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Unpaired Shadow Removal: Enhancing Attention to Shadow Areas via Dropkey

Unpaired Shadow Removal: Enhancing Attention to Shadow Areas...

引用

2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Yang, Jaewon Shin, Ukcheol Cho, Donghyeon Chungnam National University Dept. of Electronics Engineering Daejeon Korea Republic of Robotics Institute Carnegie Mellon University Pittsburgh United States Hanyang University Dept. of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798331510756

Shadows appear in image areas where an object obstructs the light path. These areas, having lower values than non-shadow areas, degrade image quality and lead to issues in object recognition and segmentation. Supervised shadow removal depends on datasets with shadow and shadow-free image pairs, sparking increased interest in unpaired techniques. However, unpaired shadow removal faces challenges in accurately focusing on shadow areas without direct supervisory signals from ground truth. Consequently, shadows are not effectively removed, and non-shadow areas may suffer from unintended distortions. In this paper, we introduce a transformer-based network designed to identify shadow areas accurately, leveraging global context through spatial and channel attention mechanisms. Additionally, in the training phase, the transformer network is trained to precisely concentrate on shadow areas using a domain classifier and a dropkey mechanism, which randomly drops features of the keys to enhance focus. Our method is tested across several benchmark datasets for shadow removal, such as ISTD, ISTD+, SRD and WSRD, demonstrating better performance compared to current unpaired approaches. © 2025 IEEE.

关键词： Image segmentation

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A Transformer Based Model for Bangla Fake News Detection Using DistilBERT

A Transformer Based Model for Bangla Fake News Detection Usi...

引用

Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Nazmul Abdal Most. Humayera Kabir Oshie Md. Azizul Haque Salman Rahman Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Dept of Computer Science and Engineering Jahangirnagar University Savar Bangladesh Dept of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

Fake news is widely disseminated and constitutes an imminent danger to society since it may shift public opinion, affect elections, and stir up social unrest. For an informed and reliable information ecosystem to continue to exist, the ability to identify fake news in various languages is essential. In this paper, we propose a transformer-based model for Bangla fake news detection using DistilBERT, a distilled variant of the BERT model. We begin by curating a large-scale dataset of Bangla news articles, comprising both authentic and fake news samples. To capture the contextual information and semantic relationships within the Bangla language, we fine-tune the DistilBERT model on this dataset. The pre-training process entails putting the model through its paces on a sizable corpus of Bangla text data. We compare its performance with that of other standard machine learning and deep learning models, such as Support Vector Machine (SVM), Random Forest (RF), and Long Short Term Memory (LSTM) in order to show that our model is superior at correctly identifying fake news in Bangla. The experimental findings show that the suggested model performs competitively in detecting Bangla fake news, surpassing other approaches with 97.85% accuracy.

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Deep Learning-Based Stock Price Prediction: A Comprehensive Approach Using Bi-LSTM 4

Deep Learning-Based Stock Price Prediction: A Comprehensive ...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Chy, Md. Kalim Amzad Masum, Abdul Kadar Muhammad Alam, Mohammed Shamsul Azad, Md. Abul Kalam Hassan, Md. Maruf Bhuiyan, Touhid International Islamic University Dept. of Computer Science and Engineering Chittagong Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Islamic University of Technology Dept. of Business and Technology Management Gazipur Bangladesh School of It Washington University of Science and Technology VA United States

ISBN: (纸本)9798331529635

This study delves into the prediction of stock prices in the Bangladesh stock market using advanced deep learning frameworks, with a particular focus on the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Traditional statistical models often struggle to effectively capture the complex, non-linear characteristics of financial data, leading to inconsistent results. The Bi-LSTM model, however, addresses this challenge by processing data in both forward and backward sequences, making it particularly suitable for analyzing the intricate dynamics of the Bangladesh stock market. A rigorous feature engineering process is undertaken, incorporating 64 distinct features that include market trends, stock-specific indicators, technical and fundamental variables, as well as crucial macroeconomic factors like gold prices and foreign exchange reserves. The dataset spans historical stock market data from 2012 to 2022, with comprehensive preprocessing steps taken to ensure data quality and consistency. Hyperparameter tuning is executed through a grid search to optimize model performance. The evaluation of the Bi-LSTM model is carried out using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and the R2 score, which demonstrate its enhanced predictive power compared to traditional models. This research contributes to the growing field of deep learning in financial forecasting, offering new perspectives for stock price prediction in emerging markets, while also addressing ethical issues to minimize risks like market manipulation. © 2024 IEEE.

