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Workshop on Robotics Software engineering (RoSE)

作者： Marco Stadler Stefan Biffl Michael Vierhauser Johannes Sametinger LIT SCSL / Inst. of Business Informatics - Software Eng. Johannes Kepler University Linz Austria CDL-SQI Institute of Information Systems Engineering TU Wien and CDP Vienna Austria Department of Computer Science University of Innsbruck Austria

ISBN: (数字)9798331537951

ISBN: (纸本)9798331537968

Problems and failures that emerge in Cyber-Physical Systems (CPSs), particularly in robotic applications, may originate from various sources, including software bugs, security incidents, hardware malfunctions, and human errors. As robotic systems are deployed in various domains and application contexts, such as manufacturing sites, shop floors, agriculture, and autonomous vehicles, ensuring their safe and secure operation is a crucial aspect. While high-fidelity simulations are frequently used to validate system behavior and to perform tests, the “simulator-to-reality gap” presents significant challenges, requiring additional field testing to validate a system under realistic conditions. As simulations alone are insufficient for performing comprehensive testing and for ensuring adherence to both functional and non-functional requirements, real-world field testing helps to alleviate these issues. However, compared to wellestablished unit testing approaches, field testing typically is still a rather ad hoc process, with insufficient support from tools and frameworks. Field tests often heavily rely on human observations, hence risking overlooking critical issues. There is a pressing need for structured, guided field-testing processes combined with adaptive runtime monitoring to capture the data required for effective error diagnosis and analysis. This paper introduces initial concepts for the Smart Unified Runtime Monitoring Infrastructure for Guided Field-Testing (SMURF) framework designed for robotic applications, combining structured test execution with automated, adaptable monitors, to ensure the efficient collection of data required for post-test analysis. Building on prior efforts in drone field-testing frameworks, we extend our scope to identify essential features for testing and monitoring ROS-based systems. Future work shall further refine this process and implement a practical framework to support developers and testers in achieving reliable, safe, and secure

关键词： Runtime Pressing Software Safety Manufacturing Security Reliability Robots Monitoring Testing

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Research Trends, Challenges, and Future Directions in Vehicular Networks

Research Trends, Challenges, and Future Directions in Vehicu...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Islam Tharwat Abdel Halim Hossam M.A. Fahmy Center for Informatics Science (CIS) School of Information Technology and Computer Science (ITCS) Nile University (NU) Giza Egypt Department of Computers and Systems Engineering Ain Shams University Cairo Egypt

nowadays, vehicular networks constitute the core of the Intelligent Transportation Systems (ITSs) with the main objective is to improve the road safety by taking advantage of the emerging communication technology and the lower cost of wireless devices. Indeed, thanks to sensors installed in vehicles or at the edge of roads and control centers. Vehicle communications will enable drivers to be warned early enough, in case of possible dangers. In addition, these networks are no longer content to improve road safety only, but they also will offer new services to road users making the road more pleasant. The major objective of this article is to provide a brief review of vehicular networks evolution, architecture, characteristics, standards, constraints, and challenges that face designers in the design of protocols dedicated to this type of networks. Also, this paper provides perspectives for future research and developments.

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Model-Based Rate-Distortion Optimized Video-Based Point Cloud Compression with Differential Evolution 11th

Model-Based Rate-Distortion Optimized Video-Based Point Clou...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Yuan, Hui Hamzaoui, Raouf Neri, Ferrante Yang, Shengxiang School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom School of Control Science and Engineering Shandong University Jinan China School of Computer Science University of Nottingham Nottingham United Kingdom School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9783030873547

The Moving Picture Experts Group (MPEG) video-based point cloud compression (V-PCC) standard encodes a dynamic point cloud by first converting it into one geometry video and one color video and then using a video coder to compress the two video sequences. We first propose analytical models for the distortion and bitrate of the V-PCC reference software, where the models’ variables are the quantization step sizes used in the encoding of the geometry and color videos. Unlike previous work, our analytical models are functions of the quantization step sizes of all frames in a group of frames. Then, we use our models and an implementation of the differential evolution algorithm to efficiently minimize the distortion subject to a constraint on the bitrate. Experimental results on six dynamic point clouds show that, compared to the state-of-the-art, our method achieves an encoding with a smaller error to the target bitrate (4.65% vs. 11.94% on average) and a slightly lower rate-distortion performance (on average, the increase in Bjøntegaard delta (BD) distortion is 0.27, and the increase in BD rate is 8.40%). © 2021, Springer Nature Switzerland AG.

