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Privacy framework for context-aware robot development

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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Analyzing the Effect of Taproot on Bitcoin Deanonymization

Analyzing the Effect of Taproot on Bitcoin Deanonymization

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International Conference on distributed Computing systems Workshop

作者： Felix Kleinwort Wolf Posdorfer Janick Edinger Department of Computer Science Distributed Operating Systems University of Hamburg Hamburg Germany Department of Computer Science Computer Networks University of Hamburg Hamburg Germany

Bitcoin was released in 2008 as an electronic peer-to-peer payment system. The aim was to enable financial transactions in an anonymous manner between participating parties. However, Bitcoin is considered a pseudonymous network rather than anonymous, as the identity of the address owner is unknown, but every transaction is permanently stored on the Bitcoin blockchain and can be tracked by anyone. The structure of the blockchain, its transactions, and the Bitcoin network make it possible to deanonymize pseudonyms through various methods such as flow analysis, heuristics, and network traffic observations. Once a connection is established between pseudonyms used in the Bitcoin network and the real world, all previous transactions can be attributed to that identity. In 2021, Taproot was introduced to further increase privacy within the Bitcoin Network by introducing a new address format which will allow transactions to be more indistinguishable from one another. In this paper, we analyze current methods for deanonymizing Bitcoin transactions to understand which parts of the Bitcoin protocol they exploit. In addition, we look at the changes introduced by Taproot and determine the extent to which these changes affect the methods and what assumptions must be made for these methods to remain applicable.

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Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction

arXiv

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arXiv 2025年

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Distributed Systems and Informatic Devices Silesian University of Technology Poland

In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm with heterogeneous transfers. We design dense and sparse feature tensors to deal with the sparsity of data sources. Heterogeneous federated learning is developed to share asynchronous parts of networks and select appropriate models for knowledge transfer. Experimental results show that the proposed HFL achieves the lowest prediction error among all benchmark systems on eight out of ten prediction tasks, with MSE reduction of 94.8%, 48.3%, and 52.1% compared to the benchmark systems. These results demonstrate the effectiveness of HFL in transferring knowledge from heterogeneous domains, especially in the smaller target domain. Ablation studies then demonstrate the effectiveness of the designed mechanisms for heterogeneous domain selection and switching in predicting healthcare time series with privacy, model security, and heterogeneous knowledge transfer. © 2025, CC BY-NC-SA.

关键词： Federated learning

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Direct and Inverse Optimization Problems in a Gas Deposit Model 15

Direct and Inverse Optimization Problems in a Gas Deposit Mo...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Skiba, Alexander Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Design Methods for Developing Systems Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

A continuous dynamic model for the development of a gas field is studied. Two mathematical problems are posed, solved and analyzed. In the direct problem, we are looking for the maximum accumulated profit on a set of temporary functions of a certain type. In the inverse optimization problem, we define the entire set of planning horizons. We subject the assigned tasks to a comprehensive analysis. © 2022 IEEE.

关键词： Profitability

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Joint Learning: A Pattern for Reliable and Efficient Decision-Making in Self-Adaptive Internet of Things 23

Joint Learning: A Pattern for Reliable and Efficient Decisio...

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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Michiel Provoost Danny Weyns Dimitri Van Landuyt Sam Michiels Tomáš Bureš Department of Computer Science Katholieke Universiteit Leuven Belgium Department of Computer Science Katholieke Universiteit Leuven Belgium and Department of Computer Science and Media Technology Linnaeus University Sweden Department of Distributed and Dependable Systems Charles University Czech Republic

ISBN: (纸本)9798400700408

An Internet-of-Things (IoT) system typically comprises many small computing elements (nodes) that are battery-powered and communicate over a wireless network. These elements monitor properties in the environment and send the data to client applications via gateways. The wireless networks used by the elements are subject to uncertainties that are difficult to predict upfront, such as dynamic objects (swaying trees, cars,...) and changing weather conditions that may deteriorate the transmissions. To ensure reliable communication over a wireless network of energy-constrained elements, recent research has proposed self-adaptive IoT systems. Such a self-adaptive system equips the network of elements – referred to as the managed system – with a feedback loop – the managing system. The managing system monitors the changing conditions and adapts the transmission settings of the IoT network to ensure the system’s quality goals. Leveraging and consolidating the existing knowledge in this area, we present a pattern that we coined Joint Learning that provides a solution to the decision-making problem of large, distributed self-adaptive IoT systems. With this pattern, elements use a joint learner to make adaptation decisions for individual elements while yielding reliable communication of the overall network. The pattern is applied to two cases to show that the solutions realize the system goals while operating under uncertainties.

