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Approaching Interoperability and Data-Related Processing Issues in a Human-Centric Industrial Scenario 1st

Approaching Interoperability and Data-Related Processing Iss...

引用

1st Global Internet of Things and Edge Computing Summit

作者： Syed, Danish Abbas Quadrini, Walter Choubeh, Nima Rahmani Pinzone, Marta Gusmeroli, Sergio Politecn Milan Dept Management Econ & Ind Engn Milan Italy

ISBN: (纸本)9783031785719;9783031785726

Industry 4.0 industrial automation paradigm and the related new Operator 4.0 role and pool of competencies are playing a critical role in bringing forth the Digital Transformation to manufacturing industry and SMEs in particular. The human-centric aspect of Industry 4.0 in combination with resilience, sustainability and circularity of manufacturing processes is gaining wider acceptance in Europe and across the globe while the transition towards Industry 5.0 starts to gain momentum as well as the integration of human centric solutions in Industry 4.0 automation systems. The current work uses a three-pronged approach to wearable sensors integrated with existing Industry 4.0 automation systems, by addressing sensor heterogeneity, data interoperability and network latency issues under the umbrella of a single unified and harmonised solution. Such a solution is realised in a realistic industrial scenario showcasing adaptive Human-Robot collaboration and leverages open-source software and open reference architectures.

关键词： Human in the loop Operator 5.0 5G IIoT NGSI-LD CPPS

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Towards Efficient Federated Learning via Vehicle Selection and Resource Optimization in IoV 5th

Towards Efficient Federated Learning via Vehicle Selection a...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Gong, Nan Yan, Guozhi Zhang, Hao Xiao, Ke Yang, Zuoxiu Li, Chuzhao Liu, Kai Chongqing Univ Coll Comp Sci Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China Chongqing Normal Univ Sch Comp & Informat Sci Chongqing Peoples R China Chongqing Univ Natl Elite Inst Engn Chongqing Peoples R China China Automot Engn Res Inst Co Ltd Chongqing Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Federated Learning (FL) is a novel distributed machine learning paradigm that holds great potential for collaborative model training in Internet of Vehicles (IoV). However, employing FL in IoV presents several challenges. First of all, the heterogeneous vehicular resources may lead to the bias of vehicle selection for model training and result in poor performance with respect to data diversity. Moreover, executing FL tasks with limited resources and energy is also a non-trivial problem. In view of this, we investigate an efficient FL framework in IoV, and then formulate the Vehicle Selection and Resource Optimization (VSRO) problem, which aims at minimizing the energy consumption and training latency without jeopardizing model accuracy. On this basis, we first propose a vehicle selection algorithm based on Lyapunov optimization method that dynamically selects vehicles to participate in each round of FL, taking into account the computational and transmission capabilities of vehicles, as well as their participation frequency in FL tasks. Furthermore, a resource scheduling algorithm is proposed for the selected vehicles, which utilizes the Lagrange dual problem and sub-gradient projection to iteratively determine the optimal training process. The simulation results demonstrate that the proposed algorithms can achieve best performance on enhancing model accuracy and simultaneously reducing system energy consumption and latency.

关键词： Internet of Vehicles Federated learning Efficient training Vehicle selection Resource optimization Lyapunov optimization

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Deep Learning-Based Network Intrusion Detection Systems: A Systematic Literature Review 5th

Deep Learning-Based Network Intrusion Detection Systems: A S...

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5th Southern African Conference for Artificial Intelligence Research

作者： Mutembei, Leonard L. Senekane, Makhamisa C. van Zyl, Terence Univ Johannesburg Acad Comp Sci & Software Engn Johannesburg South Africa Univ Johannesburg Inst Intelligent Syst Johannesburg South Africa Natl Inst Theoret & Computat Syst Stellenbosch South Africa

