The proceedings contain 108 papers. The topics discussed include: copula-based cox regression to modelling bivariate time-to-event data;Chinese NER based on adversarial training and interactive attention;a survey of n...
ISBN:
(纸本)9798400709234
The proceedings contain 108 papers. The topics discussed include: copula-based cox regression to modelling bivariate time-to-event data;Chinese NER based on adversarial training and interactive attention;a survey of novel framework of anomaly-based intrusion detection system in computer networks using ensemble feature integration with deep learning techniques;multi-modal contextual prompt learning for multi-label classification with partial labels;an evolutionary approach to forecasting models hybridization;structural subspace learning for few-shot fine-grained recognition;SCC-Conv: a novel convolution with spatial-channel cheap operation for single image super-resolution;task-aware local representation mining network for few-shot fine-grained recognition;and Mpox-PyramidTransferNet: a hierarchical transfer learning framework for monkeypox and dermatological disease classification.
The proceedings contain 96 papers. The topics discussed include: simulation analysis of binary antenna radiation field mathematical model of instrument landing system;quantum algorithm for regularized spectral cluster...
ISBN:
(纸本)9781450398411
The proceedings contain 96 papers. The topics discussed include: simulation analysis of binary antenna radiation field mathematical model of instrument landing system;quantum algorithm for regularized spectral clustering;parallel simulation of lid-driven flow with internal square obstacle;multi-missile path planning algorithm based on reinforcement learning;maximizing network reliability in large scale infrastructure networks: a heat conduction model perspective;a pipeline and a graph-based model for paragraph extraction: take the insurance document as an example;an improved bare bone multi-objective particle swarm optimization algorithm for solving stochastic wind-solar-small hydro power dispatch problems;distant supervision relation extraction of combination bag with hierarchical attention;multi-factor quantitative investment tactic design based on support vector machine;unexpectedly useful: convergence bounds and real-world distributed learning;stochastic compositional kernel estimation for Gaussian process models;and ensemble two stage machinelearning for network abnormal detection.
The proceedings contain 84 papers. The topics discussed include: characterizing phenotypes for mental health disorders with wrapper‐typed feature selection techniques: comparison of RF‐RFE and fuzzy forest;chaos pre...
ISBN:
(纸本)9781450395700
The proceedings contain 84 papers. The topics discussed include: characterizing phenotypes for mental health disorders with wrapper‐typed feature selection techniques: comparison of RF‐RFE and fuzzy forest;chaos prediction of power systems by using deep learning;a highly efficient, confidential, and continuous federated learning backdoor attack strategy;a transformer‐based deep learning model for evaluation of accessibility of image descriptions;single stock trading with deep reinforcement learning: a comparative study;self‐supervised domain adaptation model based on contrastive learning;pseudo reward and action importance classification for sparse reward problem;a machinelearning‐based approach for cardiovascular diseases prediction;DeepGCNMIL: multi‐head attention guided multi‐instance learning approach for whole‐slide images survival analysis using graph convolutional networks;and optimize the NOx emission concentration of circulation fluidized bed boiler based on on‐line learning neural network and modified TLBO algorithm.
The proceedings contain 91 papers. The topics discussed include: applications of machinelearning techniques in genetic circuit design;efficient domain-specific news push service using deep learning based text regress...
ISBN:
(纸本)9781450389310
The proceedings contain 91 papers. The topics discussed include: applications of machinelearning techniques in genetic circuit design;efficient domain-specific news push service using deep learning based text regression without users’ information;online optimal investment portfolio model based on deep reinforcement learning;research on flow classification model based on similarity and machinelearning algorithm;factors affecting accuracy of genotype imputation using neural networks in deep learning;algorithmic generation of positive samples for compound-target interaction prediction;using deep learning to construct auto web penetration test;acoustic classification of bird species using wavelets and learning algorithms;and an enhanced adaptive large neighborhood search algorithm for the capacitated vehicle routing problem.
The proceedings contain 102 papers. The topics discussed include: an attentive pruning method for edge computing;a fast detecting method for clone functions using global alignment of token sequences;subconcept based o...
ISBN:
(纸本)9781450376426
The proceedings contain 102 papers. The topics discussed include: an attentive pruning method for edge computing;a fast detecting method for clone functions using global alignment of token sequences;subconcept based one class classification method with cluster updating;urban residential land price appraisal via quantifying impact factors based on deep belief networks;a new method for short-term traffic flow prediction based on multi-segments features;a framework for event-oriented text retrieval based on temporal aspects: a recent review;similarity measure of multivariate time series based on segmentation;distributed semi-supervised multi-label classification with quantized communication;revisiting the correlation of basketball stats and match outcome prediction;and source model selection as a meta-learning technique to learn novel concepts.
