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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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Task-Adapted Single-Finger Explorations of Complex Objects 14th

Task-Adapted Single-Finger Explorations of Complex Objects

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14th International Conference on Human Haptic Sensing and Touch Enabled computer Applications, EuroHaptics 2024

作者： Lin, Lisa Pui Yee Böhm, Alina Belousov, Boris Kshirsagar, Alap Schneider, Tim Peters, Jan Doerschner, Katja Drewing, Knut Department of General Psychology Justus-Liebig University Gießen Gießen Germany Intelligent Autonomous Systems Group Computer Science Department TU Darmstadt Darmstadt Germany Darmstadt Germany Hessian Centre for Artificial Intelligence Darmstadt Germany

ISBN: (纸本)9783031700576

The perception of material/object properties plays a fundamental role in our daily lives. Previous research has shown that individuals use distinct and consistent patterns of hand movements, known as exploratory procedures (EPs), to extract perceptual information relevant to specific material/object properties. Here, we investigated the variation in EP usage across different tasks involving objects that varied in task-relevant properties (shape or deformability) as well as in task-irrelevant properties (deformability or texture). Participants explored 1 reference object and 2 test objects with a single finger before selecting the test object that was most similar to the reference. We recorded their finger movements during explorations, and these movements were then categorised into different EPs. Our results show strong task-dependent usage of EPs, even when exploration was confined to a single finger. Furthermore, within a given task, EPs varied as a function of material/object properties unrelated to the primary task. These variations suggest that individuals flexibly adapt their exploration strategies to obtain consistent and relevant information. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： active touch Exploratory procedures haptic perception

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Sensor-Based PUF: A Lightweight Random Number Generator for Resource Constrained IoT Devices 5th

Sensor-Based PUF: A Lightweight Random Number Generator for...

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5th IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2022

作者： Hillerström, Maaike Ullah, Ikram Havinga, Paul J. M. University of Twente Enschede Netherlands Pervasive Systems Group Department of Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9783031188718

Internet of Things (IoT) prevalence is surging swiftly over the past years, and by 2050, the number of IoT devices are expected to exceed 50 billion. IoT has been deployed in many application domains such as smart health, smart logistics and smart manufacturing. IoT has significantly improved quality of our day-to-day life. However, IoT faces multiple challenges due to its lack of adequate computational and storage capabilities and consequently it is very strenuous to implement sophisticated cryptographic mechanisms for security, trust and privacy. The number of IoT devices are increasing drastically which potentially leads to additional challenges namely transparency, scalability and central point of failure. Furthermore, the growing number of IoT applications induces the need of decentralized and resource constrained mechanisms. Therefore, in this paper, we propose a decentralized Random Number Generator (RNG) based on sensor Physical Unclonable Functions (PUF) in smart logistics scenario. PUF is a secure and lightweight source of randomness and hence suitable for constrained devices. Data is collected from various sensors and processed to extract cryptographically secure seed. NIST tests are performed to appraise the aptness of the proposed mechanism. Moreover, the seed is fed into an Elliptic Curve Cryptographic (ECC) mechanism to generate pseudo-random numbers and keys which can potentially be used for authentication, encryption and decryption purposes. © 2022, IFIP International Federation for Information Processing.

关键词： Internet of things

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The Jade Gateway to Exergaming: How Socio-Cultural Factors Shape Exergaming Among East Asian Older Adults

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Proceedings of the ACM on Human-computer Interaction 2024年第CHI PLAY期8卷 1-34页

作者： Hadi Mogavi, Reza Son, Juhyung Yang, Simin Wang, Derrick M. Choong, Lydia Alhilal, Ahmad Zhou, Peng Yuan Hui, Pan Nacke, Lennart E. HCI Games Group Stratford School of Interaction Design and Business University of Waterloo WaterlooON Canada Guangzhou China Hong Kong University of Science and Technology Hong Kong Aarhus University Aarhus Denmark The Hong Kong University of Science and Technology University of Helsinki Hong Kong The Department of Systems Design Engineering University of Waterloo Canada The Cheriton School of Computer Science University of Waterloo Canada

