computer systems technology students attending Kansas State University at Salina are typically focused on technology and have minimal exposure to art and graphic design concepts. In the "Fundamentals of Web Desig...
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Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering,economics,and computer *** algorithms are designed to find high-quality solutions ...
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Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering,economics,and computer *** algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising *** heuristic optimization algorithms vary in their specific details,they often exhibit common patterns that are essential to their *** paper aims to analyze and explore common patterns in heuristic optimization *** a comprehensive review of the literature,we identify the patterns that are commonly observed in these algorithms,including initialization,local search,diversity maintenance,adaptation,and *** each pattern,we describe the motivation behind it,its implementation,and its impact on the search *** demonstrate the utility of our analysis,we identify these patterns in multiple heuristic optimization *** each case study,we analyze how the patterns are implemented in the algorithm and how they contribute to its *** these case studies,we show how our analysis can be used to understand the behavior of heuristic optimization algorithms and guide the design of new *** analysis reveals that patterns in heuristic optimization algorithms are essential to their *** understanding and incorporating these patterns into the design of new algorithms,researchers can develop more efficient and effective optimization algorithms.
The microphysical structure of rain has a significant impact on the quality of radio signal transmission in the upcoming deployment of 5G millimetre-wave wireless communications in South Africa. To address this, mitig...
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The k-Nearest Neighbors (kNN) algorithm is one of the most widely used techniques for data classification. However, the imbalanced class is a key problem for its declining performance. Therefore, the kNN algorithm is ...
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Phishing attacks are among the persistent threats that are dynamically evolving and demand advanced detection mechanisms to counter more sophisticated techniques. Traditional detection approaches are usually based on ...
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With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Ear...
With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.
People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces....
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People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.
The essence of music is inherently multi-modal – with audio and lyrics going hand in hand. However, there is very less research done to study the intricacies of the multi-modal nature of music, and its relation with ...
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Deep learning has gained superior accuracy on Euclidean structure data in neural *** a result,nonEuclidean structure data,such as graph data,has more sophisticated structural information,which can be applied in neural...
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Deep learning has gained superior accuracy on Euclidean structure data in neural *** a result,nonEuclidean structure data,such as graph data,has more sophisticated structural information,which can be applied in neural networks as well to address more complex and practical ***,actual graph data obeys a power-law distribution,so the adjacent matrix of a graph is random and *** processing accelerator(GPA)is designed to handle the problems ***,graph computing only processes 1-dimensional *** graph neural networks(GNNs),graph data is ***,GNNs include the execution processes of both traditional graph processing and neural network,which have irregular memory access and regular computation,*** obtain more information in graph data and require better model generalization ability,the layers of GNN are deeper,so the overhead of memory access and computation is *** present,GNN accelerators are designed to deal with this *** this paper,we conduct a systematic survey regarding the design and implementation of GNN ***,we review the challenges faced by GNN accelerators,and existing related works in detail to process ***,we evaluate previous works and propose future directions in this booming field.
Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-m...
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Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accurac
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