关键词： Encoding (symbols)

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Tensorized Incomplete Multi-View Kernel Subspace Clustering

SSRN

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SSRN 2023年

作者： Zhang, Guang-Yu Huang, Dong Wang, Chang-Dong College of Mathematics and Informatics South China Agricultural University China School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Information Security Technology China

Recently considerable advances have been achieved in the incomplete multi-view clustering (IMC) research. However, the current IMC works are still faced with three challenging issues. First, they mostly lack the ability to recover the nonlinear subspace structure hidden in the multiple kernel spaces. Second, they usually neglect the high-order correlation contained in heterogeneous views. Third, many of them have three or even more hyper-parameters and may not be practical for some realistic applications. To tackle these issues, we present a new subspace clustering approach for incomplete multi-view data, namely Tensorized Incomplete Multiview Kernel subspace Clustering (TIMKC). By incorporating the kernel learning technique into an incomplete subspace clustering framework, our approach can robustly explore the latent subspace structure hidden in multiple views. Furthermore, the proposed approach imputes the incomplete kernel matrices and learns the low-rank tensor representations in a mutual enhancement manner. Notably, our approach can discover the underlying relationship among observed samples and missing samples, while capturing the high-order complementary information for robust subspace clustering. For solving the optimization model, we design a three-step algorithm to efficiently minimize the unified objective function, which only has one hyper-parameter to be adjusted. Experiments on various incomplete multi-view datasets demonstrate the superiority of our TIMKC approach. © 2023, The Authors. All rights reserved.

关键词： Tensors

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Experimental study of neuromorphic node based on a multiwaveband emitting two-section quantum dot laser

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Photonics Research 2021年第4期9卷 I0007-I0015页

作者： George Sarantoglou Menelaos Skontranis Adonis Bogris Charis Mesaritakis Department of Information and Communication Systems Engineering University of the AegeanPalama 2Karlovassi 83200 SamosGreece Department of Informatics and Computer Engineering University of West AtticaAghiou Spiridonos12243 EgaleoAthensGreece

In this work,we present experimental results concerning excitability in a multiband emitting quantum-dot-based photonic *** experimental investigation revealed that the same two-section quantum dot laser can be tuned through a simple bias adjustment to operate either as a leaky integrate and fire or as a resonate and fire ***,by exploiting the inherent multiband emission of quantum-dot devices revealed by the existence of multiple lasing thresholds,a significant enhancement in the neurocomputational capabilities,such as spiking duration and firing rate,is *** firing rate increased by an order of magnitude that leads to an enhancement in processing speed and,more importantly,neural spike duration was suppressed to the picosecond scale,which corresponds to a significant temporal resolution *** new regimes of operation,when combined with thermal insensitivity,silicon cointegration capability,and the fact that these multiband mechanisms are also present in miniaturized quantum-dot devices,render these neuromorphic nodes a proliferating platform for large-scale photonic spiking neural networks.

关键词： laser quantum section

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Method To Improve The Accuracy Of PV Curtailment To Maintain The System Stability in North Sulawesi and Gorontalo Power Grid

Method To Improve The Accuracy Of PV Curtailment To Maintain...

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International Conference on High Voltage engineering and Power Systems (ICHVEPS)

作者： Arif Majiid Nusa Pratama Muhammad Irfan Hashfi Ido Fandy Dermawan Kevin Marojahan Banjar-Nahor Nanang Hariyanto School of Electrical Engineering and Informatics Institut Teknologi Bandung PT PLN (Persero) Bandung Indonesia Faculty of Computer Science Universitas Indonesia PT PLN (Persero) Jakarta Indonesia UP2B Minahasa system PT PLN (Persero) Tomohon Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

The penetration of renewable energy with high intermittencies, such as solar power, presents a challenge for the North Sulawesi and Gorontalo power grid. Currently, the largest photovoltaic plant in the region is Likupang PV with a capacity of 15 MWp, followed by Isimu PV with a capacity of 10 MWp. The daytime peak load of the electrical system is 330 MW, with the initial penetration of photovoltaics into the system at 7.58%. Due to the low stiffness of the system, which was measured at 22.32 Mw/Hz in 2022, the intermittence has a direct impact on the system’s operation. The study method used for PV curtailment first determines the magnitude by analyzing the correlation between changes in multiple PV and changes in demand affecting frequency. The Pearson correlation coefficient is used to determine the magnitude of the correlation among these variables. If Likupang PV has a significant influence on changes in frequency, then it can become a curtailment target to limit the amount of power change due to intermittence. Secondly, through data mining, we can understand the function of the influence of changes in PV power on frequency changes. From this function, we can determine the operating limits of the PV to achieve the desired frequency operating range. Thirdly, in this study, this function is tested through simulations and experiments in the field to verify the accuracy of the required PV curtailment execution. By applying this approach, business owners can optimize the planning, regulation, and reliability of the electricity system, thereby mitigating power interruptions in solar power plants.