ISBN: (纸本)9783031782541;9783031782558

Machine Learning algorithms have been used to develop models in different fields like banking, healthcare, transportation, cybersecurity, and others. Many studies have shown the use of Machine Learning in network security by detecting network intrusions. However, the increase in user devices increases the data size and hence increases the complexity of the network. Cyber attackers continue to create advanced cyber attacks, and identifying them becomes extremely challenging. On the other hand, traditional Machine Learning models cannot efficiently handle large amounts of data and complexity. Therefore, this study examines how Deep Learning methods can be implemented for the Network Intrusion Detection Systems. The Network Intrusion Detection System (NIDS) helps to secure businesses within companies' networks from bad actors. As Deep Learning advances, network security experts must incorporate the techniques within the NIDS to minimize the effects of cyber attacks. For the investigation of the Deep Learning techniques in implementing NIDS, a study used Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) framework to conduct a systematic literature review and summarizes 111 studies published between 2021 and November 2023. Articles were analyzed by categorizing them into Deep Learning algorithms, architectures and datasets. The performance comparisons have been evaluated based on available articles' results on various datasets. Methods have been compared based on precision, recall and F1 score metrics. The results revealed that the most commonly used datasets are CICIDS2017, CSE-CIC-IDS2018, CICDDoS2019, NSL-KDD and UNSW-NB15. Furthermore, it was observed that hybrid implementation approaches have been shown to produce accurate and robust models compared with traditional methods. Finally, the results further demonstrated the need to explore using Machine Learning in NIDS.

关键词： Artificial Intelligence Deep Learning Machine Learning Network Intrusion Detection System PRISMA

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Integrating Knowledge and Data-Driven Artificial Intelligence for Decisional Enterprise Interoperability 5th

Integrating Knowledge and Data-Driven Artificial Intelligenc...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Emmanouilidis, Christos Waschull, Sabine Zotelli, Jessica Univ Groningen POB 800 NL-9700 AV Groningen Netherlands

ISBN: (纸本)9783031807749;9783031807756

Although data-driven artificial intelligence (AI) is increasingly applied in decision-making, challenges such as a lack of explainability and trust limit its integration in enterprise ***-making processes. Establishing a minimum level of common information sharing and trust among decision-making stakeholders, often termed as decisional interoperability, is needed for industry adoption. Purely data-driven approaches risk ignoring the enterprise environment and situational context of decisions and are insufficient for such interoperability to the extent that the decision-making rationale is opaque. While linked data and knowledge approaches have long been pursued in the context of data-driven machine learning, these have not been particularly well explored in the context of decisional enterprise interoperability. This paper aims to narrow this gap. It explores how the introduction of AI is changing decisional interoperability concerns. It then outlines patterns of human-AI teaming in decision-making, as well as methods, such as knowledge-infused AI and mechanisms, such as active learning, for enhancing decisional interoperability. Three diverse application domain-cases offer context for an analysis of AI decision-making considerations and decisional interoperability. The paper concludes with arguments about how integration of knowledge into data-driven AI contributes to decisional interoperability and further work needed in this area.

关键词： Decision-making Decisional interoperability Knowledge-infused artificial intelligence

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Fast Genomic Data Compression on Multicore Machines 12th

Fast Genomic Data Compression on Multicore Machines

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12th Conference on Cloud Computing, Big Data and Emerging Topics

作者： Sanz, Victoria Pousa, Adrian Naiouf, Marcelo De Giusti, Armando Natl Univ Plata Sch Comp Sci III LIDI La Plata Argentina CIC Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

ISBN: (纸本)9783031708060;9783031708077

Nowadays, Genomics has gained relevance since it allows preventing, diagnosing and treating diseases in a personalized way. The reduction in sequencing time and cost has increased the demand and, thus, the amount of genomic data that must be stored or transferred. Consequently, it becomes necessary to develop genome compression algorithms that help to reduce storage usage without consuming too much time. This is now possible thanks to modern multicore machines. This paper improves MtHRCM, a multi-threaded compression algorithm for large collections of genomes, by reducing its sequential component in order to enhance performance and scalability. Experimental results show that our optimized version is faster than MtHRCM and achieves the same compression ratio. Also, they reveal that this new version scales well when increasing the number of threads/cores for smaller test collections, while the high amount of simultaneous I/O requests to disk limits the scalability for larger test collections.

