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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Florian Dehling Sebastian Koch Luigi Lo Iacono Hannes Federrath Institute of Computer Science University of Giessen Giessen Germany Security in Distributed Systems University of Hamburg Hamburg Germany

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Keystroke dynamics are behavioral biometric traits that are frequently proposed to be used in novel authentication systems. Keystroke dynamics are based on the analysis of intervals between keystroke events originating from user input and thus directly depend on available timing information. However, modern web browsers limit the accuracy of timestamps to improve security and privacy. This study systematically investigates the impact of limited timestamp accuracies on the performance of keystroke dynamic analysis. By conducting multiple experiments with popular web browsers, we demonstrate that the minor timestamp modifications that mitigate timing side-channel attacks do not interfere with the effectiveness of keystroke dynamics analysis algorithms. Furthermore, they are surprisingly resilient to larger timestamp modifications, which results in a serious threat to users’ privacy. This research provides fundamental knowledge enabling researchers, privacy engineers, and browser vendors to study risks in keystroke dynamics-related systems and to develop mitigations against tracking methods that fingerprint users instead of devices or browsers.

关键词： Privacy Accuracy Prevention and mitigation Keystroke dynamics Authentication Side-channel attacks Robustness Browsers Timing Security

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Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Aymen, Abid Khediri, Salim El Thaljaoui, Adel Miladi, Moahmed Kachouri, Abdennaceur Computer & amp Embedded Systems Lab National Engineering School of Sfax University of Sfax Sfax Tunisia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Science of Gafsa University of Gafsa Gafsa Tunisia Preparatory Institute for Engineering Studies of Gafsa Gafsa Tunisia Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Advanced Fluid Dynamics Energetics and Environment Lab National Engineering School of Sfax University of Sfax Sfax Tunisia

Electroencephalogram (EEG) data are susceptible to artifacts, such as lapses in concentration or poor imagination, which can significantly impact the accuracy of disease diagnosis in e-health applications. To mitigate this issue, the use of machine learning (ML) and potentially artificial intelligence (AI) solutions to accurately identify outliers becomes crucial. Unlike many AI methods that incorporate unnecessary or redundant input variables, our study focuses on detecting anomalous values in EEG data through the K nearest neighbor (KNN) process and Euclidean distance metric. Our proposed unsupervised non-parametric algorithm, known as the smart KNN outlier detector (SKOD), eliminates the need for initial parameter configurations such as the number of neighbors (K), while achieving high performance. Evaluation of SKOD using real EEG data from 140 trials demonstrated sensitivity and specificity exceeding 60%, with nearly perfect accuracy in detecting outliers reaching close to 100%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Data analysis EEG signal Healthcare Knowledge-based systems Machine learning Outlier detection

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FPGARLab: Leveraging FPGA Design and Verification Using Intelligent Cyber Physical System Based Collaborative Remote Laboratory

FPGARLab: Leveraging FPGA Design and Verification Using Inte...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Rashed Al Amin Veit Wiese Mohammad Musfiqur Rahman Sven Jacobs Steffen Jaschke Roman Obermaisser Institute for Embedded Systems University of Siegen Siegen Germany Computer Science Education University of Siegen Siegen Germany

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The Field-Programmable Gate Arrays (FPGAs) computing architecture allows users to program and reconfigure hardware, providing flexibility and adaptability. FPGAs have garnered considerable attention in both academic research and industrial contexts due to their potential applications. Teaching hardware design, requiring direct access to specific hardware platform for conclusive testing, faces challenges with FPGAs, which are not easily shared among multiple users. The rising use of remote laboratory systems has addressed this constraint, allowing access to various equipment and devices from different geographic locations, has witnessed an upsurge in engineering education and industrial research. The traditional laboratory approach has transformed with the integration of computers, electronic devices, and the internet, giving rise to a novel paradigm in laboratory practice known as the remote laboratory system. This system allows users to access lab equipment at their convenience, fostering self-paced learning. This paper demonstrates an intelligent cyber physical system based platform for remote FPGA kit access, facilitating collaborative task-solving through a web interface. Tailored for FPGAs, the system autonomously manages logic design, synthesis, and simulation. Acting as a gateway, it provides users fully control over FPGA development boards, including peripherals with real-time feedback via video streaming and live chat. This proposed intelligent cyber-physical system also possesses the capability to oversee multiple and diverse FPGA development boards through smart light control and power-saving mode, thereby augmenting the system's overall efficiency. This distinctive design offers the advantage of conducting student laboratory exercises or industrial training where FPGA hardware must be accessed by numerous users simultaneously.

