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arXiv 2024年

作者： Wimalasiri, Chathura Sahoo, Prasan Kumar Dept. of Computer Engineering University of Peradeniya Peradeniya Sri Lanka Dept. of Computer Science and Information Engineering Chang Gung University Guishan Taiwan

In response to the increasing demand for efficient and non-invasive methods to estimate food weight, this paper presents a vision-based approach utilizing 2D images. The study employs a dataset of 2380 images comprising fourteen different food types in various portions, orientations, and containers. The proposed methodology integrates deep learning and computer vision techniques, specifically employing Faster R-CNN for food detection and MobileNetV3 for weight estimation. The detection model achieved a mean average precision (mAP) of 83.41%, an average Intersection over Union (IoU) of 91.82%, and a classification accuracy of 100%. For weight estimation, the model demonstrated a root mean squared error (RMSE) of 6.3204, a mean absolute percentage error (MAPE) of 0.0640%, and an R-squared value of 98.65%. The study underscores the potential applications of this technology in healthcare for nutrition counseling, fitness and wellness for dietary intake assessment, and smart food storage solutions to reduce waste. The results indicate that the combination of Faster R-CNN and MobileNetV3 provides a robust framework for accurate food weight estimation from 2D images, showcasing the synergy of computer vision and deep learning in practical applications. © 2024, CC BY-NC-ND.

关键词： Mean square error

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PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection

arXiv

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arXiv 2024年

作者： Ovi, Md Sultanul Islam Rahman, Md. Hasibur Hossain, Mohammad Arif Dept. of Computer Science George Mason University FairfaxVA United States Dept. of Computer Science and Engineering Green University of Bangladesh Bangladesh

Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. To combat these attacks, this paper introduces PhishGuard, an optimal custom ensemble model designed to improve phishing site detection. The model combines multiple machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost, to enhance detection accuracy. Through advanced feature selection methods such as SelectKBest and RFECV, and optimizations like hyperparameter tuning and data balancing, the model was trained and evaluated on four publicly available datasets. PhishGuard outperformed state-of-the-art models, achieving a detection accuracy of 99.05% on one of the datasets, with similarly high results across other datasets. This research demonstrates that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Phishing

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Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare

Distributionally Robust Clustered Federated Learning: A Case...

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IEEE Conference on Decision and Control

作者： Xenia Konti Hans Riess Manos Giannopoulos Yi Shen Michael J. Pencina Nicoleta J Economou-Zavlanos Michael M. Zavlanos Dept. of Computer Science Duke University Dept. of Electrical and Computer Engineering Duke University Dept. of Mechanical Engineering & Materials Science Duke University School of Medicine Duke University

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clustered Federated Learning (CS-RCFL). Our approach leverages the Wasserstein distance to construct ambiguity sets around each client’s empirical distribution that capture possible distribution shifts in the local data, enabling evaluation of worst-case model performance. We then propose a model-agnostic integer fractional program to determine the optimal distributionally robust clustering of clients into coalitions so that possible biases in the local models caused by statistically heterogeneous client datasets are avoided, and analyze our method for linear and logistic regression models. Finally, we discuss a federated learning protocol that ensures the privacy of client distributions, a critical consideration, for instance, when clients are healthcare institutions. We evaluate our algorithm on synthetic and real-world healthcare data.

关键词： Analytical models Logistic regression Privacy Protocols Ethnicity Hospitals Federated learning Neural networks Clustering algorithms Data models

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Secure Prefetching for Secure Cache Systems

Secure Prefetching for Secure Cache Systems

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Sumon Nath Agustin Navarro-Torres Alberto Ros Biswabandan Panda Dept. of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India Computer Engineering Department University of Murcia Murcia Spain

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Transient execution attacks like Spectre and its vari-ants can cause information leakage through a cache hierarchy. There are two classes of techniques that mitigate speculative execution attacks: delay-based and invisible speculation. Invisible speculation-based techniques like GhostMinion are the high-performing yet secure techniques that mitigate all kinds of spec-ulative execution attacks. Similar to a cache system, hardware prefetchers can also cause speculative information leakage. To mitigate it, GhostMinion advocates on-commit prefetching on top of strictness ordering in the cache system. Our experiments show that the GhostMinion cache system interacts negatively with the hardware prefetchers leading to redundant traffic between different levels of cache. This traffic causes contention and increases the miss latency leading to performance loss. Next, we observe that on-commit prefetching enforced by GhostMinion leads to nerformance loss as it affects the prefetcher timeliness. We perform the first thorough analysis of the interaction between state-of-the-art prefetching techniques and the secure cache system. Based on this, we propose two microarchitectural solutions that ensure high performance while designing secure prefetchers on top of secure cache system. The first solution detects and filters redundant traffic when updating the cache hierarchy non-speculatively. The second solution ensures the timeliness of the prefetcher to compensate for the delayed triggering of prefetch requests at commit, resulting in a secure yet high-performing prefetcher. Overall, our enhancements are secure and provide synergistic interactions between hardware prefetchers and a secure cache system. Our experiments show that our filter consistently improves the performance of secure cache systems like GhostMinion in the presence of state-of-the-art prefetchers (by 1.9% for single-core and 19.0% for multi-core for the top-performing prefetcher). We see a synergistic behavior of

