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arXiv 2024年

作者： Pellegrino, Alessio Akgün, Özgür Dang, Nguyen Kiziltan, Zeynep Miguel, Ian Dept. of Computer Science and Engineering University of Bologna Italy School of Computer Science University of St Andrews United Kingdom

Constraint modelling languages such as Essence offer a means to describe combinatorial problems at a high-level, i.e., without committing to detailed modelling decisions for a particular solver or solving paradigm. Given a problem description written in Essence, there are multiple ways to translate it to a low-level constraint model. Choosing the right combination of a low-level constraint model and a target constraint solver can have significant impact on the effectiveness of the solving process. Furthermore, the choice of the best combination of constraint model and solver can be instance-dependent, i.e., there may not exist a single combination that works best for all instances of the same problem. In this paper, we consider the task of building machine learning models to automatically select the best combination for a problem instance. A critical part of the learning process is to define instance features, which serve as input to the selection model. Our contribution is automatic learning of instance features directly from the high-level representation of a problem instance using a language model. We evaluate the performance of our approach using the Essence modelling language with a case study involving the car sequencing problem. © 2024, CC BY.

关键词： Modeling languages

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Pattern Recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features

Pattern Recognition in Lung Disease Classification: Leveragi...

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International Conference on Computing and Networking Technology (ICCNT)

作者： M Ramya Sree Mettu Siddhartha Poli Vamsi Vardhan Reddy Tripty Singh Rekha R Nair Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. Recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and Local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction.

关键词： Visualization Accuracy Quantization (signal) Pneumonia Lungs Computational modeling Feature extraction Convolutional neural networks X-ray imaging Medical diagnostic imaging

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Random Selection for Cloud-Based Load-Distributing in Varied P2P Networks using Enhanced Throttled Algorithm 5

Random Selection for Cloud-Based Load-Distributing in Varied...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Prakash, V.S. Mary, Margaret T. Shivakumar, B.N. Gnanadurai, Jasmine Divya, K.S. Vennila, J. Kristu Jayanti College Department of Computer Science Karnataka Bangalore India Cmr Institute of Technology Department of Mathematics Karnataka Bengaluru India George Fox University Dept. Electrical and Computer Science Engineering OR United States Manipal College of Health Professions Mahe Department of Statistics Manipal India

ISBN: (纸本)9798350379884

A peer-to-peer network connection is essential to most computer users' daily activities. On heterogeneous P2P networks, maintaining cloud-based load balance is a difficult goal to achieve. Several previous research have provided load rearrangement procedures for additional load diversions, and load division on the point where the original peer arrives. The goal of cloud-based load-sharing in P2P structures is to evenly distribute the burden among net nodes, then it is not effective in efficiently removing traffic that is already happening on the P2P network's routes. By removing the traffic flow and consuming the Random Selection Load Balancing (RSLB) method and the Incredible Store Model, the behaviors in this study are characterized. Using an improved throttled method, the Incredible Store Model is examined to broaden the effects of node variety in addition to agitation toward the capacity circulation in P2P systems. Now different from the current load distribution balancing strategy, the presentation of the Random Selection Load Balancing (RSLB) technique by the Incredible Store Model decreases the network traffic flow pathways with little cloud rate of P2P. By combining the burden in the P2P net system, an investigative and observed conclusion delivers a destruction-free system with information calculated. Peer failure probability in addition to traffic control effectiveness is used to estimate the presentation of the random selection load assessment. © 2024 IEEE.

关键词： Peer to peer networks

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SafeEmbodAI: a Safety Framework for Mobile Robots in Embodied AI Systems

arXiv

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arXiv 2024年

作者： Zhang, Wenxiao Kong, Xiangrui Braunl, Thomas Hong, Jin B. Dept. of Computer Science and Software Engineering The University of Western Australia Perth Australia Dept. of Electrical Electronic and Computer Engineering The University of Western Australia Perth Australia

Embodied AI systems, including AI-powered robots that autonomously interact with the physical world, stand to be significantly advanced by Large Language Models (LLMs), which enable robots to better understand complex language commands and perform advanced tasks with enhanced comprehension and adaptability, highlighting their potential to improve embodied AI capabilities. However, this advancement also introduces safety challenges, particularly in robotic navigation tasks. Improper safety management can lead to failures in complex environments and make the system vulnerable to malicious command injections, resulting in unsafe behaviours such as detours or collisions. To address these issues, we propose SafeEmbodAI, a safety framework for integrating mobile robots into embodied AI systems. SafeEmbodAI incorporates secure prompting, state management, and safety validation mechanisms to secure and assist LLMs in reasoning through multi-modal data and validating responses. We designed a metric to evaluate mission-oriented exploration, and evaluations in simulated environments demonstrate that our framework effectively mitigates threats from malicious commands and improves performance in various environment settings, ensuring the safety of embodied AI systems. Notably, In complex environments with mixed obstacles, our method demonstrates a significant performance increase of 267% compared to the baseline in attack scenarios, highlighting its robustness in challenging conditions. © 2024, CC BY.

