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2nd IEEE International Conference on Artificial Intelligence in Everything, aiE 2022

作者： Vubangsi, Mercel Nforna, Edwin Akongnwi Kamsu, Brigitte Fotso Tchoffo, M. Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon Htttc Bambili University of Bamenda Materials Science Research Unit Department of Fundamental Sciences Cameroon Faculty of Science University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin Kyrenia10 Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we evaluate the effect of variation of mole fraction of Tin in doped titanium oxide on band gap. To achieve this, we resort to Matminer's "dielectric_constant"dataset and apply an algorithm to featurize the chemical formulae of the compounds in the dataset. Using k-fold cross-validation of random forest, we obtain a Machine learning model with R2 value of 0.81 MAE=0.45. Applying our model to predict the band gap of doped Titanium oxide over a range of 6 compositions, we find that the 50% doping level has a remarkably lower band gap than all other compositions which makes it a more suitable candidate for optical applications such as in solar cells. © 2022 IEEE.

关键词： Smart contract

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Segmentation of Retinal Blood Vessels in Fundus Images Using Fully Convolutional Networks

Segmentation of Retinal Blood Vessels in Fundus Images Using...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Vijayalakshmi G Mohammed Al-Farouni Sourav Ch Satya Sravani Rajesh Kumar Singh R. Saiharsha Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computer Science & Engineering Aditya Engineering College Surampalem India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of CSE-AI Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Segmentation is mandatory to analyze retinal blood vessel indication for diagnosis of various ophthalmic and systemic disorders. This paper takes a look at this problem, specifically, the application of Fully Convolutional Networks (FCNs) in this whole process, with a primary focus on the U-Net architecture. In this work, the authors conduct the experiments on three datasets, namely DRIVE, STARE and CHASE_DB1, and prepare the images for modeling through normalization, resizing, and data augmentation. Due to the binary structure of the task, the loss function of the proposed method is binary cross-entropy along with the Dice coefficient, and the model is trained with the Adam optimizer. The improvement scores that was obtained shows that the proposed model has a high accuracy, precision, recall and F1 score on all the datasets used and has proven to be better than other existing methods. The study establishes the usefulness of FCNs in segmenting the tree structure of the retinal blood vessel structures in uniterrupted manner, and presented promising improvement for early diagnosis and treatment in the clinical application. it emphasizes on the necessity of utilizing far improved deep learning architectures and more holistic training mechanisms in striving to create more accurate diagnosis support systems in medical image analysis.

关键词： Training Image segmentation Accuracy Image analysis Transfer learning Blood vessels Retina Data augmentation Convolutional neural networks Medical diagnostic imaging

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Maintaining Balance of Mobile Manipulators for Safe Pick-Up Tasks

Maintaining Balance of Mobile Manipulators for Safe Pick-Up ...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Francesco D'Orazio Tommaso Belvedere Spyridon G. Tarantos Giuseppe Oriolo Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy CNRS Université de Rennes Inria IRISA Rennes France Center of AI & Robotics (CAIR) and Engineering Division New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper presents a novel method to maintain the dynamic balance of a Mobile Manipulator (MM) during the pick-up of heavy objects. The approach entails the generation of a preliminary reach-to-grasp trajectory, which is subsequently refined by an Optimization-Based Controller (OBC) formulated as a Quadratic Program (QP). The trajectory is modified in a minimal fashion to ensure that the robot maintains balance during the reaching phase and remains balanced when the payload is grasped. This is accomplished by incorporating a balance constraint into the OBC that predicts the Zero Moment Point (ZMP) position of the robot at the beginning of the pick-up phase. This accounts for the gravitational and inertial effects that the object has on the robot. The method is validated through simulations conducted with the TIAGo robot in Gazebo. The results demonstrate that the proposed approach effectively prevents the robot from tipping over when the payload is considered.

关键词： Simulation Wheels Stability analysis Robustness Trajectory Safety Mobile robots Robots Manipulator dynamics Payloads

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Leveraging Service Supply Dynamics in Senselife: Building an Explainable Recommender System for Tailored Frailty Prevention 21

Leveraging Service Supply Dynamics in Senselife: Building an...

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2024 IEEE/ACS 21st International Conference on computer Systems and Applications, aiCCSA 2024

作者： Frikha, Ghassen Lorca, Xavier Pingaud, Herve Taweel, Adel Bortolaso, Christophe Borgiel, Katarzyna Lamine, Elyes IMT Mines Albi Industrial Engineering Center University of Toulouse Albi France Champollion National University Institute University of Toulouse CNRS-LGC Albi France Birzeit University Department of Computer Science Birzeit Palestine Research and Innovation Division Berger-Levrault Labège France ISIS Champollion National University Institute University of Toulouse Castres France

ISBN: (纸本)9798331518240

The growing elderly population in developed countries highlights the critical need for preventing frailty, which poses significant challenges to health systems due to increased risks of severe health issues. This paper introduces Senselife, a framework that provides explainable service recommendations specifically tailored for frailty prevention. It begins by outlining the medical context and challenges associated with aging, followed by an overview of existing recommender systems with similar objectives. We detail the integration of three key resources-ROR, RNA, and Data Laregion-within the Senselife framework to represent service supply. The paper explains how service supply is structured and the transformation of available data for use within our recommender engine. We introduce the concept of operational activities derived from the ROR and leverage the capabilities of LLMs to incorporate RNA data into Senselife. Additionally, we illustrate how these services are ultimately compiled into recommended service packages. Finally, the paper concludes by summarizing key findings and suggesting potential directions for future research. © 2024 IEEE.