Exergaming, blending exercise and gaming, improves the physical and mental health of older adults. We currently do not fully know the factors that drive older adults to either engage in or abstain from exergaming. Large-scale studies investigating this are still scarce, particularly those studying East Asian older adults. To address this, we interviewed 64 older adults from China, Japan, and South Korea about their attitudes toward exergames. Most participants viewed exergames with a positive inquisitiveness. However, socio-cultural factors can obstruct this curiosity. Our study shows that perceptions of aging, lifestyle, the presence of support networks, and the cultural relevance of game mechanics are the crucial factors influencing their exergame engagement. Thus, we stress the value of socio-cultural sensitivity in game design and urge the HCI community to adopt more diverse design practices. We provide several design suggestions for creating more culturally approachable exergames. © 2024 Owner/Author.

关键词： Game design

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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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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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NORMATIVE FEELING: SOCIALLY PATTERNED AFFECTIVE MECHANISMS

arXiv

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arXiv 2024年

作者： Anagnou, Stavros Polani, Daniel Salge, Christoph Adaptive Systems Research Group Department of Computer Science University of Hertfordshire United Kingdom

Norms and the normative processes that enforce them such as social maintenance are considered fundamental building blocks of human societies, shaping many aspects of our cognition. However, emerging work argues that the building blocks of normativity emerged much earlier in evolution than previously considered. In light of this, we argue that normative processes must be taken into account to consider the evolution of even ancient processes such as affect. We show through an agent-based model (with an evolvable model of affect) that different affective dispositions emerge when taking into account social maintenance. In this paper we demonstrate that social maintenance results in the emergence of a minimal population regulation mechanism in a dynamic environment, without the need to predict the state of the environment or reason about the mental state of others. We use a cultural interpretation of our model to derive a new definition of norm emergence which distinguishes between indirect and direct social maintenance. Indirect social maintenance tends to one equilibrium (similar to environmental scaffolding) and the richer direct social maintenance results in many possible equilibria in behaviour, capturing an important aspect of normative behaviour in that it bears a certain degree of arbitrariness. We also distinguish between single-variable and mechanistic normative regularities. A mechanistic regularity rather than a particular behaviour specified by one value e.g. walking speed, its a collection of values that specify a culturally patterned version of a psychological mechanism e.g. a disposition. This is how culture reprograms entire cognitive and physiological systems. © 2024, CC BY.

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A Novel Machine Learning-Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

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computer systems science & Engineering 2023年第8期46卷 2123-2140页

作者： Saurabh Adhikari Tushar Kanti Gangopadhayay Souvik Pal D.Akila Mamoona Humayun Majed Alfayad N.Z.Jhanjhi School of Engineering Swami Vivekananda UniversityIndia Department of Computer Science and Engineering Sister Nivedita University(Techno India Group)KolkataWest BengalIndia Sambalpur University SambalpurIndia Department of Computer Applications Saveetha College of Liberal Arts and SciencesSIMATS Deemed to be UniversityChennaiIndia Department of Information Systems College of Computer and Information SciencesJouf UniversityKSA College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia School of Computer Science(SCS) Taylor’s UniversitySubang Jaya47500Malaysia

Machine learning is a technique for analyzing data that aids the construction of mathematical *** of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction *** type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect *** rise of touch-free applications and the number of deaf people have increased the significance of hand gesture *** applications of hand gesture recognition research span from online gaming to surgical *** location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in *** gestures may be difficult to distinguish from nonsensical motions in the field of gesture *** gestures may be difficult to distinguish from nonsensical motions in the field of gesture *** this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or *** a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered *** have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture *** findings from the simulation support the accuracy,precision,gesture

关键词： Machine learning gesture recognition framework accuracy rate precision rate gesture recognition rate sensitivity rate efficiency rate

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Invertibility of Discrete-Time Linear systems with Sparse Inputs 63

Invertibility of Discrete-Time Linear Systems with Sparse In...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Poe, Kyle Mallada, Enrique Vidal, Rene University of Pennsylvania Applied Mathematics and Computational Science Group PA19104 United States Johns Hopkins University Mallada Is with the Department of Electrical and Computer Engineering MD21218 United States University of Pennsylvania Radiology Computer and Information Science Statistics and Data Science Departments of Electrical and Systems Engineering PA19104 United States

ISBN: (纸本)9798350316339

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024 IEEE.

关键词： State space methods

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