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GAD: A Real-Time Gait Anomaly Detection System with Online Adaptive Learning 39th

GAD: A Real-Time Gait Anomaly Detection System with Online A...

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39th International Conference on ICT Systems Security and Privacy Protection (SEC)

作者： Lee, Ming-Chang Lin, Jia-Chun Katsikas, Sokratis Norwegian Univ Sci & Technol NTNU Dept Informat Secur & Commun Technol Gjovik Norway

ISBN: (纸本)9783031651779;9783031651755;9783031651748

Gait anomaly detection is a task that involves detecting deviations from a person's normal gait pattern. These deviations can indicate health issues and medical conditions in the healthcare domain, or fraudulent impersonation and unauthorized identity access in the security domain. A number of gait anomaly detection approaches have been introduced, but many of them require offline data preprocessing, offline model learning, setting parameters, and so on, which might restrict their effectiveness and applicability in real-world scenarios. To address these issues, this paper introduces GAD, a real-time gait anomaly detection system. GAD focuses on detecting anomalies within an individual's three-dimensional accelerometer readings based on dimensionality reduction and Long Short-Term Memory (LSTM). Upon being launched, GAD begins collecting a gait segment from the user and training an anomaly detector to learn the user's walking pattern on the fly. If the subsequent model verification is successful, which involves validating the trained detector using the user's subsequent steps, the detector is employed to identify abnormalities in the user's subsequent gait readings at the user's request. The anomaly detector will be retained online to adapt to minor pattern changes and will undergo retraining as long as it cannot provide adequate prediction. We explored two methods for capturing users' gait segments: a personalized method tailored to each individual's step length, and a uniform method utilizing a fixed step length. Experimental results using an open-source gait dataset show that GAD achieves a higher detection accuracy ratio when combined with the personalized method.

关键词： Gait Real-time Time Series Anomaly Detection LSTM Dimensionality Reduction

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Evaluation of Sentinel-1 GRD Data with GEE for Floods Mapping in Rubkona, South Sudan 8th

Evaluation of Sentinel-1 GRD Data with GEE for Floods Mappin...

引用

8th International Conference on information technology in Disaster Risk Reduction (ITDRR)

作者： Kenyi, Manzu Gerald Simon Yamamoto, Kayoko Univ Electrocommun Chofugaoka 1-5-1 Chofu Tokyo Japan

ISBN: (纸本)9783031640391;9783031640377;9783031640360

This study explores the integration of Sentinel-1 synthetic aperture radar (SAR) imagery and Google Earth Engine (GEE) for processing for flood mapping. The methodology involves the generation of flood and water masks through systematic processing of Sentinel-1 GRD data within the GEE platform. The RefineLEE algorithm is employed to reduce speckle noise and enhance the accuracy of flood extent delineation. The study includes a comprehensive validation process, incorporating ground truth data or independent sources to assess the accuracy of the derived flood and water masks. The validation aims to quantify the performance of the methodology in capturing flood extents against ground truth. Key outcomes include improved flood mapping accuracy backed by ground truth, which is a potential for operational flood monitoring. The combination of Sentinel-1 imagery and GEE processing demonstrate a robust methodology for mapping floods and can be used for providing valuable information for disaster management, environmental monitoring, and water resource management. This study contributes to flood mapping, offering a scalable and efficient approach for monitoring water-related events using freely available satellite data. The results highlight the significance of integrating SAR data processing with advanced algorithms and cloud-based platforms for accurate and timely flood mapping applications.

关键词： Floods Google Earth Engine Sentinel-1 Flood mapping

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Vibration-Based Operating Status Monitoring of a Production Line with Low-Cost IoT Devices 43rd

Vibration-Based Operating Status Monitoring of a Production ...

引用

43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Meckler, Sascha Dorsch, Rene Filipp, Peter Fraunhofer Inst Integrated Circuits IIS Fraunhofer IIS Nurnberg Germany

ISBN: (纸本)9783031716393;9783031716379;9783031716362

Many production lines still in use today have been designed without provisions or machine interfaces for data recording and data analysis. An efficient solution for generating operation metrics for these brownfield lines is smart retrofitting with Internet of Things (IoT) devices. We present a cost-efficient, nonintrusive IoT solution for monitoring the operating status of modules of an assembly line based on the measured background vibrations. In contrast to existing solutions which often examine a single machine or process step, our solution monitors entire production modules with many moving components on a higher, business-focused level. The vibration is measured by a wireless network of low-cost IoT devices. A middleware software processes the sensor data for later analysis with unsupervised machine learning. Experiments at an assembly line have proven that low-cost IoT solutions are capable of identifying normal operation, state transitions, and standstill of production modules from the background vibrations. The monitoring of the actual operating status is necessary for deriving key indicators for business questions or maintenance optimization.

