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IEEE International Conference on Communications (ICC)

作者： Trent Reichenbach Chenglong Fu Xiaojiang Du Jia Di Yuede Ji Dept. of Electrical and Computer Engr Stevens Institute of Technology Hoboken NJ USA Dept. of Software and Information Systems UNC Charlotte Charlotte NC USA Dept. of Computer Science and Engr University of Arkansas Fayetteville AR USA Dept. Computer Science and Engr University of North Texas Denton TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

As smart home IoT systems gain popularity, they inevitably become targets for security risks and concerns. Among various cyber-attacks targeting these systems, the fake event attack poses significant issues due to its ability to manipulate secure devices through automation rules. In response to this threat, we propose TrustEvent - a system designed to offer end-to-end event signature verification. By integrating TrustEvent with existing home automation platforms, event authenticity is verified against signatures generated from edge devices before these events trigger automation rule execution. Notably, we have developed a signature proxy module, enhancing our system's compatibility across various platform scenarios. We have implemented a TrustEvent prototype in conjunction with existing commercial smart home IoT platforms, evaluating its overhead in the process. Our experimentation demonstrates that our system only marginally increases the automation execution latency, by an average of 3.74 seconds, representing a acceptable compromise for enhanced security.

关键词： Handwriting recognition Automation System performance Instruments Prototypes Smart homes Static analysis

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Metaverse and Healthcare: An Empirical Investigation 5

Metaverse and Healthcare: An Empirical Investigation

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Mahdi, Hussain Falih Sharma, Bhumika Sille, Roohi Choudhury, Tanupriya University of Diyala Department of Computer and Software Engineering Baquba Iraq Uttarakhand Dehradun248007 India SoCS Uttarakhand Dehradun248007 India Graphic Era Hill University CSE Dept. Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350337525

A virtual setting that provides experiences in real-time is introduced through a new field of research called metaverse. Combining the metaverse with healthcare results in a unique real-space experience that enables both patients and physicians to evaluate the health of their respective patients and engage in in-person interactions. The metaverse is a collection of technologies that includes elements of artificial intelligence, virtual reality, augmented reality, the Internet of Things, and more. The integration of healthcare and the metaverse has made it possible to focus on the patient's health in a more accurate, effective, and precise manner. Using the many imaging technologies in the metaverse, researchers are working to speed up the usage of the metaverse for patient diagnosis in addition to clinical diagnostics. It is no longer necessary for a doctor to be there in person while making a diagnosis. In order to diagnose patients in a highly effective and exact manner, there is a lot of research being done to improve the metaverse environment. This study's main focus will be on how the metaverse has streamlined the healthcare industry and the various healthcare amenities it offers. This article offers a thorough examination of clinical and diagnostic developments achieved in the metaverse in an effort to enhance the field of healthcare in the metaverse. Also, the focus is on the cutting-edge developments that will transform the metaverse healthcare industry. © 2023 IEEE.

关键词： Augmented reality

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Enhancing Robotic Grasp Failure Prediction Using A Pre-hoc Explainability Framework*

Enhancing Robotic Grasp Failure Prediction Using A Pre-hoc E...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Cagla Acun Ali Ashary Dan O. Popa Olfa Nasraoui Dept. of Computer Science & Engineering Knowledge Discovery & Web Mining Lab Next Generation Systems Group

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Enhancing the explainability of machine learning (ML) models is crucial for bridging their use gap for practical applications of robotic autonomy. The common standard for adding explainability has been to use a post-hoc explanation approach. However, post-hoc approaches have recently attracted criticism for their lack of transparency, specifically because the post-hoc methods learn a surrogate model after the predictive model has already been learned, which means that the explanations are not authentically explaining the model’s behavior. This study aims to add explainability to the task of robot grasping failure prediction using Factorization Machines as the predictive model and using a novel pre-hoc explainability framework to learn an explainable Factorization Machine model. Unlike post-hoc methods, pre-hoc explainability starts with learning the explainable model before training the black-box model and then provides guidance while learning the latter to make predictions that are faithful to the explanations through a regularization mechanism. Through a detailed case study, we explore the trade-off between prediction accuracy and explanation fidelity and show that our framework is able to make predictions that are more accurate than an explainable white-box model while simultaneously learning a model whose pre-hoc explanations achieve a high level of fidelity relative to the predictions. Results show that our framework can predict the robustness of the grasp with 83% accuracy while explaining that increased effort exerted in Joint 2 of Finger 3 contributes tremendously to producing grasp failure, which is in contrast to increased efforts exerted at Joint 2 of Fingers 1 and 2 and at joint 1 of Finger 3, that all lead to reducing grasp failure.

关键词： Training Knowledge engineering Accuracy Fingers Grasping Predictive models Robustness Robots Standards Optimization

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Genair: Generative AI for Resilient Urban Air Mobility with Vtols in Disaster Evacuation 24

Genair: Generative AI for Resilient Urban Air Mobility with ...