关键词： Filters Microarchitecture Multicore processing Prefetching Information leakage Turning Hardware Proposals Transient analysis

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Self-supervised Mamba-based Mastoidectomy Shape Prediction for Cochlear Implant Surgery

arXiv

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arXiv 2024年

作者： Zhang, Yike Davalos, Eduardo Su, Dingjie Lou, Ange Noble, Jack H. Dept. of Computer Science Vanderbilt University United States Dept. of Electrical and Computer Engineering Vanderbilt University United States

Cochlear Implant (CI) procedures require the insertion of an electrode array into the cochlea within the inner ear. To achieve this, mastoidectomy, a surgical procedure involving the removal of part of the mastoid region of the temporal bone using a high-speed drill provides safe access to the cochlea through the middle and inner ear. In this paper, we propose a novel Mamba-based method to synthesize the mastoidectomy volume using only preoperative Computed Tomography (CT) scans, where the mastoid remains intact. Our approach introduces a self-supervised learning framework designed to predict the mastoidectomy shape and reconstruct a 3D post-mastoidectomy surface directly from preoperative CT scans. This reconstruction aligns with intraoperative microscope views, enabling various downstream surgical applications. For training, we leverage postoperative CT scans to bypass manual data cleaning and labeling, even when the region removed during mastoidectomy is affected by challenges such as metal artifacts, low signal-to-noise ratio, or electrode wiring. Our method achieves a mean Dice score of 0.70 in estimating mastoidectomy regions, demonstrating its effectiveness for accurate and efficient surgical preoperative planning. Copyright © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning 16

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Shrestha, Sujan Taniguchi, Yoji Tanaka, Tetsuo Graduate school of Engineering Kanagawa Institute of Tehnology Course of Information and computer sciences Atsugi Japan Daiichi Institute of Technology Artificial Intelligence and Data Science Department of Information Tokyo Japan Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information and Computer Sciences Atsugi Japan

ISBN: (纸本)9798350377903

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence;however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Creation of Bookshelf Using Autodesk 3ds Max: 3D Modelling and Rendering 11

Creation of Bookshelf Using Autodesk 3ds Max: 3D Modelling a...

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11th International Conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Jain, Akshat Dangi, Priyal Samanta, Debabrata Dutta, Soumi Bhattacharya, Abhishek Joseph, Niju P. Christ University Dept. of Computer Science Karnataka Bangalore India Dept. of Computing and Information Technologies Kosovo Europe Prishtina10000 Serbia Rochester Institute of Technology Rit Global Campus Kosovo Europe Prishtina10000 Serbia Institute of Engineering & Management Dept. of Computer Science & Applications Kolkata India

ISBN: (纸本)9781665475129

The step-by-step process of creating a bookshelf design is specified, including the ProBoolean compound primitive, applying edit poly modifier, using detach option, making use of lattice modifier, using bend modifier, using twist modifier. The manner in which materials are added to the model, together with environment lighting and renderer configuration, is defined. Procedures and methods for rendering are also defined. What we aim to achieve through our research is to create a Bookshelf design that uses materials to enhance the models. The shapes used in the model were Box, Teapot, sphere, chamfercyl, Oiltank, ProBoolean compound. The modifiers used were edit poly, bend, twist, lattice. Afterwards we used the Arnold light and material editor to enhance and glorify the model. © 2023 IEEE.

