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Optimizing Knowledge Graph Embeddings for Enhanced Question Answering Performance

Optimizing Knowledge Graph Embeddings for Enhanced Question ...

引用

Intelligent Computing, Communication, Networking and Services (ICCNS), international conference on

作者： Akshar Patel Department of Computer Science City College of New York New York NY USA

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

This study investigates the impact of various negative sampling techniques on the performance of Knowledge Graph Embeddings (KGEs) in Knowledge Graph Question Answering (KGQA) systems. I evaluate six KGE variations using three metrics: Mean Rank, Mean Reciprocal Rank (MRR), and Hits@k. Our analysis reveals that the ComplEx model with Random Corrupt Negative Sampling outperforms other configurations, offering superior accuracy by effectively capturing complex relational patterns in knowledge graphs. Furthermore, I explore the performance of twelve Embed-KGQA variations, demon-strating that the RoBERTa model excels as a question encoder, especially when paired with ComplEx embed dings and Random Corrupt Negative Sampling. Comparative results between the EmbedKGQA pipeline and cosine similarity methods highlight EmbedKGQA's significantly higher success rate in answering questions. This performance gain underscores the advantage of integrating advanced natural language processing models like RoBERTa and selecting optimal negative sampling strategies to enhance the accuracy and efficiency of KGQA systems.

关键词： measurement Analytical models Accuracy Computational modeling Pipelines Knowledge graphs Performance gain Question answering (information retrieval)

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Efficiently enforcing mutual state exclusion requirements in symbolic supervisor synthesis 17

Efficiently enforcing mutual state exclusion requirements in...

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17th IEEE international conference on Automation Science and Engineering (CASE)

作者： Thuijsman, Sander Reniers, Michel Hendriks, Dennis Eindhoven Univ Technol Dept Mech Engn Eindhoven Netherlands ESI TNO Eindhoven Netherlands

ISBN: (纸本)9781665418737

Given a model of an uncontrolled system and a requirement specification, a supervisory controller can be synthesized so that the system under control adheres to the requirements. There are several ways in which informal behavioral safety requirements can be formalized, one of which is using mutual state exclusion requirements. In current implementations of the supervisor synthesis algorithm, synthesis may be inefficient when mutual state exclusion requirements are used. We propose a method to efficiently enforce these requirements in supervisor synthesis. We consider symbolic supervisor synthesis, where Binary Decision Diagrams are used to represent the system. The efficiency of the proposed method is evaluated by means of an industrial and academic case study.

关键词： computer aided software engineering Automation Computational modeling conferences Lithography Binary decision diagrams Control systems

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Real-time Car Part Instance Segmentation: the Comparison of the State-of-the-Art

Real-time Car Part Instance Segmentation: the Comparison of ...

引用

international computer Science and Engineering conference (ICSEC)

作者： Wittawin Susutti Siwarat Laoprom Thanaphit Sutthipanyo Kanok Vongsaroj Pirun Dilokpatpongsa Warin Wattanapornprom Department of Mathematics King Mongkut’s University of Technology Bangkok Thailand Department of Computer Engineering King Mongkut’s University of Technology Bangkok Thailand

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

This study investigates Deep Learning’s (DL) impact on car part segmentation, particularly through Convolutional Neural Networks (CNNs). While frameworks like Mask R-CNN improve object detection, efficiency concerns motivate the exploration of faster models. Our research prioritizes high-precision car part segmentation for real-world applications. We compared one-stage and multi-stage models, selecting Mask R-CNN, DetectoRS, QueryInst, RTMDet, and YOLOv8 for car part segmentation. Utilizing the 'Humans in the Loop' dataset with 998 extensively annotated car part images, the study contributes to computer vision advancement in the automotive domain. Training the selected models with on-the-fly data augmentation and evaluated using mean Average Precision (mAP) and mAP-50 metrics for bounding box evaluation. YOLOv8, a real-time one-stage model, achieved mAP scores of 0.70 and mAP50 scores of 0.89, highlighting its potential for efficient in car part segmentation task.

关键词： Instance segmentation Training measurement Accuracy Computational modeling Detectors Object detection Real-time systems Human in the loop Automobiles

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VR/AR System Interchangeability: A Data-Driven Method for Better Integration and Reliability

VR/AR System Interchangeability: A Data-Driven Method for Be...

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IoT, Communication and Automation Technology (ICICAT), international conference on

作者： S. Sivakami Ashlin Deepa R N Himanshu Sharma K Shilpa Myasar M. Adnan Reddykonda Thrisha Department of ECE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamilnadu India Department of CSE GRIET Bachupally Hyderabad Telangana India Department of Computer Engineering & Applications GLA University Mathura Department of CSE CMR Technical Campus Hyderabad Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Saveetha Institute of Medical and Technical Sciences SIMATS Chennai Tamilnadu India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

This research investigates the application of Graph Neural Networks (GNNs) to improve interoperability in Virtual and Augmented Reality (VR/AR) systems. We constructed a comprehensive GNN model that analyzes graph-structured data to identify patterns and enhance data integration and communication. Using datasets from VR and AR applications, our model achieved an accuracy of 89%, a 30% reduction in latency, and a 45% improvement in user satisfaction. The results indicate that GNNs are a powerful tool for addressing interoperability challenges in VR and AR systems, providing scalable and adaptable solutions.