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Proceedings of the Winter Simulation Conference

作者： Abdullah Alsaheal William Hoy Priscila Haro Asad Rehman Nurcin Celik Dept. of Industrial and Systems Engineering University of Miami Coral Gables FL USA Dept. of Mathematics and Computer Science Saint Louis University Madrid SPAIN

ISBN: (纸本)9798331534202

Coastal regions face growing threats, making timely and safe evacuation paramount. Current plans rely heavily on congested ground transportation, leading to delays and heightened stress levels. The emerging field of Urban Air Mobility (UAM), utilizing Vertical Take-Off and Landing Vehicles (VTOLs), promises to alleviate these issues by providing solutions for quick evacuation strategies. Here, we aim to leverage Generative Adversarial Networks (GANs) to expand limited datasets with synthetic data specific to disaster scenarios, evacuation routes, airspace considerations, and the impact of real-time weather events, enabling robust simulation of UAM deployment in disaster evacuations. We identified two applicable scenarios: i) UAM for Extreme Weather Emergency Evacuation and ii) Hospital Evacuations using VTOLs as use cases and illustrated their impact. This research seeks to pave the way for optimized, data-driven evacuation planning with UAM and VTOLs, ultimately enhancing the safety and efficiency of evacuations in the face of extreme events.

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TCCFD: An Efficient Tree-based Framework for Credit Card Fraud Detection 16

TCCFD: An Efficient Tree-based Framework for Credit Card Fra...

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16th International Conference on computer engineering and systems, ICCES 2021

作者： Own, Reem M. Salem, Sameh A. Mohamed, Amr E. Helwan University Faculty of Engineering Computer Systems Engineering Dept. Cairo Egypt

ISBN: (纸本)9781665408677

Due to digitalization, credit card fraud has become one of the most prevalent global risks. The global economy loses billions of dollars annually due to credit card fraud. Financial institutions adopt a strategic approach to credit card fraud mitigation, relying on business rules to combat the occurrence of fraud and sophisticated analysis to detect fraud. However, Fraudsters are altering their patterns regularly with the help of new technologies. Thus, organizations continually seek to detect new fraud patterns. The detection of credit card fraud has improved tremendously through using artificial intelligence, particularly machine learning techniques. We introduce through this paper an efficient framework that adopts various tree-based machine learning models to detect fraudulent transactions. We apply the proposed framework to a highly asymmetric real-world dataset. The proposed framework integrating the extra trees classifier with the borderline synthetic minority oversampling technique surpasses others and previously developed models with 99.96 % accuracy and 96% area under the ROC curve. The proposed framework asserts its efficacy and reliability in classification while also addressing the class imbalance issue. © 2021 IEEE.

关键词： Machine learning

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BioPoint: Enhancing Human-computer Interaction through Single-Site, Multi-Sensor Gesture Recognition

BioPoint: Enhancing Human-Computer Interaction through Singl...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Xavier Isabel Evan Campbell Loïc Olivier Gabriel Gagné Erik Scheme Ulysse Côté-Allard Gabriel Gagnon-Turcotte Dept. of Electrical and Computer Engineering Laval University Québec Canada Department of Electrical and Computer Engineering University of New Brunswick Fredericton Canada Dept. of Technology Systems University of Oslo Oslo Norway

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Advancements in human-computer interaction (HCI) and machine learning are seen as key avenues to help individuals living with upper limb disabilities in accomplishing their activities of daily living. Multi-channel myoelectric systems are a promising approach for HCI due to their intuitive and accurate capture of user intent through muscle activity. However, such systems are still bulky compared to widely accepted smartwatches-like devices and as such pose a challenge for seamless integration into daily life. Consequently, this work presents the first single-site EMG-IMU-PPG system for multi-gesture recognition from the wrist. Overall, when evaluated on eight able-bodied participant, the system achieves an average accuracy of 65.28%±10.45% on 17 finger and wrist gestures and 90.98%±5.65% on eight hand gestures. This study also introduces "Myo Hero", an online environment inspired by "Guitar Hero." Myo Hero provides a dynamic method for testing multi-gesture recognition systems, improving the evaluation process’s appeal. Online evaluation of the multimodal system substantially outperformed random play, signifying EMG-IMU-PPG’s potential for human-computer interaction.

关键词： Human computer interaction Wrist Accuracy Pipelines Gesture recognition Rhythm Electromyography Recording Synchronization Testing

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Medicinal Plants Recognition Using Deep Learning

Medicinal Plants Recognition Using Deep Learning

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Multimedia Computing, Networking and Applications (MCNA), International Conference on

作者： Yousef Sharrab Dimah Al-Fraihat Monther Tarawneh Ahmad Sharieh Dept. of Computer Science Isra University Amman Jordan Dept. of Software Engineering Isra University Amman Jordan Dept. of Computer Science The university of jordan Amman Jordan

