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18th IEEE India Council International Conference, INDICON 2021

作者： Pisipati, Manasa Nandy, Anup National Institute of Technology-Rourkela Machine Intelligence and Bio-Motion Lab Department of Computer Science and Engineering Rourkela India

ISBN: (纸本)9781665441759

Electroencephalogram (EEG) signal provides information about the emotion state of an individual and music can be used as a stimulus to evoke specific kinds of emotions in the human brain. The proposed work focuses on discrete emotion recognition to classify the EEG signals into nine different emotion states. A novel hybrid feature extraction model based on time, frequency, and time-frequency domain is proposed to extract important features from EEG signal. Various machine learning models (k-nearest neighbor (k-NN), Random Forest, and XGBoost) and a deep learning algorithm (Convolution Neural Network (CNN)) are used to classify the emotions with promising results on DREAMER dataset. It is found that kNN, random forest, XGBoost, and CNN provide classification accuracy of 94.49%, 99.94%, 99.39%, and 99.90% respectively. The proposed work is compared with state-of-the-art techniques and the efficiency of the hybrid feature extraction model improves classification accuracy. © 2021 IEEE.

关键词： Emotion Recognition

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vision Transformer Based Automated Model for Enhancing Lung Cancer Classification

Vision Transformer Based Automated Model for Enhancing Lung ...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Akbar Sheikh Akbari Arvind Kumar B Ramachandra Reddy Koushlendra Kumar Singh Masahiro Takei Leeds Beckett University UK Machine Vision and Intelligence Lab National Institute of Technology Jamshedpur Jharkhand India Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Department of Mechanical Engineering Chiba University Chiba Japan

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

Lung cancer is one of the leading causes of cancer related mortality. The early detection and classification of the cancers tissues will reduce the mortalities rate. The present research focus on the development of automated classification model for lung and colon cancers tissues based on the histopathology images. The present work encompasses a vision transformer (ViT) based model to enhance diagnostic accuracy of lung cancers tissues. The proposed model utilizes the self-attention mechanism of ViT to focus on essential features present in histopathologicals images. The proposed model has been validated using two different dataset namely LC25000 & IQ-OTH/NCCD with 25000 & 1096 images respectively. The performance of proposed model is compared with traditional convolutional neural network (CNN) model and it has been observed the based model outforms better in terms of accuracy which - 98.80% & 99.09% respectively for datasets.

关键词： computer vision Analytical models Accuracy Lungs Lung cancer Imaging Transformers Convolutional neural networks Reliability Testing

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Recent Advances in IoT Based Smart Object Detection and Its Authentication by BlockChain Approaches 3rd

Recent Advances in IoT Based Smart Object Detection and Its ...

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3rd International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2020

作者： Shareef, Ahmed A. A. Yannawar, Pravin L. Vision and Intelligence System Lab Department of Computer Science and IT Dr. Babasaheb Ambedkar Marathwada University Aurangabad MH India

ISBN: (纸本)9789811605062

With the rapid improvement of the modern digital world and IoT-based systems, the accurate detection of objects moving in a dynamic scene has become an important and critical task. The object presented in videos and images is detected and recognized using various techniques, its information is extracted, transferred through the internet and displayed on the main monitoring system. The recognized object can be anything we repeatedly observe in our daily life such as human, animal, car, bicycle, etc. To detect such objects in a video or an image, the system needs to do a complex task using hardware and software components in order to complete the function as expected. This paper offers a review of the different techniques that have been used to detect an object, categorize it, localize it, extract its features, and many more, in images and videos. The comments are drawn from the studied literature that is related to IoT based object detection. Some perspectives solutions for object reorganization and tracking are also presented. This paper may significantly help researchers in this domain to start with essential knowledge for developing their own efficient schemes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

arXiv

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arXiv 2023年

作者： Sun, Shoukun Xian, Min Xu, Fei Capriotti, Luca Yao, Tiankai Machine Intelligence and Data Analytics Lab Department of Computer Science University of Idaho United States Idaho National Laboratory United States

The click-based interactive segmentation aims to extract the object of interest from an image with the guidance of user clicks. Recent work has achieved great overall performance by employing feedback from the output. However, in most state-of-theart approaches, 1) the inference stage involves inflexible heuristic rules and requires a separate refinement model, and 2) the number of user clicks and model performance cannot be balanced. To address the challenges, we propose a click-based and mask-guided interactive image segmentation framework containing three novel components: Cascade-Forward Refinement (CFR), Iterative Click Loss (ICL), and SUEM image augmentation. The CFR offers a unified inference framework to generate segmentation results in a coarse-to-fine manner. The proposed ICL allows model training to improve segmentation and reduce user interactions simultaneously. The proposed SUEM augmentation is a comprehensive way to create large and diverse training sets for interactive image segmentation. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach on five public datasets. Remarkably, our model reduces by 33.2%, and 15.5% the number of clicks required to surpass an IoU of 0.95 in the previous state-of-the-art approach on the Berkeley and DAVIS sets, respectively. © 2023, CC BY.

