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DNACoder: a CNN-LSTM attention-based network for genomic sequence data compression

Neural Computing and Applications 2024年第29期36卷 18363-18376页

作者： Sheena, K.S. Nair, Madhu S. Artificial Intelligence & amp Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India

Genomic sequencing has become increasingly prevalent, generating massive amounts of data and facing a significant challenge in long-term storage and transmission. A solution that reduces the storage and transfer requirements without compromising data integrity is needed. The effectiveness of neural networks has already been endorsed in tasks like image and speech compression. Adapting them to recognize the intricate patterns in genomic sequences could help to find more redundancies and reduce storage requirements. The proposed method, called DNACoder, leverages deep learning techniques to achieve significant compression ratios while preserving the essential information in genomic data and offers a high-performance compression for genomic sequences in any data format. The results of the experiments clearly demonstrate the effectiveness of the method and its potential applications in genomic data storage. Our proposed method improves compression by 21.1% on bits per base compared to existing compressors on the benchmarked dataset. By using a deep learning prediction model that is structured as a convolutional layer followed by an attention-based long short-term memory network, we propose a novel lossless and reference-free compression approach (DNACoder), which can also be utilized as a reference-based compressor. The experimental outcome on the tested data illustrates that the advocated compression algorithm’s CNN-LSTM model makes generalizations effectively for genomic sequence data and outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Long short-term memory

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VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM 1

VISTA: Visual Integrated System for Tailored Automation in M...

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1st Workshop on Large Foundation Models for Educational Assessment

作者： Lee, Jeongwoo Park, Kwangsuk Park, Jihyeon Department of Applied Artificial Intelligence Sungkyunkwan University Deep Computer Vision LAB MODULABS Korea Republic of Deep Computer Vision LAB MODULABS Korea Republic of

Generating accurate and consistent visual aids is a critical challenge in mathematics education, where visual representations like geometric shapes and functions play a pivotal role in enhancing student comprehension. This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to automate the creation of complex mathematical visualizations alongside coherent problem text. Our approach not only simplifies the generation of precise visual aids but also aligns these aids with the problem’s core mathematical concepts, improving both problem creation and assessment. By integrating multiple agents, each responsible for distinct tasks—such as numeric calculation, geometry validation, and visualization—our system delivers mathematically accurate and contextually relevant problems with visual aids. Evaluation across Geometry and Function problem types shows that our method significantly outperforms basic LLMs in terms of text coherence, consistency, relevance and similarity, while maintaining the essential geometrical and functional integrity of the original problems. Although some challenges remain in ensuring consistent visual outputs, our framework demonstrates the immense potential of LLMs in transforming the way educators generate and utilize visual aids in math education. © 2024 J. Lee, K. Park & J. Park.

关键词： Multi agent systems

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Oh-FedRec: One-Shot and Heterogeneous Vertical Federated Recommendation System

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 849-861页

作者： Kong, Xiangjie He, Xiaohua Ma, Xulin Yan, Xiaoran Wang, Lingyun Shen, Guojiang Liu, Zhi Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Lab Research Institute of Artificial Intelligence Hangzhou310023 China

Federated learning has a wide range of applications in recommendation systems, but most federated recommendation systems can only achieve federated communication between users and servers. Only a few are vertical federated recommendation systems, achieving federated server communication. In addition, the current federated recommendation frameworks require that each participant have the same model. This condition is very harsh for all participants involved. What is more, the current federated recommendation frameworks require multiple rounds of communication, which consumes many communication resources and much time. To solve these problems, we propose a one-shot and heterogeneous vertical federated recommendation framework called Oh-FedRec. This framework needs only one round of communication, dramatically reducing the consumption of communication resources. Additionally, it no longer requires participants to have consistent models, significantly reducing constraints on the various participants. We tested our framework on Tmall and Jingdong datasets, and the test results proved the superiority of Oh-FedRec. © 1975-2011 IEEE.

关键词： Federated learning

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Prostate Segmentation in Magnetic Resonance Images Using artificial Neural Networks: A Systematic Literature Review

Prostate Segmentation in Magnetic Resonance Images Using Ar...