关键词： measurement Solid modeling Refining Data integration Graph neural networks Data models Resource management Reliability Interoperability Augmented reality

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Adaptive & Fine-Grained Domain Adaptation for Pavement Crack Segmentation using YOLOv8 Learning Framework

Adaptive & Fine-Grained Domain Adaptation for Pavement Crack...

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Advances in Data Engineering and Intelligent Computing systems (ADICS), international conference on

作者： Devarapalli Abhilash Reddy Kolli Sathish Kumar P. Selvi Rajendran Department of computer Science &Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

In this study, the utilization of Roboflow's YOLOv8 architecture for crack segmentation is explored, focusing on evaluating the model's performance and readiness for real- world deployment. The methodology involves key steps such as data collection, pre processing, model training, evaluation, and deployment. Initially, a diverse dataset of images containing various types and severity of cracks is curated and annotated. The dataset undergoes pre processing to enhance image quality and standardize input dimensions for optimal training performance. Subsequently, the YOLOv8 architecture is employed for model training due to its efficiency in realtime object detection tasks and effectiveness in handling small objects like cracks. To evaluate the trained model, metrics such as precision, recall, F1score, and mean Intersection over Union (mIoU) are computed on a separate validation dataset to assess the model's ability to accurately segment cracks while minimizing false positives and false negatives.

关键词： Training measurement Image quality Adaptation models Image segmentation Computational modeling Focusing

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Intrusion Detection in SDN Using Ensemble Learning Technique

Intrusion Detection in SDN Using Ensemble Learning Technique

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Smart systems for Electrical, Electronics, Communication and computer Engineering (ICSSEECC), international conference on

作者： Bharath S Dhanashree D Nandika M A. Sunitha Nandhini Department of CSE PSG Institute of Technology and Applied Research Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

Software Defined Networks (SDN) have provided an advancement in terms of flexibility and scalability in recent times in networking. SDN paves the way for a centralized monitoring system. This centralisation has also led to increased security threats which has made the network monitors to focus on the improved security of the systems with utmost priority. The proposed methodology simulates an SDN environment using two Virtual Machines with Mininet and RYU controller. A traffic is generated to create a false attack and the detection is visualized using this environment. Machine Learning techniques provide an efficient and logical way to implement a solution. Feature Selection is an important aspect and Recursive Feature Elimination with Cross-Validation (RFECV) is used to get the final subset of features based on the importance of their significance. Synthetic Minority Oversampling Technique (SMOTE) is used in data preprocessing to handle the data imbalance. Models like XGBoost, Random Forest and Convolutional Neural Networks are used. Using ensemble technique, a meta-learner is constructed using Random Forest, XGBoost and AdaBoost to use the strengths of the standalone models and create the best version of the classifier that shows 98.8% accuracy. Accuracy, F1 score, Precision and Recall are used to evaluate and compare the performance of the models. The generalization of the constructed meta-learner is tested on another dataset and the results show good accuracy. This model can be integrated into real-world networks to enhance security by accurate intrusion detection.

关键词： Accuracy Biological system modeling Intrusion detection Telecommunication traffic Ensemble learning Software measurement Software defined networking

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Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection

Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bén...

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international joint conference on Neural Networks (IJCNN)

作者： Thorben Markmann Michiel Straat Barbara Hammer Technische Fakultät Bielefeld University Bielefeld Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Several related works have introduced Koopman-based Machine Learning architectures as a surrogate model for dynamical systems. These architectures aim to learn non-linear measurements (also known as observables) of the system’s state that evolve by a linear operator and are, therefore, amenable to model-based linear control techniques. So far, mainly simple systems have been targeted, and Koopman architectures as reduced-order models for more complex dynamics have not been fully explored. Hence, we use a Koopman-inspired architecture called the Linear Recurrent Autoencoder Network (LRAN) for learning reduced-order dynamics in convection flows of a Rayleigh Bénard Convection (RBC) system at different amounts of turbulence. The data is obtained from direct numerical simulations of the RBC system. A traditional fluid dynamics method, the Kernel Dynamic Mode Decomposition (KDMD), is used to compare the LRAN. For both methods, we performed hyperparameter sweeps to identify optimal settings. We used a Normalized Sum of Square Error measure for the quantitative evaluation of the models, and we also studied the model predictions qualitatively. We obtained more accurate predictions with the LRAN than with KDMD in the most turbulent setting. We conjecture that this is due to the LRAN’s flexibility in learning complicated observables from data, thereby serving as a viable surrogate model for the main structure of fluid dynamics in turbulent convection settings. In contrast, KDMD was more effective in lower turbulence settings due to the repetitiveness of the convection flow. The feasibility of Koopman-based surrogate models for turbulent fluid flows opens possibilities for efficient model-based control techniques useful in a variety of industrial settings.