关键词： Image segmentation

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Your Transformer May Not be as Powerful as You Expect 36

Your Transformer May Not be as Powerful as You Expect

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36th Conference on Neural Information Processing Systems, NeurIPS 2022

作者： Luo, Shengjie Li, Shanda Zheng, Shuxin Liu, Tie-Yan Wang, Liwei He, Di National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Machine Learning Department School of Computer Science Carnegie Mellon University United States Microsoft Research United States Center for Data Science Peking University China Zhejiang Lab China

ISBN: (纸本)9781713871088

Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far as we know, theoretical understanding of the RPE-based Transformers is largely unexplored. In this work, we mathematically analyze the power of RPE-based Transformers regarding whether the model is capable of approximating any continuous sequence-to-sequence functions. One may naturally assume the answer is in the affirmative-RPE-based Transformers are universal function approximators. However, we present a negative result by showing there exist continuous sequence-to-sequence functions that RPE-based Transformers cannot approximate no matter how deep and wide the neural network is. One key reason lies in that most RPEs are placed in the softmax attention that always generates a right stochastic matrix. This restricts the network from capturing positional information in the RPEs and limits its capacity. To overcome the problem and make the model more powerful, we first present sufficient conditions for RPE-based Transformers to achieve universal function approximation. With the theoretical guidance, we develop a novel attention module, called Universal RPE-based (URPE) Attention, which satisfies the conditions. Therefore, the corresponding URPE-based Transformers become universal function approximators. Extensive experiments covering typical architectures and tasks demonstrate that our model is parameter-efficient and can achieve superior performance to strong baselines in a wide range of applications. The code will be made publicly available at https://***/lsj2408/URPE. © 2022 Neural information processing systems foundation. All rights reserved.

关键词： Encoding (symbols)

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.

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From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities

arXiv

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arXiv 2023年

作者： Ishmam, Md Farhan Shovon, Md Sakib Hossain Mridha, M.F. Dey, Nilanjan Department of Computer Science and Engineering Islamic University of Technology Dhaka Bangladesh Advanced Machine Intelligence Research Lab Dhaka Bangladesh Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Computer Science and Engineering Techno International New Town Kolkata India

The multimodal task of Visual Question Answering (VQA) encompassing elements of computer vision (CV) and Natural Language Processing (NLP), aims to generate answers to questions on any visual input. Over time, the scope of VQA has expanded from datasets focusing on an extensive collection of natural images to datasets featuring synthetic images, video, 3D environments, and various other visual inputs. The emergence of large pre-trained networks has shifted the early VQA approaches relying on feature extraction and fusion schemes to vision language pre-training (VLP) techniques. However, there is a lack of comprehensive surveys that encompass both traditional VQA architectures and contemporary VLP-based methods. Furthermore, the VLP challenges in the lens of VQA haven’t been thoroughly explored, leaving room for potential open problems to emerge. Our work presents a survey in the domain of VQA that delves into the intricacies of VQA datasets and methods over the field’s history, introduces a detailed taxonomy to categorize the facets of VQA, and highlights the recent trends, challenges, and scopes for improvement. We further generalize VQA to multimodal question answering, explore tasks related to VQA, and present a set of open problems for future investigation. The work aims to navigate both beginners and experts by shedding light on the potential avenues of research and expanding the boundaries of the field. © 2023, CC BY.