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Knowing Where to Focus: Attention-Guided Alignment for Text-based Person Search

arXiv

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arXiv 2024年

作者： Tan, Lei Li, Weihao Dai, Pingyang Chen, Jie Cao, Liujuan Ji, Rongrong Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China School of Electronic and Computer Engineering Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518066 China

In the realm of Text-Based Person Search (TBPS), mainstream methods aim to explore more efficient interaction frameworks between text descriptions and visual data. However, recent approaches encounter two principal challenges. Firstly, the widely used random-based Masked Language Modeling (MLM) considers all the words in the text equally during training. However, massive semantically vacuous words (’with’,’the’, etc.) be masked fail to contribute efficient interaction in the cross-modal MLM and hampers the representation alignment. Secondly, manual descriptions in TBPS datasets are tedious and inevitably contain several inaccuracies. To address these issues, we introduce an Attention-Guided Alignment (AGA) framework featuring two innovative components: Attention-Guided Mask (AGM) Modeling and Text Enrichment Module (TEM). AGM dynamically masks semantically meaningful words by aggregating the attention weight derived from the text encoding process, thereby cross-modal MLM can capture information related to the masked word from text context and images and align their representations. Meanwhile, TEM alleviates low-quality representations caused by repetitive and erroneous text descriptions by replacing those semantically meaningful words with MLM’s prediction. It not only enriches text descriptions but also prevents overfitting. Extensive experiments across three challenging benchmarks demonstrate the effectiveness of our AGA, achieving new state-of-the-art results with Rank-1 accuracy reaching 78.36%, 67.31%, and 67.4% on CUHK-PEDES, ICFG-PEDES, and RSTPReid, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Grammar Error Correction in Indonesian Sports News: Comparing the Performance of Pre-Trained T5 and BART Models

Grammar Error Correction in Indonesian Sports News: Comparin...

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informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Muh Adnan Esa Prakasa Yuyun Najirah Umar Nasrullah Adi Sadli A. Edeth Fuari Anatasya Hazriani Mashur Razak dept. of informatics engineering Handayani University of Makassar Makassar Indonesian National Research and Innovation Agency research center for data and information science Bandung Indonesian dept. of computer system Handayani University Makassar Makassar Indonesian

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This research compares the performance of two pre-trained models based on the transformer, namely text-to-text transfer transformer (T5) and bidirectional and auto-regressive transformer (BART), through a fine-tuned for correcting grammar in Indonesian sports news texts. We used vennify/t5-base-grammar-correction and onionLad/grammar-correction-bart-base, was trained using a dataset of 10,075 sentences from Indonesian language sports news sites. We tested the model performance using bilingual evaluation understudy (BLEU), google's language evaluation understanding (GLEU), training loss, and validation loss. The results show that BART was more efficient in learning data patterns and has better generalization ability, supported by BLEU and GLEU scores of 0.8560 and 0.9655. Meanwhile, T5 maintains simpler sentence structures, supported by BLEU and GLEU scores of 0.8120 and 0.9564. BART excels at handling more complex contexts, making it more effective at accurately correcting errors in dynamic sports news. Both models have their respective advantages, with BART being better at correcting detailed grammatical errors, while T5 offers consistent correction of more simple sentence structures.

关键词： Training Measurement Analytical models Accuracy Computational modeling computer architecture Transformers Grammar Sports Context modeling

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions

M3GNAS: Multi-modal Multi-view Graph Neural Architecture Sea...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Raeed AL-Sabri Jianliang Gao Jiamin Chen Babatounde Moctard Oloulade Zhenpeng Wu Monir Abdullah Xiaohua Hu School of Computer Science and Engineering Central South University Changsha China Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khlifa University Doha Qatar College of Computing and Information Technology University of Bisha Saudi Arabia College of Computing & Informatics Drexel University USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.

关键词： Representation learning Biological system modeling Manuals Predictive models Neural architecture search Topology Bioinformatics Faces

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