关键词： Accuracy Computational modeling Fluid dynamics Machine learning computer architecture Predictive models Reduced order systems

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LoGo Transformer: Hierarchy Lightweight Full Self-Attention Network for Corneal Endothelial Cell Segmentation

LoGo Transformer: Hierarchy Lightweight Full Self-Attention ...

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international joint conference on Neural Networks (IJCNN)

作者： Yinglin Zhang Zichao Cai Risa Higashita Jiang Liu School of Computer Science University of Nottingham Ningbo China Ningbo China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Tomey Corporation Nagoya Japan

Corneal endothelial cell segmentation plays an important role in quantifying clinical indicators for the cornea health state evaluation. Although Convolution Neural Networks (CNNs) are widely used for medical image segmentation, their receptive fields are limited. Recently, Transformer outperforms convolution in modeling long-range dependencies but lacks local inductive bias so the pure transformer network is difficult to train on small medical image datasets. Moreover, Transformer networks cannot be effectively adopted for secular microscopes as they are parameter-heavy and computationally complex. To this end, we find that appropriately limiting attention spans and modeling information at different granularity can introduce local constraints and enhance attention representations. This paper explores a hierarchy full self-attention lightweight network for medical image segmentation, using Local and Global (LoGo) transformers to separately model attention representation at low-level and high-level layers. Specifically, the local efficient transformer (LoTr) layer is employed to decompose features into finer-grained elements to model local attention representation, while the global axial transformer (GoTr) is utilized to build long-range dependencies across the entire feature space. With this hierarchy structure, we gradually aggregate the semantic features from different levels efficiently. Experiment results on segmentation tasks of the corneal endothelial cell, the ciliary body, and the liver prove the accuracy, effectiveness, and robustness of our method. Compared with the convolution neural networks (CNNs) and the hybrid CNN-Transformer state-of-the-art (SOTA) methods, the LoGo transformer obtains the best result.

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Machine Learning and Deep Learning for Enhanced DDoS Detection in IoT-Based Intrusion Detection System

Machine Learning and Deep Learning for Enhanced DDoS Detecti...

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Innovative and Intelligent Information Technologies (IC3IT), international conference on

作者： Kamir Kharoubi Sarra Cherbal Maroua Akkal LRSD Lab Faculty of Sciences Ferhat Abbas University Setif 1 Setif Algeria

ISBN: (数字)9798331541064

ISBN: (纸本)9798331541071

Securing Internet of Things (IoT) networks is critical due to their increasing prevalence and susceptibility to cyberattacks. Machine Learning (ML) and Deep Learning (DL)powered Intrusion Detection systems (IDS) provide a promising solution to counter these threats. This study focuses on detecting Distributed Denial of Service (DDoS) attacks, ensuring IoT device availability and protecting critical services within IoT ecosystems. Five ML and DL models, including XGBoost, were trained and evaluated on the recent CICIoT2023 dataset to classify network traffic as benign or DDoS. Evaluation results show that XGBoost outperformed other models, achieving a 99. 96% accuracy in multi-classifying DDoS attack types with benign traffic and a rapid prediction time of 0.56 microseconds per traffic sample. These results highlight XGBoost's potential for real-time IoT security applications.

关键词： measurement Deep learning Accuracy Biological system modeling Intrusion detection Denial-of-service attack Real-time systems Internet of Things Security computer crime

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Deep Learning Approaches for Mango Species Classification: Performance Metrics and Model Comparisons

Deep Learning Approaches for Mango Species Classification: P...

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international conference on computer and Information Technology (ICCIT)

作者： Md. Sariful Islam Jubayer Ahmed Shawon Wahidur Rahman Md Ibrahim Mohammad Kaiser Ishaque Md Tusher Ahmad Bappy Kamrunnahar Mim Samia Yasmin Dept. of Computer Science and Engineering Uttara University Dhaka Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Mangifera indica, generally referred to as mango, is one of the most economically and nutritionally important fruits. Conventional methods for the identification of mango species are clumsy and prone to high manual errors, highlighting the need for automated systems. The following study focuses on deep learning models to bring improvements in mango species classification by considering both custom-designed Convolutional Neural Networks and pre-trained models with transfer learning to take on the challenge. The dataset includes images of eight varieties of mangoes, amounting to 3,048, collected from Pabna of Bangladesh. Several preprocessing and augmentation techniques were done to enhance the efficiency of the training. The proposed methodology is based on custom CNNs and nine pre-trained models, such as DenseNet, EfficientNet, MobileNet, VGG, and Xception for mango species classification. Some of the metrics used for the evaluation of models include accuracy, the F1 score, precision, recall, and error rates including MSE and RMSE. The results showed very good performances using the custom CNN and Xception models with accuracies of 96.94% and 99.78%, respectively. By contrast, ResNet50 and MobileNetV2 exhibited weak performance with overall low accuracy and higher error metrics. The study concludes that deep learning models, especially Xception and custom CNN, could be very promising for practical applications of mango automated sorting and quality control. Further work will be focused on the exploration of more advanced models, such as Vision Transformers, by expanding the dataset in order to enhance the robustness of the model for real-time agricultural scenarios.

关键词： Deep learning measurement Training Accuracy Biological system modeling Computational modeling Transfer learning Transformers Convolutional neural networks Sorting

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