关键词： Electronic health record

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Multifidelity Simulation-based Inference for Computationally Expensive Simulators

arXiv

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arXiv 2025年

作者： Krouglova, Anastasia N. Johnson, Hayden R. Confavreux, Basile Deistler, Michael Gonçalves, Pedro J. Departments of Computer Science and Electrical Engineering KU Leuven Belgium Belgium Gatsby Computational Neuroscience Unit UCL London United Kingdom University of Tübingen Germany Tübingen AI Center Tübingen Germany Leuven Belgium

Across many domains of science, stochastic models are an essential tool to understand the mechanisms underlying empirically observed data. Models can be of different levels of detail and accuracy, with models of high-fidelity (i.e., high accuracy) to the phenomena under study being often preferable. However, inferring parameters of high-fidelity models via simulation-based inference is challenging, especially when the simulator is computationally expensive. We introduce MF-NPE, a multifidelity approach to neural posterior estimation that leverages inexpensive low-fidelity simulations to infer parameters of high-fidelity simulators within a limited simulation budget. MF-NPE performs neural posterior estimation with limited high-fidelity resources by virtue of transfer learning, with the ability to prioritize individual observations using active learning. On one statistical task with analytical ground-truth and two real-world tasks, MF-NPE shows comparable performance to current approaches while requiring up to two orders of magnitude fewer high-fidelity simulations. Overall, MF-NPE opens new opportunities to perform efficient Bayesian inference on computationally expensive simulators. © 2025, CC BY.

关键词： Stochastic models

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A Proficient and secure way of Transmission using Cryptography and Steganography 2

A Proficient and secure way of Transmission using Cryptograp...

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2nd International Conference on Technological Advancements in Computational sciences, ICTACS 2022

作者： Reddy, G Diwakara Kiran, Yaddanapudi Vssrr Udai Singh, Prabhdeep Singh, Shubhranshu Vikram Shaw, Sanchita Singh, Jitendra Chennai India Gitam University Pattancheru Mandal Department of Computer Science Hyderabad India Graphic Era Deemed to Be University Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Dehradun India Amity University Greater Uttar Pradesh Noida India Lloyd Institute of Engineering & Technology Department of CSE Uttar Pradesh Greater Noida India Uttaranchal University Division of Research & Innovation Uttarakhand Dehradun India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

People are concerned about the security of their data over the internet. The data can be protected in many ways to keep unauthorized individuals from accessing it. To secure data, steganography can be used in conjunction with cryptography. It is common for steganography to be used for hiding data or secret messages, whereas cryptography encrypts the messages so that they cannot be read. As a result, the proposed system combines both cryptography and steganography. A steganographic message can be concealed from prying eyes by using an image as a carrier of data. In steganography, writing is done secretly or covertly. The digital steganography algorithm uses text, graphics, and audio as cover media. Due to recent advancements in technology, steganography is challenging to employ to safeguard private data, messages, or digital photographs. This paper presents a new steganography strategy for confidential communications between private parties. A transformation of the ciphertext into an image system is also performed during this process. To implement XOR and ECC (Elliptic Curve Cryptography) encryption, three secure mechanisms were constructed using the least significant bit (LSB). In order to ensure a secure data transmission over web applications, both steganography and cryptography must be used in conjunction. Combined techniques can be used and replace the current security techniques, since there has been an incredible growth in security and awareness among individuals, groups, agencies, and government institutions. © 2022 IEEE.

关键词： Steganography

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DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

arXiv

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arXiv 2023年

作者： Yan, Zhiyuan Zhang, Yong Yuan, Xinhang Lyu, Siwei Wu, Baoyuan School of Data Science The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Tencent AI Lab China Department of Computer Science and Engineering University at Buffalo State University of New York United States

A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in inconsistent data inputs for detection models. Additionally, there are noticeable differences in experimental settings, and evaluation strategies and metrics lack standardization. To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation, and 3) standardized evaluation metrics and protocols to promote transparency and reproducibility. Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations. Moreover, we provide new insights based on extensive analysis of these evaluations from various perspectives (e.g., data augmentations, backbones). We hope that our efforts could facilitate future research and foster innovation in this increasingly critical domain. All codes, evaluations, and analyses of our benchmark are publicly available at https://***/SCLBD/DeepfakeBench. Copyright © 2023, The Authors. All rights reserved.