The early detection of potential failures in industrial machinery components is paramount for ensuring the reliability and safety of operations, thereby preserving machine Condition Monitoring (MCM). This research add...
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ISBN:
(纸本)9798400709234
The early detection of potential failures in industrial machinery components is paramount for ensuring the reliability and safety of operations, thereby preserving machine Condition Monitoring (MCM). This research addresses this imperative by introducing an innovative approach to Real-Time Acoustic Anomaly Detection. Our method combines semi-supervised temporal convolution with representation learning and a hybrid model strategy with Temporal Convolutional Networks (TCN) to handle various intricate anomaly patterns found in acoustic data effectively. The proposed model demonstrates superior performance compared to established research in the field, underscoring the effectiveness of this approach. Not only do we present quantitative evidence of its superiority, but we also employ visual representations, such as t-SNE plots, to further substantiate the model's efficacy.
Quantum machinelearning algorithms use qubit encoding and quantum circuits to perform feature extraction and pattern recognition. However, the choice of data encoding methods can have a significant impact on the mode...
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ISBN:
(纸本)9798400709234
Quantum machinelearning algorithms use qubit encoding and quantum circuits to perform feature extraction and pattern recognition. However, the choice of data encoding methods can have a significant impact on the model's performance. To evaluate the effect of different encoding methods in a systematic way, we propose two metrics: distribution distance and distribution radius. These metrics describe how the encoded data distribute in the Hilbert space. We show that there is a positive correlation between prediction accuracy and distribution distance, and a negative correlation between prediction accuracy and distribution radius, both theoretically and experimentally. Based on our findings, we suggest a comparative evaluation of data encoding methods for quantum machinelearning, which can help improve the learning efficiency.
This comprehensive study explores the enduring fascination with and scholarly examination of Egyptian hieroglyphs. The investigation focuses on the writing structure of Egyptian hieroglyphs, employing image and pixel ...
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ISBN:
(纸本)9798400709234
This comprehensive study explores the enduring fascination with and scholarly examination of Egyptian hieroglyphs. The investigation focuses on the writing structure of Egyptian hieroglyphs, employing image and pixel representations with the aim of achieving accurate reconstruction. The study utilizes a stable diffusion model and DeepSVG. We investigate challenges in providing precise reconstructions and evaluate the strengths and weakness of these methods. Thorough A significant contribution of the study is the presentation of a dataset comprising both pixel-based and vector-based images of Egyptian hieroglyphs. The findings contribute to ongoing discussions in linguistics, archaeology, and the interdisciplinary intersection of AI with historical studies.
The present work takes inspiration from the work of Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan at Google Research, Brain Team about CoAtNet. In that work it was showed that it is possible to combine the strengt...
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ISBN:
(纸本)9798400709234
The present work takes inspiration from the work of Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan at Google Research, Brain Team about CoAtNet. In that work it was showed that it is possible to combine the strengths from both convolution and transformer architectures, by unifying convnets and self-attention into a machinelearning model. We want to apply the CoAtNet to a visual dataset of malware images and compare its performances to a baseline CNN model. For this reason we need a data set of appropriate size and format. From these needs triggers the requirement to find or generate a visual dataset of the malware images capable to measure the accuracy of the constructed model. As will be seen, the creation of a new dataset will be preferred to the search for an existing dataset. Although the visual approach has already been extensively tested in recent years, there is still a need for more customised data for the model under examination. The work described in this paper can serve as a guide to a balanced and dimensioned construction of an optimal malware visual image dataset.
Reinforcement learning algorithms struggle with tasks that have complex hierarchical dependency structures. For this problem, humans usually represent the whole task in a structured way and solve it layer by layer. In...
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ISBN:
(纸本)9798400709234
Reinforcement learning algorithms struggle with tasks that have complex hierarchical dependency structures. For this problem, humans usually represent the whole task in a structured way and solve it layer by layer. In this paper, we propose a novel approach called Past Data-Driven Adaptation in Hierarchical Reinforcement learning (AdaHRL). AdaHRL leverages 'past samples' from a replay buffer to discover subgoals and construct a subgoal tree, effectively steering the agent's learning trajectory. Simultaneously, AdaHRL fine-tunes the data distribution of the entire replay buffer using a filter function, empowering adaptive learning within the agent. Experimental results demonstrate that our approach outperforms Unified Model-Free HRL Framework (UHRL) and Hindsight experience replay (HER) in tasks with complex hierarchical dependencies.
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