关键词： 3D modeling

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Predictive Modeling for Cirrhosis Diagnosis: A Machine Learning Exploration 1

Predictive Modeling for Cirrhosis Diagnosis: A Machine Learn...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Sudeesh Kumar, V. Natarajan, B. Murali, P. Selvam, Prabu Venkatraman, K. Nagarajan, N.R. Amrita Vishwa Vidyapeetham Dept. of Artificial Intelligence Chennai India School of Computer science and Engg. VIT University Chennai India Saveetha school of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer science and Engg. Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of CSE Chennai India K.Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350371802

Cirrhosis manifests as a condition wherein healthy liver tissue is gradually replaced by scar tissue, resulting in a decline in liver function. This ailment frequently correlates with autoimmune disorders, non-alcoholic fatty liver disease, excessive alcohol intake, and persistent liver conditions such as hepatitis B or C. As cirrhosis progresses, it can cause liver failure and various complications such as ascites, portal hypertension, hepatic encephalopathy, and an increased risk of liver cancer. Since the liver plays a crucial role in filtering blood and performing essential functions, individuals with acute liver failure are more prone to infections, particularly those affecting the circulatory, respiratory, and urinary systems. Kidney failure is a common consequence of liver failure, especially in cases of acetaminophen overdose, as this medication can damage both the liver and kidneys. In the realm of healthcare, the integration of machine learning has enabled the early prediction of liver cirrhosis using historical datasets. The aim of this research is to create a machine learning model that utilizes artificial intelligence to forecast the occurrence of liver cirrhosis using supplied data. By employing hyperparameter tuning techniques to enhance accuracy, this research employs machine learning techniques like logistic regression and Gradient Boosted Classifier using XGBoost. Utilizing GridSearch within XGBoost, this research work achieves an impressive accuracy rate of 98%, accompanied by precision, recall, and F1-scores of 96%, 97%, and 96%, respectively. These findings hold promise for the advancement of more sophisticated and accurate liver cirrhosis prediction models, thereby benefiting researchers in the field. © 2024 IEEE.

关键词： Machine learning

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Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery

arXiv

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arXiv 2024年

For those experiencing severe-to-profound sensorineural hearing loss, the cochlear implant (CI) is the preferred treatment. Augmented reality (AR) aided surgery can potentially improve CI procedures and hearing outcomes. Typically, AR solutions for image-guided surgery rely on optical tracking systems to register pre-operative planning information to the display so that hidden anatomy or other important information can be overlayed and co-registered with the view of the surgical scene. In this paper, our goal is to develop a method that permits direct 2D-to-3D registration of the microscope video to the pre-operative Computed Tomography (CT) scan without the need for external tracking equipment. Our proposed solution involves using surface mapping of a portion of the incus in surgical recordings and determining the pose of this structure relative to the surgical microscope by performing pose estimation via the perspective-n-point (PnP) algorithm. This registration can then be applied to pre-operative segmentations of other anatomy-of-interest, as well as the planned electrode insertion trajectory to co-register this information for the AR display. Our results demonstrate the accuracy with an average rotation error of less than 25 degrees and a translation error of less than 2 mm, 3 mm, and 0.55% for the x, y, and z axes, respectively. Our proposed method has the potential to be applicable and generalized to other surgical procedures while only needing a monocular microscope during intra-operation. Copyright © 2024, The Authors. All rights reserved.

关键词： Augmented reality

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Cao Robot for Taiwanese/English Knowledge Graph Application

Cao Robot for Taiwanese/English Knowledge Graph Application

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Oriental COCOSDA International Conference on Speech Database and Assessments

作者： Chang-Shing Lee Mei-Hui Wang Guan-Ying Tseng Chao-Cyuan Yue Hao-Chun Hsieh Marek Reformat Dept. of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Dept. of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798331506032

ISBN: (纸本)9798331506049

This paper proposes a Content Attention Ontology (CAO) robot for constructing Taiwanese/English Knowledge Graphs (KGs) by prompting audio or texts to Large Language Models (LLMs), including TAIDE, Zephyr, and Llama 3.1. The collected data includes lecture videos from the IEEE WCCI 2024 in Japan and the 2024 National Language Development Forum in Taiwan, along with students' learning data from the 2024 Summer School on Taiwanese/English Human and Robot Co-Learning at Rende Elementary School (RDES). In addition, the fundamental concepts of Computational Intelligence (CI) and Quantum CI (QCI) learning were incorporated into the study. The generative KGs highlight important concepts, relations, and communities within the collected teaching and learning data. Additionally, we utilized data from subjects wearing braincomputer interface (BCI) devices while speaking Taiwanese/English to generate KGs. We also compared the differences in these KGs and analyzed the similarities between the transcribed texts of lectures and learners. In the future, we plan to expand the CAO robot to more validation fields across Taiwan, aiming to engage young students in speaking Taiwanese while concurrently enhancing their English language skills through interaction with the robot.

关键词： Measurement Quantum computing Statistical analysis Large language models Knowledge graphs Speech enhancement Ontologies Physiology Robots Videos

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