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International Conference on computer Science, Electronics and Industrial Engineering, CSEI 2023

作者： Vacacela, Silvia-P. Benalcázar, Marco-E. Artificial Intelligence and Computer Vision Research Lab Departamento de Informática y Ciencias de la Computación Escuela Politécnica Nacional Quito Ecuador

ISBN: (纸本)9783031692277

Currently, artificial Neural Networks have become a very popular machine learning approach for the automatic segmentation of medical images. To understand how the field of automatic prostate segmentation using artificial neural networks over MRIs has evolved lately, we present a Systematic Literature Review (SLR) on this topic. This SLR is based on the Kitchenham methodology applied to 372 primary studies published from 2015 to 2020. Inclusion and exclusion criteria were applied to reduce the number of papers to 65. The contributions of this SLR consist of determining the segmentation structure, type of segmentation, standard architectures, and the metrics used. We also identified findings that could open new directions of work for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Urology

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Exploiting autoencoder’s weakness to generate pseudo anomalies

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Neural Computing and Applications 2024年第23期36卷 14075-14091页

作者： Astrid, Marcella Zaheer, Muhammad Zaigham Aouada, Djamila Lee, Seung-Ik Department of Artificial Intelligence University of Science and Technology Daejeon34113 Korea Republic of Field Robotics Research Section Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg1855 Luxembourg Department of Computer Vision Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE is expected to well reconstruct normal but to poorly reconstruct anomalous data. However, contrary to the expectation, anomalous data are often well reconstructed as well. In order to further separate the reconstruction quality between normal and anomalous data, we propose creating pseudo anomalies from learned adaptive noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. The generated noise is added to the normal data to create pseudo anomalies. Extensive experiments on Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the effectiveness and generic applicability of our approach in improving the discriminative capability of AEs for anomaly detection. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance

Cross-View Meets Diffusion: Aerial Image Synthesis with Geom...

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2025 IEEE/CVF Winter Conference on Applications of computer vision, WACV 2025

作者： Arrabi, Ahmad Zhang, Xiaohan Sultani, Waqas Chen, Chen Wshah, Safwan University of Vermont Vermont Artificial Intelligence Lab Department of Computer Science United States Information Technology University Intelligent Machines Lab Pakistan University of Central Florida Center for Research in Computer Vision United States

ISBN: (纸本)9798331510831

Aerial imagery analysis is critical for many research fields. However, obtaining frequent high-quality aerial images is not always accessible due to its high effort and cost requirements. One solution is to use the Ground-to-Aerial (G2A) technique to synthesize aerial images from easily collectible ground images. However, G2A is rarely studied, because of its challenges, including but not limited to, the drastic view changes, occlusion, and range of visibil-ity. In this paper, we present a novel Geometric Preserving Ground-to-Aerial (G2A) image synthesis (GPG2A) model that can generate realistic aerial images from ground images. GPG2A consists of two stages. The first stage predicts the Bird's Eye View (BEV) segmentation (referred to as the BEV layout map) from the ground image. The second stage synthesizes the aerial image from the predicted BEV layout map and text descriptions of the ground image. To train our model, we present a new multimodal cross-view dataset, namely VIGORv2, built upon VIGOR [64] with newly collected aerial images, maps, and text descriptions. Our extensive experiments illustrate that GPG2A synthesizes better geometry-preserved aerial images than existing models. We also present two applications, data augmentation for cross-view geo-localization and sketch-based region search, to further verify the effectiveness of our GPG2A. The code and dataset are available at https://***/AhmadArrabi/GPG2A. © 2025 IEEE.

关键词： Photointerpretation

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VSMCNN-dynamic summarization of videos using salient features from multi-CNN model

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Journal of Ambient intelligence and Humanized Computing 2023年第10期14卷 14071-14080页

作者： Nair, Madhu S. Mohan, Jesna Artificial Intelligence & Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Nalanchira Kerala Thiruvananthapuram695015 India

A dynamic video summarization system detects key parts of the input video to generate its compact representation. The summaries can be used for efficient management of video data. This paper proposes an approach, Video summarization based on multi-CNN model (VSMCNN), that exploits major aspects of human cognition to generate meaningful summaries from videos. As the method focuses on dynamic summarization, the input video is divided into a set of shots. A multi-CNN model, which is a combination of different pre-trained models of CNN, is used for feature extraction from shots. The salient features are extracted from high dimensional feature vector using an unsupervised feature reduction technique applied in multiple subspaces to rank features in the vector. The distance measure between feature vectors is then thresholded to detect prime parts of the tested video. Experiments are performed on SumMe dataset and the results prove that our approach is successful in detecting portions of the tested video that has an essential message. The analysis shows that the method outperforms the state-of-the-art methods in the literature. Further evaluation on comparison with human-generated summaries in the ground truth proves the effectiveness of the proposed method. The paper also presents a detailed analysis to show which combination of pre-trained models of CNN is best suitable for generating dynamic summaries. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Video recording

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Effects on Hand Gesture Recognition Accuracy after Severe Cranial Trauma 7

Effects on Hand Gesture Recognition Accuracy after Severe Cr...