关键词： Data handling

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An Integrated Platform for Virtual Ecosystems and Wireless Edge Computing for Web 3.0

An Integrated Platform for Virtual Ecosystems and Wireless E...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Khushi Garg Mahee Jattu Hariharasitaraman S R. Raja Subramanian Division of Cyber Security and Digital Forensics VIT Bhopal University Madhya Pradesh India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Computer Science and Engineering VIT Bhopal University Madhya Pradesh India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

The emergence of Web 3.0 has paved the way for a new generation of the internet that enables users to view, publish, and own content independently. This innovation is being driven by a combination of blockchain, semantic communication, edge computing, and artificial intelligence, which together create value networks that facilitate participatory decision-making. Blockchain, in particular, offers security services by recording content in a decentralized and transparent manner. Additionally, it is compatible with decentralized wireless edge computing designs that can accurately express the intended meanings of contents without requiring a lot of resources. Building on this foundation, this research proposes a paradigm for unified blockchain-semantic ecosystems for Web 3.0 with wireless edge intelligence. Specifically, we present a semantic proof technique built on Oracle that enables Web 3.0's off-chain and on-chain interactions. To improve interaction efficiency and support Web 3.0, Oracle has developed an adaptable Deep Reinforcement Learning-based sharding technique. Our work also introduces a blockchain-based semantic exchange architecture that tokenizes semantic data into non-fungible tokens (NFT) for semantic exchange in this system. We employ Zero-Knowledge Proof to trade real semantic information without publicly posting it before accepting payment, in contrast to traditional NFT marketplaces. This makes it possible to trade fairly and privately. Overall, our proposed framework can precisely perform decentralized semantic sharing and information transfer, further achieving the advantages of semantic extraction and communication in Web 3.0.

关键词： Semantic Web Wireless communication Technological innovation Semantics Ecosystems Decision making Communication system security

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Predictive Analysis Using Modified Inception Net and Support Vector Machine for Forecasting Copra Yield in Agriculture

Predictive Analysis Using Modified Inception Net and Support...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Kavitha M G Raviprakash M L Kassem Al-Attabi B. Sathyavani S Meenakshi Sundaram Department of Computer science and engineering Kalpataru Institute of Technology Tiptur India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

One of the major economic sectors in various states is copra agriculture, with a significant portion of its global production coming from countries such as India, Indonesia, and Sri Lanka. However, the severity of copra issues can vary depending on factors such as moisture levels, fungal presence, and the quality of the copra, leading to imbalanced data and potential overfitting in copra image analysis. Threshold-based segmentation is involved to identify different segments of copra based on factors like moisture content. Higher moisture content may indicate areas prone to fungal growth or poor drying conditions, while segments with uniform colour and texture may signify high-quality copra. The proposed approach involves using the Modified Inception Net (MIN) for feature extraction and Support Vector Machine (SVM) for hyperparameter tuning. SVM handles the kernel function in nonlinear and high-dimensional spaces, enhancing its capabilities and mitigating overfitting. However, the MIN-SVM combination may require significant computational resources, particularly during training and parameter tuning. The proposed MIN-SVM method achieves promising performance metrics, including a Mean Absolute Error (MAE) of 0.071, Mean Square Error (MSE) of 0.0125, Matthews Correlation Coefficient (MCC) of 0.620 and $R^{2}$ value of 0.9410. This performance is compared to existing methods such as Back Propagation Algorithm (BPA), K-means and SVM.

关键词： Support vector machines Measurement Backpropagation Image segmentation Moisture Production Feature extraction

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ReBot: Scaling Robot Learning with Real-to-Sim-to-Real Robotic Video Synthesis

arXiv

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arXiv 2025年

作者： Fang, Yu Yang, Yue Zhu, Xinghao Zheng, Kaiyuan Bertasius, Gedas Szafir, Daniel Ding, Mingyu Department of Computer Science University of North Carolina at Chapel Hill 201 S Columbia St Chapel HillNC27599 United States Robotics and AI Institute 145 Broadway CambridgeMA02142 United States Department of Electrical and Computer Engineering University of Washington 1410 NE Campus Parkway SeattleWA98195 United States

Vision-language-action (VLA) models present a promising paradigm by training policies directly on real robot datasets like Open X-Embodiment. However, the high cost of real-world data collection hinders further data scaling, thereby restricting the generalizability of VLAs. In this paper, we introduce ReBot, a novel real-to-sim-to-real approach for scaling real robot datasets and adapting VLA models to target domains, which is the last-mile deployment challenge in robot manipulation. Specifically, ReBot replays real-world robot trajectories in simulation to diversify manipulated objects (real-to-sim), and integrates the simulated movements with inpainted real-world background to synthesize physically realistic and temporally consistent robot videos (sim-to-real). Our approach has several advantages: 1) it enjoys the benefit of real data to minimize the sim-to-real gap;2) it leverages the scalability of simulation;and 3) it can generalize a pretrained VLA to a target domain with fully automated data pipelines. Extensive experiments in both simulation and real-world environments show that ReBot significantly enhances the performance and robustness of VLAs. For example, in SimplerEnv with the WidowX robot, ReBot improved the in-domain performance of Octo by 7.2% and OpenVLA by 21.8%, and out-of-domain generalization by 19.9% and 9.4%, respectively. For real-world evaluation with a Franka robot, ReBot increased the success rates of Octo by 17% and OpenVLA by 20%. More information can be found at our project page. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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