关键词： Question answering

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Efficient Multi-Query Oriented Continuous Subgraph Matching 40

Efficient Multi-Query Oriented Continuous Subgraph Matching

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Ma, Ziyi Yang, Jianye Zhou, Xu Xiao, Guoqing Wang, Jianhua Yang, Liang Li, Kenli Lin, Xuemin School of Artificial Intelligence Hebei University of Technology China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control China Cyberspace Institute of Advanced Technology Guangzhou University China PengCheng Laboratory Department of New Networks China College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China Antai College of Economics and Management Shanghai Jiao Tong University China

ISBN: (纸本)9798350317152

Continuous subgraph matching (CSM) is a critical task for analyzing dynamic graphs and has a wide range of applications, such as merchant fraud detection, cyber-attack hunting, and rumor detection. Although many efficient CSM algorithms have been recently proposed, they are mainly designed to process a single query. However, in some application scenarios, multi-query oriented continuous subgraph matching (MQCSM) may be of more interest. To our knowledge, the two existing solutions to MQCSM are outdated due to unsatisfactory performance. In this paper, we propose MQ-Match, an efficient approach to MQCSM. First, we design a compact yet effective index structure CCG, which maintains the local matching result of vertices in the data graph using a directed graph. The directed edges in CCG can be utilized as an effective pruning rule for the subsequent incremental matching algorithm when expanding a partial match. Then, we develop a computation sharing incremental matching algorithm. In specific, a set of matching trees is constructed based on the depth-first search trees of the query graphs. By utilizing CCG, we conduct subgraph matching for the matching tree to collect the incremental matches for the query graphs, where the common structures of query graphs are matched only once. Extensive experiments show that MQ-Match can achieve 3.1x-7071.4x speedup over the competitors, and consumes much less memory under the majority of the experiment settings. © 2024 IEEE.

关键词： Directed graphs

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Unsupervised Pre-training with Language-vision Prompts for Low-Data Instance Segmentation

arXiv

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arXiv 2024年

作者： Zhang, Dingwen Li, Hao He, Diqi Liu, Nian Cheng, Lechao Wang, Jingdong Han, Junwei The Brain and Artificial Intelligence Lab. Northwestern Polytechnical University Shaanxi Xi’an710000 China The Inception Institute of Artificial Intelligence United Kingdom The School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Department of Computer Vision Baidu Inc. China

In recent times, following the paradigm of DETR (DEtection TRansformer), query-based end-to-end instance segmentation (QEIS) methods have exhibited superior performance compared to CNN-based models, particularly when trained on large-scale datasets. Nevertheless, the effectiveness of these QEIS methods diminishes significantly when confronted with limited training data. This limitation arises from their reliance on substantial data volumes to effectively train the pivotal queries/kernels that are essential for acquiring localization and shape priors. To address this problem, we propose a novel method for unsupervised pre-training in low-data regimes. Inspired by the recently successful prompting technique, we introduce a new method, Unsupervised Pre-training with Language-vision Prompts (UPLVP), which improves QEIS models’ instance segmentation by bringing language-vision prompts to queries/kernels. Our method consists of three parts: (1) Masks Proposal: Utilizes language-vision models to generate pseudo masks based on unlabeled images. (2) Prompt-Kernel Matching: Converts pseudo masks into prompts and injects the best-matched localization and shape features to their corresponding kernels. (3) Kernel Supervision: Formulates supervision for pre-training at the kernel level to ensure robust learning. With the help of our pre-training method, QEIS models can converge faster and perform better than CNN-based models in low-data regimes. Experimental evaluations conducted on MS COCO, Cityscapes, and CTW1500 datasets indicate that the QEIS models’ performance can be significantly improved when pre-trained with our method. Code will be available at: https://***/lifuguan/UPLVP. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Dynamic Decision Frequency with Continuous Options

Dynamic Decision Frequency with Continuous Options

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Amirmohammad Karimi Jun Jin Jun Luo A. Rupam Mahmood Martin Jagersand Samuele Tosatto Department of Computer Science University of Alberta Edmonton Canada Noah's Ark Lab Huawei Technologies Canada Edmonton Canada Alberta Machine Intelligence Institute (Amii) Department of Computer Science Digital Science Center University of Innsbruck Innsbruck Austria

In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter, as setting it too short may increase the problem's difficulty by requiring the agent to make numerous decisions to achieve its goal while setting it too long can result in the agent losing control over the system. However, physical systems do not necessarily require a constant control frequency, and for learning agents, it is often preferable to operate with a low frequency when possible and a high frequency when necessary. We propose a framework called Continuous-Time Continuous-Options (CTCO), where the agent chooses options as sub-policies of variable durations. These options are time-continuous and can interact with the system at any desired frequency providing a smooth change of actions. We demonstrate the effectiveness of CTCO by comparing its performance to classical RL and temporal-abstraction RL methods on simulated continuous control tasks with various action-cycle times. We show that our algorithm's performance is not affected by the choice of environment interaction frequency. Furthermore, we demonstrate the efficacy of CTCO in facilitating exploration in a real-world visual reaching task for a 7 DOF robotic arm with sparse rewards.

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