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7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023

作者： Zea, Jonathan A. Santillán, Luis Gabriel Macias López, Lorena Isabel Barona Caraguay, Ángel Leonardo Valdivieso Benalcázar, Marco E. Artificial Intelligence and Computer Vision Research Lab Departamento de Informática y Ciencias Computación Escuela Politécnica Nacional Quito Ecuador

ISBN: (纸本)9798350338232

This study examines the impact of severe cranial trauma on the accuracy of Hand Gesture Recognition (HGR) models based on electromyography (EMG) signals. The study focuses on a single subject, who suffered left-sided hemiplegia, and exhibits limited mobility and spasticity in the left hand. EMG samples corresponding to six hand gestures from his both forearms were collected using the Myo Armband. The 2023HGR5-CNN-LSTM HGR model was used to evaluate the recognition accuracy of these samples. Results showed significantly lower HGR performance in the left side (10.67% recognition accuracy) compared to the right side (56% recognition accuracy). As the selected model was trained with EMG data only from right arms, a channel mapping to adapt the EMG signals from the left side was also developed. Unfortunately, the channel mapping implemented did not improve the recognition accuracy (9.33%). These findings highlight the challenges in achieving high performance HGR for individuals with cranial injuries. It might also point out that developing myoelectric prostheses using datasets from unaffected individuals may be inadequate for training robust models. © 2023 IEEE.

关键词： Mapping

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How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges

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Machine intelligence research 2023年第5期20卷 605-613页

作者： Haotong Qin Ge-Peng Ji Salman Khan Deng-Ping Fan Fahad Shahbaz Khan Luc Van Gool Computer Vision Lab(CVL) ETH ZurichZurich 8001Switzerland College of Engineering Computing&CyberneticsAustralian National UniversityCanberra 8105Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi 999041UAE

Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational ***,Bard has recently been updated to handle visual inputs alongside text prompts during *** Bard's impressive track record in handling textual inputs,we explore its capabilities in understanding and interpreting visual data(images)conditioned by text *** exploration holds the potential to unveil new insights and challenges for Bard and other forthcoming multi-modal Generative models,especially in addressing complex computer vision problems that demand accurate visual and language ***,in this study,we focus on 15 diverse task scenarios encompassing regular,camouflaged,medical,under-water and remote sensing data to comprehensively evaluate Bard's *** primary finding indicates that Bard still struggles in these vision scenarios,highlighting the significant gap in vision-based understanding that needs to be bridged in future *** expect that this empirical study will prove valuable in advancing future models,leading to enhanced capabilities in comprehending and interpreting finegrained visual *** project is released on https://***/htqin/GoogleBard-VisUnderstand.

关键词： Google Bard multi-modal understanding visual comprehension large language models conversational AI chatbot.

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Development of a Hand Gesture Recognition Model Capable of Online Readjustment Using EMGs and Double Deep-Q Networks 1

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International Conference on Information Technology and Systems, ICITS 2023

作者： Díaz, Danny Benalcázar, Marco E. Barona, Lorena Valdivieso, Ángel Leonardo Artificial Intelligence and Computer Vision Research Lab Departamento de Informática y Ciencias de la Computación Escuela Politécnica Nacional Quito Ecuador

ISBN: (数字)9783031332586

ISBN: (纸本)9783031332579

Hand Gesture Recognition (HGR) has enabled the development of alternative forms of human-machine interaction in recent years. HGR models based on supervised learning have been developed with high accuracy. However, over time, the need to add new gestures appears. Consequently, it is necessary to establish a model to adapt to this need. Reinforcement learning permits developing agents capable of adapting their behavior to dynamic environments. In this work, we use Double Deep-Q Networks (DDQN), a reinforcement learning algorithm, to build an agent (an HGR model in this case), based on electromyography signals (EMGs), capable of recognizing new gestures over time. The proposed model is able to recognize 5 hand gestures, achieving an accuracy of 97.36% for classification and 94.83% for recognition. We achieved this result due to a modification in the experience replay, the post-processing of the algorithm, and its Markov Decision Process definition. This model can recalibrate its accuracy over time by adapting to a dynamic environment, while maintaining high performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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