关键词： Genomic Data Compression Multi-threaded Hybrid Referential Compression Method Multicore Performance DNA

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Automatic Candling System with Artificial Intelligence Based on Neural Networks 4th

Automatic Candling System with Artificial Intelligence Based...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Patricio Fernandez, Angel Urdaneta, Maryory Albarracin, Wilmer Univ Tecnol Israel Quito 170522 Ecuador

ISBN: (纸本)9783031832093;9783031832109

Artificial vision systems use complex algorithms to identify specific characteristics in the objects of study, but by integrating artificial intelligence, the accuracy and simplicity of the processes increases. The project focuses on a scalable, versatile and lightweight prototype, resulting in an automatic candling system. Three main characteristics of chicken eggs, used in pastry and pastillage, were identified, classifying them as normal, cracked and with excess pores. In addition, a prototype was developed with a wooden structure that houses a Smartphone in charge of capturing images, LED panels for illumination and a special perforated cuvette. The captured images are transmitted to a laptop via WiFi and processed with Python algorithms using libraries such as Qt, OpenCV, Keras and TensorFlow. Two scripts manage the training of the neural network model and the system interface, which monitors, controls and stores data in databases managed by MariaDB and phpMyAdmin. With 900 images (720 for training and 180 for validation) and 2700 additional images analyzed, an accuracy of 93.93% has been achieved with a response time of 0.021954 s per egg. The system is suitable for candling of any oviparous egg.

关键词： CNN hyperparameter IP webcam Keras OpenCV ovoscopy Python TensorFlow

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Joint Angle Estimation for an Industrial Manipulator Robot via Convolutional Object Detection and K-means Clustering 4th

Joint Angle Estimation for an Industrial Manipulator Robot v...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Pablo Vasconez, Juan del Rio, Julio Moya, Viviana Pilco, Andrea Briseno, Inesmar Pantoja, Jenny Menendez, Oswaldo Univ Andres Bello Energy Transformat Ctr Fac Engn Santiago 7500971 Chile Univ Andres Bello Fac Engn Santiago 7500735 Chile Univ Internac Ecuador UIDE Fac Ciencias Tecn Quito 170411 Ecuador Univ Catolica Norte Dept Ingn Sistemas & Computac Antofagasta 1249004 Chile

ISBN: (纸本)9783031832093;9783031832109

Accurate estimation of joint angles in industrial manipulator robots can help to improve robotic applications in different scenarios. Depending on the robot's size and application, as well as the actuator type (it can be pneumatic) it can be expensive and hard to acquire an encoder to obtain feasible joint angle information. Moreover, visual joint angle estimation for manipulators can be used for robot automatic calibration, anomaly detection, and monitoring. In this work, we propose a joint angle estimation for an ABB industrial manipulator robot by using convolutional object detection methods (CNNs) and k-means clustering. In particular, we trained and evaluated the use of the You Only Look Once (YOLO) algorithm for the joint angle detection task. For this, we first build an image dataset of different joint configurations of the manipulator robot. Then, we train, calibrate, and test the YOLO object detection method. Then, we analyze several frames to obtain different detection measures and apply k-means clustering to improve the joint position estimation. Finally, we employ geometric analysis to estimate the joint angles considering a lateral perspective of the robot. We compare the estimation of the joint angles with the encoder measures of the robot, and we demonstrate that our method can reach low error results at different robot configurations. The proposed method can enhance joint angle estimation for different industrial applications.

关键词： Industrial manipulator robot Joint angle estimation Convolutional neural network Clustering YOLO

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Caregiver Acceptability of an LLM-Powered Assistant Interface to Improve Sleep Quality of the Elderly 1

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Ajovalasit, Marco Attori, Irene Caon, Massimo Salice, Fabio Zhou, Shengnan Comai, Sara Politecn Milan Dept Design Milan Italy Politen Milano Dept Elect Informat & Bioengn Milan Italy