关键词： IoT retrofitting vibration analysis AI in manufacturing

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Finding Logical Vulnerability in Policies Using Three-Level Semantic Framework 20th

Finding Logical Vulnerability in Policies Using Three-Level ...

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Bataityte, Karolina Vassilev, Vassil Gill, Olivia Jo London Metropolitan Univ Sch Comp London England London Metropolitan Univ Cyber Secur Res Ctr London England

ISBN: (纸本)9783031632259;9783031632235;9783031632228

We present the continuation of our work on a three-level framework, which can be used to model and analyze the identification-authentication- authorization policies. Finding the gaps in such policies is challenging. We explore the cases when operations become accessible to the user because of flawed or missing authentication methods. Our objective is to model the domain and find such vulnerabilities. Our proposed framework has three levels. Each level is built on top of a previous one. The first is ontological, where we model the static domain in OWL;the second is logical, where we model the dynamic using SWRL;and the third is analytical level, where we utilize the reasoner to get the results. In this paper, we present the algorithm, which finds vulnerable situations in the policies or confirms that there are no vulnerable situations. We have modelled a couple of policies from different user-based applications to validate our approach as well as demonstrate the feasibility of using it on policies from the actual systems.

关键词： Logical Vulnerability Identification Authentication and Authorization Policies OWL SWRL Reasoner

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Detecting Illicit Data Leaks on Android Smartphones Using an Artificial Intelligence Models 1

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Nikiema, Serge Lionel Sabane, Aminata Kabore, Abdoul-Kader Kafando, Rodrique Bissyande, Tegawende F. Ctr Excellence IA Dev CITADEL Ouagadougou Burkina Faso Univ Virtuelle Burkina Faso Ouagadougou Burkina Faso Univ Joseph Ki Zerbo UJKZ Ouagadougou Burkina Faso

ISBN: (数字)9783031632150

ISBN: (纸本)9783031632174;9783031632150;9783031632143

In today's digital landscape, hackers and espionage agents are increasingly targeting Android, the world's most prevalent mobile operating system. We introduce DeepDetector - a system based on artificial intelligence to recognize data thefts in Android. This model is based upon a large dataset comprising of clean and tainted network traffic trained using a Random Forest Classifier. DeepDetector scores high in two main areas as it achieves 82.9% accuracy for connection anomaly detection and 89.9% recall in connection anomaly detection whereas it gets 78.9% accuracy and 81.6 recall in terms of detection of under the system mounted with Raspberry Pi, automatic data collection, preparing of a dataset, training and testing of the model, as well as leak detection are ensured. In this regard, DeepDetector offers a viable way of enhancing Android user security.

关键词： Android mobile security data leak machine learning anomaly detection

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PTSD Detection Using Physiological Markers 6th

PTSD Detection Using Physiological Markers

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6th ifip WG 5.5 International Cross-Domain Conference on Internet of Things

作者： Rachakonda, Laavanya Bipin, K. C. Univ North Carolina Wilmington Dept Comp Sci Wilmington NC 28403 USA

ISBN: (纸本)9783031458842;9783031458828;9783031458811

This is an extended abstract for a Research Demo Session based on our published work [1]. PTSD has been a major problem in our society and much research has been done along the line to predict and diagnose PTSD. Our method helps to predict PTSD in its early stage with the help of physiological markers which combined with prior information about the patient like PTSD history, exposure to trauma, substance abuse disorder and other information helps to create a risk score with more accuracy. Due to the lack of a public dataset on this domain, we used different uni variate relationships of physiological markers with PTSD to create a multi-modal model using a slight modification of the naive Bayes algorithm. Implementation of a micro-controller along with the cloud IoT platform and a mobile app is created to demo the possibility of the system which helps healthcare providers and users to timely track and monitor PTSD risks with background information and priors accurately.

关键词： Internet of Things (IoT) Smart Healthcare PTSD Detection Post-Traumatic Stress Disorder Probability of PTSD Naive Bayes Machine Learning

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Partner Selection in Additive Manufacturing Networks 43rd

Partner Selection in Additive Manufacturing Networks

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43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Chen, Xiaoli Thi, Yen Mai Sprigode, Julia Riedel, Ralph Grzona, Pierre Westsachs Hsch Zwickau Kornmarkt 1 D-08056 Zwickau Germany Tech Univ Chemnitz D-09111 Chemnitz Germany

ISBN: (纸本)9783031716317;9783031716294;9783031716287

Considering the special features of additive manufacturing networks, and with the aim to realize a more efficient functionality of these networks, this work focuses on the issues of partner selection. Based on an in-depth literature review encompassing more than 170 papers, 61 criteria clusters are listed as a general basis. A horizontal and vertical classification of criteria is further conducted, attempting to cover different situations of partner selection in additive manufacturing networks. Here, economic, social, environmental and resilience perspectives are composed as the horizontal view. Key stages of additive manufacturing processes serve as the vertical one. A mathematical algorithm model is further developed to facilitate partner matching. Within the algorithm model, motivations and their relative priorities are considered. This work contributes to enhancing the reliability of partner selection processes, which in the end improves the efficiency of networks' functionality.