关键词： Remote laboratories Service robots Collaboration Streaming media Hardware Real-time systems Task analysis

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Towards Personality Detection and Prediction using Smartphone Sensor Data

Towards Personality Detection and Prediction using Smartphon...

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Communication and computer Networking Conference (MedComNet), Mediterranean

作者： Dimitris Xenakis Eric Samikwa Jesutofunmi Ajayi Antonio Di Maio Torsten Braun Katja Schlegel Communication and Distributed Systems Institute of Computer Science University of Bern Institute of Psychology University of Bern

The emergence and adoption of mobile smartphones and their ubiquitous use by mobile phone users provide a distinctive opportunity for the collection of multi-modal sensor data. The data collected from such smartphone devices can be used to observe, recognize and predict patterns in user behaviors such as physical activity, psycho-physiological conditions such as affective state, personality traits such as extraversion, emotionality, and honesty-humility, as well as other mental health-related or psychological variables. In this work, we design and develop a system that can be used for the unobtrusive collection and storage of anonymized smartphone sensor data for personality detection and prediction. Our proposed system is developed across the two main smartphone operating systems to cover a wide range of users and to consider the available sensors across platform-dependent devices, as well as to account for distinctions in the personality traits of users of different operating systems. We present a correlation analysis and describe how the dataset collected from the smartphone sensors, which were deployed in a large-scale psychology study with about 200 participants, can be used to detect and predict different personality states.

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Privacy framework for context-aware robot development

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Paladyn 2021年第1期12卷 468-480页

作者： Heuer, Tanja Schiering, Ina Gerndt, Reinhard Department of Computer Science Ostfalia University of Applied Sciences Institute of Information Engineering Wolfenbüttel Germany Department of Computer Science Ostfalia University of Applied Sciences Institute of Distributed Systems Wolfenbüttel Germany

Privacy is an essential topic in (social) robotics and becomes even more important when considering interactive and autonomous robots within the domestic environment. Robots will collect a lot of personal and sensitive information about the users and their environment. Thereby, privacy does consider the topic of (cyber-)security and the protection of information against misuse by involved service providers. So far, the main focus relies on theoretical concepts to propose privacy principles for robots. This article provides a privacy framework as a feasible approach to consider security and privacy issues as a basis. Thereby, the proposed privacy framework is put in the context of a user-centered design approach to highlight the correlation between the design process steps and the steps of the privacy framework. Furthermore, this article introduces feasible privacy methodologies for privacy-enhancing development to simplify the risk assessment and meet the privacy principles. Even though user participation plays an essential role in robot development, this is not the focus of this article. Even though user participation plays an essential role in robot development, this is not the focus of this article. The employed privacy methodologies are showcased in a use case of a robot as an interaction partner contrasting two different use case scenarios to encourage the importance of context awareness. © 2021 Tanja Heuer et al., published by De Gruyter.

关键词： Machine design

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Fuzzy Querying in the Cloud-based Environment for Data Stream-driven Predictive Maintenance in AGV-enabled Smart Factories

Fuzzy Querying in the Cloud-based Environment for Data Strea...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Romanow, Dominik Grzesik, Piotr Benecki, Pawel Niyomugaba, Alexandre Habimana, Theodore Kostrzewa, Daniel Tokarz, Krzysztof Hung, Che-Lun Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Ines Ruhengeri - Institute of Applied Sciences Department of Computer Science Rwanda Silesian University of Technology Department of Applied Informatics Gliwice Poland Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei Taiwan

ISBN: (纸本)9798350362480

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models. © 2024 IEEE.

关键词： Data streams

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Blockchain Framework for Automated Life Insurance 5th

Blockchain Framework for Automated Life Insurance

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Kalsgonda, Vaishali Kulkarni, Raja Kalasgonda, Shivkumar Department of Computer Science Shivraj College Maharashtra Gadhinglaj416502 India Department of Computer Studies Chhatrapati Shahu Institute of Business Education Research University Road Maharashtra Kolhapur416004 India E-Commerce Distributed Systems Seattle United States

ISBN: (纸本)9789819906000

The insurance process is done manually in India. The insurance client has to depend on an insurance agent from buying to the claim firing process which leads to wrong entry, fraudulent claims, and cost overhead on the agent’s commission. An insurance client has to maintain documents and make them available at the time of claim firing, which is very cumbersome. Blockchain smart contracts will provide a secure, automatic, cost-cutting, paperless, and real-time insurance process. This paper aims to provide a blockchain solution for life insurance claim processing, as it is the most purchased and needy insurance. An architectural illustration of a blockchain prototype is contributed to this paper. As a result, the authors found that using Hyperledger Composer, we can develop fine-grained insurance applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Online Reduction of Exploration Space for Automated Underwater Modem Optimization

Online Reduction of Exploration Space for Automated Underwat...