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

While increased longevity and improved health at older ages are notable achievements of the 21st century, they pose significant challenges, particularly in informal care. This paper discusses NightCare Assistant, a humancentric system that uses nighttime data and Large Language Models (LLM) to translate sleep quality into practical suggestions for improving the daily routine of ageing individuals. These suggestions are displayed through a tablet application, allowing caregivers to interact with an AI assistant. The research aims to understand the acceptability of this solution, focusing on caregivers' reactions to different AI-generated outputs and their willingness to follow these suggestions. An online questionnaire tested the system's acceptability, examining caregivers' perceptions of text, image, and audio outputs generated by AI. Results indicate significant interest among caregivers in adopting technological solutions to ease caregiving responsibilities. Caregivers found audio feedback the most reliable and understandable, followed by text and image outputs. Caregivers' willingness to follow NightCare Assistant's advice supports the system's potential to improve care quality and safety for ageing individuals. The study highlights the necessity of addressing both nighttime behaviours and daily routines to effectively support cognitive health. NightCare Assistant's integration of nighttime monitoring data into actionable daily interventions provides a comprehensive strategy for enhancing the well-being of older adults and their caregivers. Its acceptance among caregivers suggests that similar technological interventions can support autonomous living and reduce caregiver burden.

关键词： Acceptability Human centred design Technology adoption Large language models Ageing Elderly care

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Infrastructure-Assisted Collective Perception Service with Emphasis on Vulnerable Road User Perception 12th

Infrastructure-Assisted Collective Perception Service with E...

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2023 International Conference on Smart Cities and Green ICT Systems and International Conference on Vehicle Technology and Intelligent Transport Systems

作者： Wolff, Vincent Albert Leibniz Univ Hannover Inst Commun Technol Hannover Germany

ISBN: (纸本)9783031709654;9783031709661

The safety of Vulnerable Road Users (VRUs) is a pivotal element in assistance-supported and autonomous driving systems, necessitating heightened awareness of pedestrians and cyclists among road users. While Intelligent Transport Systems (ITS) employ on-board sensors to identify VRUs, they are insufficient in complex traffic environments, i.e. urban city scenarios, where not all relevant entities are captured. To address this, the Collective Perception Service (CPS) offers supplementary Vehicle-to-Everything (V2X) communication for sharing information about these entities with nearby ITS stations (ITS-Ss). Additional deployment of Road Side Units (RSUs) can further enhance the performance in terms of environmental awareness. Our earlier research established that utilizing a relay and aggregation mechanism for Collective PerceptionMessages (CPMs) implemented in RSUs enhances ITS-Ss' perception of VRUs. However, it is essential to optimize the use of network resources given the limited bandwidth of the V2X channel. Unlike our prior work, which did not specify Road-Side Unit (RSU)-specific CPM rules and had a constant CPM dissemination rate, the current study incorporates the Value of information (VoI) to prioritize more relevant objects for surrounding ITS-Ss. Our simulation results according to ETSI standards show that this enhanced approach effectively reduces network channel load while maintaining high VRU awareness among vehicles.

关键词： Collective perception VRU awareness Infrastructure-assisted V2X Road side unit

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ReaL: Reassessing Softmax Score by Logits Distillation for Foundation Model Based Intent Classification 4th

ReaL: Reassessing Softmax Score by Logits Distillation for F...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Szucs, Gabor Al-Shouha, Modafar Budapest Univ Technol & Econ Dept Telecommun & Artificial Intelligence Muegyetem Rkp 3 H-1111 Budapest Hungary

ISBN: (纸本)9783031821493;9783031821509

With the extensive employment of large Foundation Models, the traditional classification concepts are challenged. In this work, we target a crucial task in natural language processing (NLP) and understanding (NLU);i.e. intent classification. We first debate the applicability of classical out-of-distribution definition and introduce a more realistic type;i.e. non-semantic OOD (NS-OOD). The results of our experiments asserted our claim and demonstrated that NS-OOD data poses a challenge for even powerful classifiers. Additionally, we support the criticism of relying only on the classifier's last layer logits to evaluate its correctness. Consequently, we propose a method (ReaL) to regain the trust of softmax score as a confidence indicator. This is performed by distilling the logits using the temperature parameter. We show that ReaL provides better separation between correct and wrong predictions' scores. To achieve a proper temperature estimation, we perform a mathematical derivation. Our approach calculates an adequate temperature value for a given model and a dataset (Our code is available at https://***/ modafarshouha/ReaL.).

关键词： intent classification out-of-distribution softmax score distillation foundation models non-semantic OOD

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