the proceedings contain 118 papers. the topics discussed include: advancements in public transport: design and implementation of an android-based real-time bus tracking system;computational techniques high-quality wir...
ISBN:
(纸本)9798350338287
the proceedings contain 118 papers. the topics discussed include: advancements in public transport: design and implementation of an android-based real-time bus tracking system;computational techniques high-quality wireless communication to the outside world for further processing in wireless sensor networks;precision agriculture using LORA radar based smart irrigation multi-cuisine and multi-data screening of food quality at service points using object detection capabilities of YOLO NAS monitoring system;neural networks and machinelearning;Indian sign language recognition using scale-invariant feature transform by depth sensor;e-commerce engineering navigating web development based voice for local;enhancing emotion recognition: machinelearning with phasic spectrogram texture features;and disaster-resilient smart city framework;a cross-layer protocol analysis for emergency earthquake response.
Withthe wide application of knowledge graphs in various scenarios and Neo4j graph databases becoming excellent knowledge graph carriers, Cypher (CQL for short) has become the most popular graph database query languag...
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ISBN:
(纸本)9798350355925
Withthe wide application of knowledge graphs in various scenarios and Neo4j graph databases becoming excellent knowledge graph carriers, Cypher (CQL for short) has become the most popular graph database query language. However, when performing graph database retrieval, the complex pattern and syntax make constructing CQL statements a complicated and time-consuming task. therefore, similar to Text-to-SQL, it is necessary and urgent to study an effective method for end-to-end transformation of natural languages into CQL. there have been many advances in Text-to-SQL for traditional relational databases, but these methods cannot be well applied to Text-to-CQL, so there is still a lack of effective work on Text-to-CQL for graph databases. In recent years, as large language models have been widely applied to different tasks with good results, and considering that the fundamental difference between CQL and SQL is the representation of graph patterns. We propose in this paper PA-LLM, a Text-to-CQL method that utilizes large language models combined with graph patterns enhancement, which combines large language models and subdivides the graph patterns into three categories according to their respective characteristics. they are simple query patterns, multi-hop query patterns, and function query patterns. For different graph patterns, the method optimizes the process of generating CQL for the model, which can be subdivided into four sub-methods, simple pattern enhancement method, multi-hop pattern enhancement method, function pattern enhancement method, and entity-relationship enhancement method. the experimental results show that the method improves the quality of the CQL statements generated by the model, including the logical accuracy ACCLX and the execution result accuracy ACCEX, and achieves better results on the SpCQL dataset proposed by the National Defense University of Science and Technology (NDUST). It provides an effective solution for the task of converting CQL t
Human Activity recognition for Fight Detection is an important research domain aimed at automatically identifying patterns indicative of physical altercations. Leveraging deep learning models like CNNs and RNNs, this ...
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In the domain of the software fault prediction numerous methods have been introduced and implemented using datamining techniques and machinelearning models. Nevertheless, initial and early fault prediction is big ch...
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Handwritten character recognition is a crucial process that involves converting handwritten text on different surfaces, such as paper and postcards, into digital formats, making it distinguishable from scanned images....
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Predicting and evaluating the operation status of the equipment online can not only reflect the personalization of the equipment but also meet the actual working needs of intelligent substations. through acoustic fing...
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this paper employs an innovative approach that combines self-supervised learning feature fusion and adapter fine-tuning for a speech emotion recognition task. the results show a 5.57% improvement in accuracy compared ...
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the proceedings contain 138 papers. the topics discussed include: research on near-infrared spectroscopy calibration transfer algorithm based on piecewise direct standardization and K-Nearest neighbor;research on inte...
ISBN:
(纸本)9798350352719
the proceedings contain 138 papers. the topics discussed include: research on near-infrared spectroscopy calibration transfer algorithm based on piecewise direct standardization and K-Nearest neighbor;research on intelligent data operation and maintenance management scheme;GAIPPTSC: an efficient container tasks orchestration and scheduling algorithm;enhancing network security: machinelearning evaluation for intrusion detection in power load management system;UAV anomaly detection model based on integrated multi-modal neural network and neural architecture search;frequency hopping modulation format recognition based on higher-order cyclic cumulants and neural network classifier;cluster-based two-stage client selection strategy for personalized federated learning;and implementation of fir digital filter algorithm in C language on DSP platform.
Planning a geotechnical site investigation for amega transportation project is a challenging task especially if a large quantum of information and data must be managed. Traditionally, the focus in planning investigati...
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Planning a geotechnical site investigation for amega transportation project is a challenging task especially if a large quantum of information and data must be managed. Traditionally, the focus in planning investigations is on the product itself with little care given to the data and information used to get to the product. this paper explains how to simplify the delivery of products and reduce costs by managing the data and the information correctly. It describes: how large language processing, optical character recognition, and machinelearning models can accelerate data extraction from geotechnical logs;the fundamental qualities and functions of a cloud-based geotechnical database to improve collaboration and configuration management;how parts of the reliability assessment of historic ground data can be automated;how geographical information system (GIS) can summarize the desk study information into a singular site plan to enable a more holistic interpretation of subsurface conditions. this paper discusses how novel methods can enhance the efficiency, accuracy, and precision of the desk study, geotechnical risk assessment and the scoping processes. It is based on a highway upgrade project in Queensland, Australia involving multiple new interchanges and bridges, and where novel methods were used to deliver a cost-effective scope.
Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-theart handcrafted heuristic strategies suffer from ...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-theart handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machinelearning methods require a significant amount of labeled data. We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm. We use imitation learning to bootstrap an RL agent and then use Proximal Policy Optimization (PPO) to further explore global optimal actions. then, a value network is used to run Monte-Carlo tree search (MCTS) to enhance the policy network. We evaluate the performance of our method on four different categories of combinatorial optimization problems and show that our approach performs strongly compared to the state-of-the-art machinelearning and heuristics based methods.
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