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MILCOM, Military Communications Conference

作者： Marcel Rieß Steffen Moser Frank Slomka Institute of Embedded Systems/Real-Time Systems Faculty of Engineering Computer Science and Psychology Ulm University Germany

Developing efficient underwater communication systems is complex, as multiple factors impact communication quality. While rapid prototyping has been a common approach, it carries a high risk of failure. To address this challenge, model-based development and implementation have gained popularity. However, selecting the optimal combination of modulation type, correction properties, synchronization properties, and other parameters remains time-consuming and intricate. This paper presents an innovative approach to streamline the development process by reducing the exploration space. The proposed method evaluates potential solutions based on scenario-specific, application-specific, and performance-related upper and lower bounds for the selectable communication system. These bounds consider common underwater challenges, such as SNR degradation and multiple bounce loss, and distinguish between dynamic and static system analyses. By adopting this approach, existing analysis and development workflow achieve more optimized communication systems and afford more efficient variant selection using narrowing down the solution space and considering the specific requirements of each scenario. The approach’s effectiveness is demonstrated through case studies and evaluations, highlighting the benefits of reducing the exploration space and utilizing scenario-, application-, and performance-based bounds. This enables the development of robust and tailored underwater communication systems, improving communication quality and overall system performance.

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Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments 43

Enhancing Drone Surveillance with NeRF: Real-World Applicati...

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Lindén, Joakim Burresi, Giovanni Forsberg, Håkan Daneshtalab, Masoud Söderquis, Ingemar School of Innovation Design and Engineering Mälardalen University Division of Intelligent Future Technologies Västerås Sweden Saab Aeronautics Stockholm Sweden School of Electrical Engineering and Computer Science Royal Institute of Technology Division of Electronics and Embedded Systems Stockholm Sweden

ISBN: (纸本)9798350349610

Machine Learning (ML) systems require representative and diverse datasets to accurately learn the objective task. In supervised learning data needs to be accurately annotated, which is an expensive and error-prone process. We present a method for generating synthetic data tailored to the use-case achieving excellent performance in a real-world usecase. We provide a method for producing automatically annotated synthetic visual data of multi rotor unmanned aerial vehicles (UAV) and other airborne objects in a simulated environment with a high degree of scene diversity, from collection of 3D models to generation of annotated synthetic datasets (synthsets). In our data generation framework SynRender we introduce a novel method of using Neural Radiance Field (NeRF) methods to capture photo-realistic high-fidelity 3D-models of multirotor UAVs in order to automate data generation for an object detection task in diverse environments. By producing data tailored to the real-world setting, our NeRF-derived results show an advantage over generic 3D asset collection-based methods where the domain gap between the simulated and real-world is unacceptably large. In the spirit of keeping research open and accessible to the research community we release our dataset VISER DroneDiversity used in this project, where visual images, annotated boxes, instance segmentation and depth maps are all generated for each image sample. © 2024 IEEE.

关键词： Drones

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A New Approach for the Analysis of Resistance to Change in the Digital Transformation Context 11th

A New Approach for the Analysis of Resistance to Change in t...

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10th International Conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th International Conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： El Karam, Bachira Abou Marghoubi, Rabia Khanboubi, Fadoua Allaki, Driss InnovatiVE REsearch on Software Systems and data INPT Rabat Morocco LIM Lab. IOS team Department of Computer Science FSTM Casablanca Morocco Smart Embedded Enterprise and Distributed Systems INPT Rabat Morocco

ISBN: (纸本)9783031473654

In a context marked by the rapid evolution of technologies and the omnipresence of IT at the heart of all activities, the digital transformation of companies is no longer a luxury but an absolute necessity. This technological transformation can lead to resistance from the employees affected by the change. In order to properly conduct the change management process, it is necessary to ensure the involvement of employees from the beginning of this process, which is composed of three phases: the change management strategy, the diagnosis, and the actions implementation. This paper focuses on the diagnostic phase and more specifically on the evaluation stage. This evaluation measures the employees’ hesitation to express their degree of resistance toward the digital initiatives launched in the company. However, this assessment is usually done subjectively, and employees express their hesitation in non-uniform manners. In this work, we propose a weighted generalized hesitating fuzzy approach based on the fusion of theoretical concepts of generalized hesitant fuzzy sets and formal concept analysis (FCA), to formalize the measurement of degree hesitation in change resistance. This approach is flexible and allows the top management of each organizational unit to correctly measure the resistance to change of their teams and then evaluate that measure at the global organizational level. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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