes. It uses machine learning techniques like Support Vector machines (SVM) and K-nearest neighbors (KNN) to classify and assess red wine quality data. Traditionally, red wine quality has been evaluated subjectively, which presents challenges. machine learning algorithms offer an objective alternative. This study compares SVM and KNN algorithms in assessing red wine quality and aims to determine the crucial attributes that affect its quality. To evaluate the classification performance of different machine learning algorithms, metrics like specificity, sensitivity, selectivity, and F1 measure are used. Additionally, grid search algorithms are employed for parameter optimization. In the red wine datasets, the Support Vector machine (SVM) model obtains an accuracy rate of 89%, surpassing the K-Nearest Neighbors (KNN) model's accuracy rate of 87%, especially regarding predictive specificity. Furthermore, using grid search algorithms improves the overall performance of the classifiers. This study highlights the effectiveness of machine learning in assessing red wine quality, providing valuable information for producers to optimize wine production processes and ensure high-quality products for consumers. © 2024 IEEE.

关键词： Adversarial machine learning

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Transformer based Compressed Data Recovery for Efficient Data Collection in Wireless Sensor Networks

Transformer based Compressed Data Recovery for Efficient Dat...

引用

2024 IEEE Students Conference on Engineering and Systems, SCES 2024

作者： Balaji, V.S. Sekar, K. Devendar Rao, B. Dheepa, T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

Data collection is challenging in wireless sensor networks (WSNs) since energy consumption remains a significant constraint. Although energy consumption has increased, most data collection methods incur excessive computation overhead. Compressive sensing is used to minimize the number of sensing samples with a suitable sparse basis. A novel Transformer neural network-based compressed data recovery (TCDR) method has been proposed for extracting temporal correlation structures from compressed signals and reconstructing them with high accuracy. The compressed signal is forwarded to the base station through multi-hop communication. The base station uses an attentionbased Transformer Neural Network (TNN) to recover the compressed signal. This model provides greater energy saving performance than the other baseline models considered for evaluation, which are 5.23 percent, 6.82 percent, 10.26 percent, and 11.47 percent, respectively. © 2024 IEEE.

关键词： Spatio-temporal data

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Revolutionizing Obstetric Care: IoT, AI-Enabled, and Data-Driven Partograph System

Revolutionizing Obstetric Care: IoT, AI-Enabled, and Data-Dr...

引用

2023 International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Zaveri, Dishant Jagtap, Vividha Gill, Jasleen Jain, Krishi Sheth, Shivam Shekokar, Narendra Djsce Dept. of Computer Engineering Mumbai India Djsce Dept. of Artificial Intelligence and Machine Learning Mumbai India Mumbai India

ISBN: (纸本)9798350314878

This research paper introduces a comprehensive real-time labor and delivery monitoring system that uses various hardware devices like ultrasound transducers, cardiotocographs, sphygmomanometers, and IoT devices. It creates a database accessible via a dedicated mobile app for doctors, nurses, and receptionists. The system includes features to identify and notify medical staff of potential risks such as preterm labor or fetal distress, using SMS alerts and LED lights for immediate attention. In emergencies, it notifies relevant medical personnel and receptionists for urgent interventions like cesarean sections. If needed facilities aren't available nearby, the system searches for hospitals within an 8-kilometer radius and sends alerts to check availability, arranging for an ambulance if necessary. This system allows real-time access to patient data, leading to faster responses and improved outcomes, enhancing decision-making and reducing maternal and child mortality through timely interventions and improved maternal care quality. © 2023 IEEE.

关键词： Ultrasonic transducers

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PAC-Bayes-Chernoff bounds for unbounded losses 38

PAC-Bayes-Chernoff bounds for unbounded losses

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Casado, Ioar Ortega, Luis A. Pérez, Aritz Masegosa, Andrés R. Spain Machine Learning Group Computer Science Dept. EPS Universidad Autónoma de Madrid Spain Department of Computer Science Aalborg University Denmark

We introduce a new PAC-Bayes oracle bound for unbounded losses that extends Cramér-Chernoff bounds to the PAC-Bayesian setting. The proof technique relies on controlling the tails of certain random variables involving the Cramér transform of the loss. Our approach naturally leverages properties of Cramér-Chernoff bounds, such as exact optimization of the free parameter in many PAC-Bayes bounds. We highlight several applications of the main theorem. Firstly, we show that our bound recovers and generalizes previous results. Additionally, our approach allows working with richer assumptions that result in more informative and potentially tighter bounds. In this direction, we provide a general bound under a new model-dependent assumption from which we obtain bounds based on parameter norms and log-Sobolev inequalities. Notably, many of these bounds can be minimized to obtain distributions beyond the Gibbs posterior and provide novel theoretical coverage to existing regularization techniques. © 2024 Neural information processing systems foundation. All rights reserved.

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Customer Behaviour Analysis: Tools Overview 2

Customer Behaviour Analysis: Tools Overview

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Bharti, Jigyasa Dongre, Snehlata Thorat, Mayuri G H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India PES's Modern College of Engineering Dept. of Artificial Intelligence and Machine Learning Pune India

ISBN: (纸本)9798350383287

Analytics is a process of collecting and analyzing data that can help in taking meaningful decisions. The data is collected from various sources, this raw data is then processed and converted to dashboards which assists in business analyzation. Analytics are of various types such as descriptive, diagnostic, predictive, prescriptive and text. One area of analytics is Business Intelligence which is a broader concept of using various tools and methods that involves presentation of business data to support decision making. Customer behavioral analysis can be conducted by having insights from web analytics. It is a process of understanding how a customer is responding to a website. Regular improvements can be done to increase customer interaction, enhance user experience and data-driven decision-making. In this paper, we will be discussing two major tools that are used for behavior analysis. © 2024 IEEE.

关键词： Decision making

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Sign Language Detection in Real-Time Applications

Sign Language Detection in Real-Time Applications

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Milinda, Renata Rachael Sara Ramsiej, Barrister Yasmin, Reshma Ambeth Kumar, V.D. Sekar, K. Anna University India Dept. of Artificial Intelligence and Machine Learning Anna University India Dept. of Computer Engineering Mizoram University Aizawl India Department of AIML Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331510299

Individuals with hearing and speech impairments often rely on gesture language for communication, but a lack of widespread understanding of this language among others creates significant barriers. A machine learning model that recognizes hand signals and converts them into English can help bridge this gap, facilitating communication between hearing and deaf individuals. Existing non-verbal communication identification systems, while leveraging computational learning and AI models for both single- and double-handed gestures, generally lack real-time capabilities. This research proposes a live symbolic language identification platform that uses a webcam to build a regional gesture communication dataset and TensorFlow for cross-domain learning. Despite a smaller dataset, the system targets high accuracy in recognition. Advanced technologies, including computer vision and deep learning, are applied to improve communication for deaf individuals by developing accessible technical applications and platforms. The proposed model, utilizing TensorFlow and OpenCV, aims to identify commonly used American Sign Language gestures in real time with accuracy and efficiency, including signs such as "hello, " "thanks, " "bye, " "yes, " "no, " "dad, " and "mom". © 2024 IEEE.

关键词： Adversarial machine learning

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