The use of medicinal plants has been a longstanding practice in traditional medicine worldwide. Accurately identifying medicinal plants is crucial for determining their medicinal properties and potential applications. However, it can be a challenging task due to the complexity of their appearance. Variations in growth stage, lighting, and imaging conditions can make classification challenging, which limits the application of traditional methods for plant identification. This paper proposes a deep learning-based approach that uses a convolutional neural network (CNN) based on the VGG-16 model. With a dataset of 25,686 images, the CNN is capable of learning and representing complex features in images, enabling it to recognize and classify medicinal plants with high accuracy. The proposed approach can efficiently classify plants with different growth stages, lighting conditions, and imaging settings, providing a reliable tool for plant identification. We achieved an impressive recognition rate of 98%, demonstrating the feasibility of using deep learning techniques for accurate plant classification. The proposed approach has enormous potential for providing healthcare professionals and herbal medicine researchers with a reliable tool for identifying herbal plants. the study represents an essential advancement in the use of deep learning techniques for medicinal plant recognition, overcoming the challenges posed by their complex appearance. The proposed approach has farreaching implications and can significantly impact the field of herbal medicine research, enabling researchers and healthcare professionals to identify and classify medicinal plants more accurately

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Monitoring and Forecasting Quality Indicators of Predough Production in the Bakery Industry

Monitoring and Forecasting Quality Indicators of Predough Pr...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Nataliia Lutska Nataliia Zaiets Lidiia Vlasenko Andrii Zhyltsov dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of electromagnetic systems The Institute of Electrodynamics of the National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The work demonstrates the process of developing a machine learning model for a system for monitoring and controlling the quality of predough at a bakery. This system will provide continuous analysis of the predough parameters, such as temperature, humidity, lifting force and titranic acidity, which will ensure timely detection of deviations in the quality of the predough and avoid production problems. In addition, it will help automate the quality control process and ensure the stability of production processes. Analysis and forecasting of dough parameters will improve the quality of produced bread or other bakery products. Management based also on the predicted values of technological indicators of the predough will allow the enterprise to avoid unnecessary costs of materials and energy and increase economic efficiency. As part of the study, seven machine learning models were created and reviewed, including three linear and four varieties of binary decision trees. An analysis of the best model - a regression decision tree - was carried out, confirming its effectiveness and the validity of the resulting hierarchical structure, in particular, the prediction of important indicators of predough with a high degree of reliability, which indicates its importance in practical applications in production. The created model can be used to monitor and control product quality in real time, allowing operators to quickly identify and correct any deviations in process performance. Also, by analyzing changes in the variables of the predough production process, it is possible to identify precursors of possible production accidents or deviations in product quality, which will allow timely measures to be taken to avoid them.

关键词： Force Process control Production Machine learning Quality control Humidity Predictive models Stability analysis Monitoring Thermal stability

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MIRAGE: Multi-Binary Image Risk Assessment with Attack Graph Employment

arXiv

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arXiv 2023年

作者： Tayouri, David Nachum, Telem Shabtai, Asaf Dept. of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

Attackers can exploit known vulnerabilities to infiltrate a device’s firmware and the communication between firmware binaries, in order to pass between them. To improve cybersecurity, organizations must identify and mitigate the risks of the firmware they use. An attack graph (AG) can be used to assess and visually display firmware’s risks by organizing the identified vulnerabilities into attack paths composed of sequences of actions attackers may perform to compromise firmware images. In this paper, we utilize AGs for firmware risk assessment. We propose MIRAGE (Multi-binary Image Risk Assessment with Attack Graph Employment), a framework for identifying potential attack vectors and vulnerable interactions between firmware binaries;MIRAGE accomplishes this by generating AGs for firmware inter-binary communication. The use cases of the proposed firmware AG generation framework include the identification of risky external interactions, supply chain risk assessment, and security analysis with digital twins. To evaluate the MIRAGE framework, we collected a dataset of 703 firmware images. We also propose a model for examining the risks of firmware binaries, demonstrate the model’s implementation on the dataset of firmware images, and list the riskiest binaries. Copyright © 2023, The Authors. All rights reserved.

关键词： Firmware

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Mitigating YouTube Recommendation Polarity using BERT and K-Means Clustering

Mitigating YouTube Recommendation Polarity using BERT and K-...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Zoheb Ahmad Danish Quasim Shreeya Garg Vinod Kumar Dept. of Software Engineering Delhi Technological University New Delhi India Dept. of Computer Engineering Delhi Technological University New Delhi India Dept. of Computer Engineering and Bioscience IIIT Delhi New Delhi India

YouTube's recommendation system is famous for its success in maintaining high retention rates. The cause of its success is its ability to learn and predict an individual user's preferences appropriately. An unintended consequence, however, is that users get stuck in what is known as their own “echo chambers” when dealing with and feeding users back their preferences. These echo chambers can cause increasing perspective bias within users, making it difficult for users to understand differing opinions. This work aims to prepare a model that counteracts YouTube's recommendation system by forcefully exposing users to content from varying viewpoints. The SSKA pipeline (Suno Sabki, Karo Apni) is a complementary deep learning model that involves Natural Language Processing (NLP) and K-Means clustering. It utilizes modern software libraries such as the YouTube API (Application Programming Interface) for data collection and was trained and tested on a varied set of users. The results prove that the model is successful in decreasing the bias recommendation by exposing users to the content of varying opinions and helping them break away from their echo chambers. The proposed methodology of explicitly exposing users to the content of varying opinions can positively impact local societies and the global community.

关键词： Adaptation models Video on demand software libraries Social networking (online) Pipelines Psychology Transformers

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