关键词： Mobile robots

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Short-term Wind Power Ramp Forecasting Using Sequential Approach

Short-term Wind Power Ramp Forecasting Using Sequential Appr...

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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Leechita Gopalakrishnan Julian L Cardenas Barrera N. Sabiyath Fatima Dept. of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Dept. of Electrical and Computer Engineering University of New Brunswick Fredericton Canada

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

As wind energy becomes a key player in the global transition to renewable energy, its variability presents significant challenges. Accurate wind ramp forecasting is crucial for managing such rapid fluctuations in wind power output, ensuring grid stability, and enhancing the reliability of renewable energy integration. This paper presents a short-term wind ramp forecasting model using a sequential approach: initially forecasting wind power through the integration of Variational Mode Decomposition (VMD) and the XGBoost algorithm, and subsequently detecting wind ramps using the Definition Based Sign Indicator (DSI) method. The historical wind speed and power data for Turkey was obtained from Kaggle. The dataset comprises readings from a wind turbine’s SCADA system in Turkey, with measurements recorded at 10minute intervals. Variational Mode Decomposition (VMD) was applied to deconstruct and restore the historical wind power of the wind field, resulting in the formation of three training datasets. Wind power projections for the subsequent 8 hours, at 10 -minute intervals, are generated using historical wind power, wind speed, and wind direction as inputs. The DSI algorithm is employed to identify ramps. To assess the predictive capability of the proposed model, two algorithms, SVR and XGBoost, were employed for forecasting and the resulting error is minimal. The proposed model, VMD + XGBOOST is also compared without VMD to evaluate the performance. Taking mean square error (MSE), root mean square error (RMSE) mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) as evaluation indicators, the results show that the forecasted accuracy of wind power is significantly improved after VMD processing. The performance of XGBOOST is better than SVR in the two evaluation indicators. Precision, Recall, and F1-score are calculated to evaluate the accuracy of the predicted wind ramps.

关键词： Renewable energy sources Accuracy Wind speed SCADA systems Wind power generation Predictive models Prediction algorithms Wind turbines Wind forecasting Forecasting

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A 10ps-Order Flexible Resolution Time-to-Digital Converter with Linearity Calibration and Legacy FPGA

A 10ps-Order Flexible Resolution Time-to-Digital Converter w...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Katoh, Kentaroh Nakura, Toru Kobayashi, Haruo Faculty of Engineering Fukuoka University Dept. of Electronics Engineering and Computer Science Japan Gunma Univesity Japan

ISBN: (纸本)9783982674100

This paper presents a 10ps-order flexible resolution time-to-digital converter (TDC) consisting of only Lookup Tables and Flip-Flops that can be applied to legacy FPGAs, which is industry friendly. The proposed TDC is a Vernier delay-line based TDC. By using MUX chains as the delay adjustable buffers, it realizes flexible and high resolution 10ps-order TDC. By controlling the control values of each MUX chain independently, the nonlinearity of TDC is compensated. In the evaluation using the AMD Artix-7 FPGA, the DNL and INL were [-0.26 LSB, 0.91 LSB] and [-0.84 LSB, 2.27 LSB], respectively, at a resolution of 8.92 ps. © 2025 EDAA.

关键词： Table lookup

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Vertical validation: evaluating implicit generative models for graphs on thin support regions 24

Vertical validation: evaluating implicit generative models f...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Mai Elkady Thu Bui Bruno Ribeiro David I. Inouye Computer Science Dept. Purdue University West Lafayette Indiana Electrical and Computer Engineering Dept. Purdue University West Lafayette Indiana

There has been a growing excitement that implicit graph generative models could be used to design or discover new molecules for medicine or material design. Because these molecules have not been discovered, they naturally lie in unexplored or scarcely supported regions of the distribution of known molecules. However, prior evaluation methods for implicit graph generative models have focused on validating statistics computed from the thick support (e.g., mean and variance of a graph property). Therefore, there is a mismatch between the goal of generating novel graphs and the evaluation methods. To address this evaluation gap, we design a novel evaluation method called Vertical Validation (VV) that systematically creates thin support regions during the train-test splitting procedure and then reweights generated samples so that they can be compared to the held-out test data. This procedure can be seen as a generalization of the standard train-test procedure except that the splits are dependent on sample features. We demonstrate that our method can be used to perform model selection if performance on thin support regions is the desired goal. As a side benefit, we also show that our approach can better detect overfitting as exemplified by memorization.

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Digital Twin of Industrial Robots Over the Cloud: Framework, Design and Analysis

Digital Twin of Industrial Robots Over the Cloud: Framework,...