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia computer science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Effective Prediction of Energy Consumption in Automated Guided Vehicles with Recurrent and Convolutional Neural Networks

Effective Prediction of Energy Consumption in Automated Guid...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Benecki, Pawel Kostrzewa, Daniel Grzesik, Piotr Shubyn, Bohdan Syu, Jia-Hao Lin, Jerry Chun-Wei Sunderam, Vaidy Mrozek, Dariusz Silesian University of Technology Department of Applied Informatics Gliwice Poland Lviv Polytechnic National University Department of Telecommunications Lviv Ukraine National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Emory University Department of Computer Science AtlantaGA30322 United States

ISBN: (纸本)9798350324457

Detection and prediction of failures in Automated Guided Vehicles (AGV) are essential for the uninterrupted operation of production plants. Anomaly detection is usually achieved by comparing expected measurement values with actual observations. Thus, it is crucial to predict telemetry signals properly. In this paper, we research the prediction of energy consumption using state-of-the-art Artificial Neural Networks architectures (SCINet) compared with other Recurrent Neural Network (RNN) approaches on the data streams acquired from CoBotAGV. We especially focus on the possibility of applying feature weighting. We show that it can improve prediction capabilities. We also investigate resource utilization in terms of time to fit the embedded AGV environment. © 2023 IEEE.

关键词： anomaly detection automated guided vehicles feature weighting Industry 4.0 time series prediction

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WIP: An Engaging Undergraduate Intro to Model Checking in Software Engineering Using TLA+

WIP: An Engaging Undergraduate Intro to Model Checking in So...

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Frontiers in Education (FIE) Conference

作者： Konstantin Läufer Gunda Mertin George K. Thiruvathukal Department of Computer Science Software and Systems Laboratory Loyola University Chicago Institute for Software Engineering and Programming Languages University of Lübeck

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions $(\text{TLA}^{+})$ , into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one's undergraduate studies, (3) demonstrate how to address successive “failuresy” through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding $\text { TLA }^{+}$ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in $\text { TLA }^{+}$ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudi

关键词： computer science Open Access Model checking System recovery Software Safety Logic Reliability Proposals Software engineering

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Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Pawlowicz, Paulina Sunderam, Vaidy Hung, Che-Lun Kwiecien, Andrzej Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Silesian University of Technology Department of Applied Informatics Gliwice Poland Emory University Department of Computer Science AtlantaGA United States National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei City Taiwan

ISBN: (纸本)9798350362480

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. Processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data. © 2024 IEEE.

关键词： Information granules

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A Study of Two Types of Introductory Exercise Problems for Network programming Learning with Python 23

A Study of Two Types of Introductory Exercise Problems for N...

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11th International Conference on computer and Communications Management, ICCCM 2023

作者： Lu, Xiqin Funabiki, Nobuo Ueda, Kiyoshi Kao, Wen-Chung Department of Information and Communication Systems Distributed System Design Lab. Okayama University Okayama Japan Department of Computer Science Control Software Lab. Nihon University Koriyama Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9798400707735

Network programming is the act of writing computer codes for communications between programs or processes in different computers across networks. It is crucial for applications and services using networks, including e-commerce, gaming, and social media. However, university courses of educating it are very limited. As a result, it has become important to develop and offer IT tools to support self-studies of network programming by novices. In this paper, we present a study of two types of introductory exercise problems, namely, Grammar-concept Understanding Problem (GUP) and Comment Insertion Problem (CIP), for self-studies of five basic network protocols of TCP/IP, ARP, SMTP, POP3, and SSH with Python. A GUP instance requests to answer the keyword such as a library function in the given source code that corresponds to the grammar concept described in the question. A CIP instance requests to insert the comment at each blank of the code. String matching is adopted to mark any answer at the browser-based answer interface. Through solving them, it is expected to understand the meaning of important words and the code structure for network programming. As preliminary evaluations, we generated six GUP and CIP instances respectively, and assigned them to 10 students in Okayama University, Japan. The results confirmed that the proposal is proper for novices to learn network programming with Python. © 2023 ACM.

关键词： Python

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