关键词： Mean square error

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AI Academic Research Aggregator

AI Academic Research Aggregator

引用

Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Anupama T. A Chandragupta Kumar Ashay Amal Aman Kumar Dept. of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

In responding to the challenges of making the discovery and analysis of academic research papers efficient, APARA is an AI-based academic research aggregator. Classical search engines leave the researchers inundated with gigantic waves of unstructured information. Usually, they lack per-sonalized recommendations and less cumbersome summaries. By means of the arXiv API and natural language processing (NLP), APARA is formulated to automatically extract metadata, summarize research contents, and improve search efficiency. The platform is built on the Django framework which offers valuable user interface capabilities and gives space for content-based research paper recommendations. Because APARA streamlines the research discovery process, relevant studies can be found faster, and academic exploration becomes much more efficient. The work presents the core methodology, system architecture, and evaluation metrics of APARA in parenthesis with the prospect of generating idle works of academic research.

关键词： Soft sensors Systems architecture Metadata Data retrieval Data aggregation Search engines Smart systems Natural language processing User experience Data mining

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Utilization of Decentralized Finance (DeFi) and Distributed Ledger Technology (DLT) in Banking operations

Utilization of Decentralized Finance (DeFi) and Distributed ...

引用

Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Ch Sree Kumar Akhilendra Pratap Singh K Hemant K Reddy Dept. of Computer Science National Institute of Technology Meghalaya Meghalaya India Dept. of Computer Science and Engineering VIT-AP Vijayawada India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Distributed Ledger Technology (DLT) is a decentralized database system where transactions are recorded and verified across multiple nodes. Its key features include immutability, time-stamping, and consensus-based validation. Numerous DLT applications are in supply chain management, intellectual property, cross border payments, energy trading, real estate, and online donations. DeFi, a combination of cryptocurrency and blockchain technology, offers financial services without intermediaries. Hence transactions with various digital assets based on cryptocurrency price feeds needs an automated framework. Smart contracts, self-executing contracts with terms directly written into code, automate processes and reduce manual intervention. This paper proposes a decentralized financial trading model using the AAVE protocol. AAVE is a decentralized platform that allows for transactions with various digital assets based on cryptocurrency price feeds. Proposed model uses Chainlink, a decentralized oracle network, to provide accurate and reliable price feeds. IPFS is used for data storage, while Graph is employed for indexing and querying blockchain data. The paper presents an example of a DeFi protocol to simulate banking operations, showcasing the potential of DeFi and DLT in revolutionizing traditional banking processes.

关键词： Accuracy Distributed ledger Finance Banking Decentralized applications Blockchains Cryptocurrency Feeds Reliability Indexing

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Balancing Safety and Security in Autonomous Driving Systems: A Machine Learning Approach with Safety-First Prioritization

Balancing Safety and Security in Autonomous Driving Systems:...

引用

computer Security Applications Conference Workshops (ACSAC Workshops), Annual

作者： Afshin Hasani Mehran Alidoost Nia Reza Ebrahimi Atani Dept. of Computer Engineering University of Guilan Rasht Iran Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9798331532819

ISBN: (纸本)9798331532826

In the era of autonomous vehicles (AVs), ensuring the safety and security of the system is an ongoing challenge, particularly when faced with increasingly cyber-attacks such as GPS spoofing and man-in-the-middle. This paper presents a novel reinforcement learning (RL)-based framework that prioritizes safety over security concerns in AVs, ensuring that life-critical tasks such as collision avoidance and speed control are always maintained, even in the presence of security threats. Our system leverages the MAPE-K loop to dynamically adapt to changing conditions, using RL in the Analyzing phase to make real-time decisions that maximize cumulative safety rewards while addressing security risks that directly affect operational integrity. We demonstrate how our RL-based self-adaptive system mitigates security threats while maintaining safe driving behaviors through various simulations and experiments. The proposed system shows significant improvements in collision avoidance and threat detection, ultimately ensuring that the AV’s safety is preserved in even the most challenging scenarios.

关键词： Conferences Velocity control Reinforcement learning Real-time systems Threat assessment Safety Security Collision avoidance Autonomous vehicles Global Positioning System

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Solving Task-Select Problems Using a Quantum Annealer

Solving Task-Select Problems Using a Quantum Annealer

引用

2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Mita, Koki Yachi, Yuta Fukada, Keisuke Togawa, Nozomu Dept. of Computer Science and Communications Engineering Waseda University Tokyo Japan

ISBN: (纸本)9798331521165

To tackle environmental and social challenges, we face numerous tasks that need to be solved. However, due to limited time and resources, it is unrealistic to solve all tasks. Therefore, prioritizing tasks to ensure efficient handling and meeting criteria is essential. When the number of tasks is large, prioritizing the optimal combination of tasks on classical computers becomes highly time-consuming. We define this prioritization as a combinatorial optimization problem, termed the task-select problem, and address it using a quantum annealer. A quantum annealer is specialized hardware designed to efficiently solve combinatorial optimization problems by transforming them into Quadratic Unconstrained Binary Optimization (QUBO) problems. In this paper, we propose a QUBO formulation for the task-select problem. Experimental evaluations demonstrate that our approach enables solving the task-select problem with up to 100 items using a real quantum annealer. © 2025 IEEE.