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International Conference on Robotics and Automation sciences (ICRAS)

作者： Hari Ganesan Aaryan M Sandeep Aakash Negi Yuktha M Ashok Kumar Patil Dept. of Computer Science Engineering PES University Bangalore India Dept. of Electronics and Communication Engineering PES University Bangalore India

ISBN: (数字)9798350352504

ISBN: (纸本)9798350352511

This paper presents a novel approach for creating real-time, secure and interactable Digital Twins of industrial robots. This approach diverges from previous approaches that relied on Digital Twin Definition Language (DTDL). The proposed method uses Robotics Operating Systems (ROS) and the Unity Game Engine to build the Digital Twins. The prototype built to showcase this approach comprises 4 sub-systems namely Data Acquisition sub-system, Cloud sub-system, Digital Client sub-system, and Remote-Control sub-system. The methodology focuses on real-time data synchronization using cloud-based communication services like loT servers. There are also multiple security measures employed to secure the data- like passing data through proxy and end-to-end encryption of data. The prototype made using Niryo Ned 2 Robot and a Raspberry Pi 3b+ showcases accurate physical-Digital Twin and effective remote control. Key findings highlight the system's ability to overcome latency, security and data accuracy, which in turn helps improve human-robot collaboration and interactions. This research contributes to the Digital Twin field by providing a modular and scalable framework that can be adapted for various robots and systems. This paper not only provides the general idea for implementation and problems faced during development but also suggests pathways for future work, like - optimization of message rates, integration of machine learning for maintenance and other purposes.

关键词： Accuracy Service robots Prototypes Industrial robots Real-time systems Digital twins Security

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Towards Robust mmWave-based Human Activity Recognition using Large Simulated Dataset for Model Pretraining

Towards Robust mmWave-based Human Activity Recognition using...

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IEEE International Conference on Big Data

作者： Vinay Joshi Shengkai Xu Qiming Cao Yi Zhu Pu Wang Hongfei Xue Dept. of Computer Science UNC Charlotte Charlotte USA Electrical and Computer Engineering Purdue University West Lafayette USA Dept. of Computer Science Wayne State University Detroit USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Human activity recognition (HAR) is crucial for real-world applications such as healthcare, surveillance, and smart homes. Among sensing technologies, millimeter wave (mmWave) sensors stand out due to their contactless nature, high sensitivity, and ability to operate in low-light environments while preserving privacy. However, the scarcity of mmWave sensing data limits the generalizability of mmWave-based HAR systems. To address this, we propose mmAP, a data augmentation and pretraining framework that synthesizes a large mmWave dataset using human mesh data, followed by pretraining a robust and general mmWave heatmap encoder using a multi-modal masked autoencoder framework using the synthesized data. We enhance the model’s robustness with heatmap-specific data perturbations and perform task-specific fine-tuning on a small real-world dataset. The experiment results over the baseline demonstrate the effectiveness of the proposed mmAP framework.

关键词： Heating systems Perturbation methods Surveillance Autoencoders Smart homes Data augmentation Data models Robustness Human activity recognition Millimeter wave communication

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AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations

AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogene...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bomgni, Alain Bertrand Ceskoutsé, Ribot Fleury Téné Njingang, Kevin Jordan Njike Bouetou, Thomas Bouetou Gadhamshetty, Venkataramana Etienne Gnimpieba, Z. Oglala Lakota College KyleSD United States University of Yaounde 1 Ecole Nationale Supérieure Polytechnique Yaounde Cameroon University of Dschang Dept. of Computer Science Dschang Cameroon South Dakota School of Mines and Technology Dept. of Civil and Environmental Engineering Rapid CitySD United States University of South Dakota Dept. of Biomedical Engineering VermillionSD United States

ISBN: (纸本)9798350386226

The synthesis of small molecules is a crucial task across multiple scientific domains, including drug discovery, materials science, and sustainable chemistry. As AI and Machine Learning (ML) continue to advance, these technologies offer transformative potential in molecular synthesis, enhancing efficiency and expanding synthetic possibilities. In particular, small molecule synthesis has profound implications for creating novel therapeutic agents, high-performance materials, and greener chemical processes, but a major challenge remains in designing efficient synthetic routes for target molecules. Retrosynthesis, the process of mapping out synthetic pathways by working backwards from a target molecule, represents a vital step;however, traditional retrosynthesis methods often struggle to predict complex or novel transformations. To address this limitation, we introduce AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations, a model that leverages graph neural network techniques to enhance retrosynthetic predictions. AIM-HKR integrates information from heterogeneous knowledge graphs, capturing the intricate relationships and analogical reasoning required for retrosynthesis. This model generates type-specific embeddings that reflect both network topology and semantic connections across different entity types, such as molecules, reactions, and functional groups. AIM-HKR's unique capacity to leverage heterogeneous graphs allows it to propose synthetic pathways that extend beyond established precedents, enabling predictions of chemically feasible but previously uncharted transformations. We believe AIM-HKR has the potential to significantly advance molecular synthesis through AI-driven retrosynthesis, establishing a new paradigm in AI-assisted chemistry. © 2024 IEEE.

关键词： Graph neural networks

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