关键词： partner selection network additive manufacturing resilience

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A Voting Approach for Explainable Classification with Rule Learning 20th

A Voting Approach for Explainable Classification with Rule L...

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Noessig, Albert Hell, Tobias Moser, Georg Univ Innsbruck Dept Comp Sci Tyrol Austria Data Lab Hell GmbH Europastr 2a A-6170 Tyrol Austria

ISBN: (纸本)9783031632259;9783031632235;9783031632228

State-of-the-art results in typical classification tasks are mostly achieved by unexplainable machine learning methods, like deep neural networks, for instance. Contrarily, in this paper, we investigate the application of rule learning methods in such a context. Thus, classifications become based on comprehensible (first-order) rules, explaining the predictions made. In general, however, rule-based classifications are less accurate than state-of-the-art results (often significantly). As main contribution, we introduce a voting approach combining both worlds, aiming to achieve comparable results as (unexplainable) state-of-the-art methods, while still providing explanations in the form of comprehensible rules. Considering a variety of benchmark data sets including a use case of significant interest to insurance industries, we prove that our approach not only clearly outperforms ordinary rule learning methods, but also yields results on a par with state-of-the-art outcomes.

关键词： Rule Learning Explainable Artificial Intelligence Ensemble Learning Voting Machine Learning

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Consideration of App Store-Related Cases and New Legislations in the EU, the US and Japan 16th

Consideration of App Store-Related Cases and New Legislation...

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16th ifip International Conference on Human Choice and Computers (HCC)

作者： Nakashima, Mika Chuo Univ Fac Global Informat Tokyo Japan

ISBN: (纸本)9783031675379;9783031675355;9783031675348

Currently, investigations, lawsuits, and the introduction of new legislation, including new legislative proposals for ex-ante regulations concerning app stores on digital platforms, are unfolding across various jurisdictions worldwide. In the EU, the European Commission investigated Apple's app store. The Digital Markets Act-a comprehensive ex-ante regulation for digital platforms-came into effect on May 2, 2023, encompassing regulations specific to app stores. In the US, Epic Games filed a lawsuit against Apple and Google, and a bill known as the Open App Markets Act, targeting app stores, was proposed on June 11, 2021. In Japan, the Fair Trade Commission conducted an inspection of Apple, and Apple and Google were designated as app store-operating companies under the Act on Improving Transparency and Fairness of Digital Platforms on April 1, 2021. Moreover, on June 16, 2023, the Digital Market Competition Council, established under the Cabinet, publicly released the Competition Assessment of the Mobile Ecosystem Final Report, signaling a shift toward the legislation of ex-ante regulations for app stores. This paper explores app store-related cases and new legislation (or proposed legislation) in Japan, the US, and Europe, with the aim of examining the interpretation of Japan's Antimonopoly Act and identifying optimal approaches to new legislation.

关键词： App Store Mobile Ecosystem Digital Platform DMA Open App Markets Act

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Assessing Additive Manufacturing and Digital Inventory Ecosystem in the Oil & Gas Context 43rd

Assessing Additive Manufacturing and Digital Inventory Ecosy...

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43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Bang-Olsen, Kristian Turkyilmaz, Ali Univ Stavanger Sch Business & Law Stavanger Norway

ISBN: (纸本)9783031716355;9783031716331;9783031716324

The integration of additive manufacturing and digital inventories presents a paradigm shift in supply chain management, which promises reduced lead times, decreased complexity, lowered inventory storage costs, and improved sustainability. Despite the substantial benefits, the oil and gas (O&G) industry has been slow in adopting this technology. Drawing insights from companies already leveraging AM, this study investigates the critical functions in a DI ecosystem, as well as the challenges and actions required for the effective utilisation of DI within the O&G sector. Through qualitative analysis, the research identifies key factors influencing the adoption and implementation of DI in on-demand manufacturing and inventory management strategies. By understanding the challenges and opportunities associated withAMand DI integration, this study contributes to the discourse on innovative supply chain strategies and provides practical insights for DI ecosystem stakeholders.

关键词： Additive Manufacturing Digital Inventory Ecosystem Oil & Gas Norway

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