关键词： Ising model

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Steganographic Encryption and Decryption Using Duality Preserving GANs 22

Steganographic Encryption and Decryption Using Duality Prese...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Shah, Aaryak Kamal, Alok Kumar Neeraj Dept. of Information Technology ABV-IIITM Gwalior Gwalior India Dept. of Electrical/Electronics ABV-IIITM Gwalior Gwalior India Dept. of Computer Science and Engineering Mahindra University Hyderabad India

ISBN: (纸本)9798331527471

Image steganography has been a significant research area in the security sphere. The core idea revolves around embedding secret messages in existing images (covers) such that the resultant image displays no noticeable detectable artifacts of the secret while also being statistically similar to the original cover. Various steganographic techniques have been developed and tested. However, these traditional techniques often exploit some specific property of the image and are often easy to detect with the right tools. In recent times, generative adversarial networks, or GANs for short, have shown massive promise in their capability to modify and synthesize data. They are now ubiquitous in image synthesis and image translation tasks. With various recent advancements in this technology allowing these models to generate, in many cases, human-imperceptible data, GAN-based steganographic systems have become a significant focus of security research. This report analyzes and compares some existing models and attempts to provide a new solution that performs better on multiple well-defined metrics. A DualGAN-based architecture is proposed that trains both the encoder and decoder processes simultaneously while maintaining the underlying dual relationship of these tasks. The proposed scheme reports better results on the selected metrics (SBR, DER, PSNR) at three values of embedding capacity set at 0.3, 0.6, and 0.9. © 2024 IEEE.

关键词： Generative adversarial networks

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The Use of Digital Twins in Inverter-based DERs to Improve Nanogrid Fault Recovery

The Use of Digital Twins in Inverter-based DERs to Improve N...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Eggebeen, Andrew Vygoder, Mark Oriti, Giovanna Gudex, Jacob Julian, Alexander L. Cuzner, Robert M. University of Wisconsin - Milwaukee Dept. of Computer Science MilwaukeeWI United States University of Wisconsin - Milwaukee Dept. of Electrical Engineering MilwaukeeWI United States Naval Postgraduate School Dept. of Electrical and Computer Engineering MontereyCA United States Seaside CA United States

ISBN: (纸本)9798350316445

Nanogrids provide a redundant mode of operation when the grid power fails. Nanogrids use data communication networks to coordinate power and energy between distributed energy resources (DERs). Under the corner-case of simultaneous communication failure and electrical faults, it can be difficult for the energy storage system with grid-forming/grid-following capability to select the correct operational mode. This paper proposes the use of a digital twin to assist in minimizing the degradation on the nanogrid caused by loss of communication during islanded operation under electrical fault conditions. The structure of the digital twin is proposed, and the construction of a virtual twin is presented. The error vector produced by the difference between the physical twin and virtual twin is demonstrated in various fault conditions. Lastly, the paper presents a discussion of how the error vector could be used to make inferences about the nanogrid state and to choose actions during off-nominal communication. © 2023 IEEE.

关键词： Digital Twin Distributed Energy Resources Machine Learning Microgrid Nanogrid Resilience

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A Comprehensive Analysis of Harmonizing Classical Approaches with the Cutting-Edge Techniques for Specular Reflection Detection in Medical Imaging

A Comprehensive Analysis of Harmonizing Classical Approaches...

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Data science and Business Systems (ICDSBS), International Conference on

作者： Anu P M Lakshmi C Dept. of Computer Science Engineering SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

The detection and removal of Specular Reflections (SR) is spotted as a significant challenge in the field of Medical Image Analysis, especially in Medical visual diagnosis for ensuring high quality images in accurate diagnosis. These reflections appear as bright spots in images, obscuring the critical tissue details and leads to mis-diagnosis. SR detection has attracted huge focus in recent years, as researchers aim to build impressive algorithms with improved performance and reduced complexity. By inculcating appropriate techniques, their impact can be minimized and consequently lead to better visualization of anatomical and pathological details, which are the prerequisite for medical diagnosis. The aim of this article is to project the various approaches adopted and the challenges in classical image processing approaches and deep learning-based techniques in SR detection and highlighting the importance of hybrid methods with their underlying principles, by comprehending the articles from various scholarly publishing platforms.

关键词： Deep learning Surveys Representation learning Visualization Image analysis Accuracy Self-supervised learning Transformers Reflection Medical diagnostic imaging

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