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Michael Heigl, Enrico Weigelt, Dalibor Fiala and Martin Schramm
Over the past couple of years, machine learning methods?especially the outlier detection ones?have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns. However, the ubiquity of massive continuously genera...
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Siti Nurmaini, Radiyati Umi Partan, Wahyu Caesarendra, Tresna Dewi, Muhammad Naufal Rahmatullah, Annisa Darmawahyuni, Vicko Bhayyu and Firdaus Firdaus
An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feat...
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Haidi Badr, Nayer Wanas and Magda Fayek
Unsupervised domain adaptation (UDA) presents a significant challenge in sentiment analysis, especially when faced with differences between source and target domains. This study introduces Weighted Sequential Unsupervised Domain Adaptation (WS-UDA), a no...
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Renjie Chen and Nalini Ravishanker
With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing t...
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Turki Turki, Sanjiban Sekhar Roy and Y.-H. Taguchi
It is difficult to identify histone modification from datasets that contain high-throughput sequencing data. Although multiple methods have been developed to identify histone modification, most of these methods are not specific to histone modification bu...
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Elizabeth Hofer and Martin v. Mohrenschildt
Machines designed to perform the same tasks using different technologies can be organized into families based on their similarities or differences. We are interested in identifying common properties and differences of such machines from raw sensor data f...
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Lucas Costa Brito, Gian Antonio Susto, Jorge Nei Brito and Marcus Antonio Viana Duarte
The monitoring of rotating machinery is an essential activity for asset management today. Due to the large amount of monitored equipment, analyzing all the collected signals/features becomes an arduous task, leading the specialist to rely often on genera...
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Shaojun Wu and Ling Gao
Most supervised person re-identification methods show their excellent performance, but using labeled datasets is very expensive, which limits its application in practical scenarios. To solve the scalability problem, we propose a Cross-camera Erased Featu...
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Yiran Hao, Yiqiang Sheng and Jinlin Wang
We use the proposed packet2vec learning algorithm for IDS preprocessing, the basic steps of IDS are as follows. First, the originally collected traffic is split into packets to be truncated into fixed length. Next, the packet2vec learning algorithm is us...
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Shumin Lai, Longjun Huang, Ping Li, Zhenzhen Luo, Jianzhong Wang and Yugen Yi
In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with robust adaptive structure learning (RMFRASL), which can select discriminative features from a large amount of multimedia data to improve the p...
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Ihar Volkau, Abdul Mujeeb, Wenting Dai, Marius Erdt and Alexei Sourin
Deep learning provides new ways for defect detection in automatic optical inspections (AOI). However, the existing deep learning methods require thousands of images of defects to be used for training the algorithms. It limits the usability of these appro...
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Han Zheng, Zanyang Cui and Xingchen Zhang
Driving modes play vital roles in understanding the stochastic nature of a railway system and can support studies of automatic driving and capacity utilization optimization. Integrated trajectory data containing information such as GPS trajectories and g...
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Jose Aguilar, Camilo Salazar, Henry Velasco, Julian Monsalve-Pulido and Edwin Montoya
This paper analyses the capabilities of different techniques to build a semantic representation of educational digital resources. Educational digital resources are modeled using the Learning Object Metadata (LOM) standard, and these semantic representati...
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Dawei Luo, Heng Zhou, Joonsoo Bae and Bom Yun
Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of-distrib...
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Francesca Calabrese, Alberto Regattieri, Raffaele Piscitelli, Marco Bortolini and Francesco Gabriele Galizia
Extracting representative feature sets from raw signals is crucial in Prognostics and Health Management (PHM) for components? behavior understanding. The literature proposes various methods, including signal processing in the time, frequency, and time?fr...
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Junwen Lu, Jinhui Wang, Xiaojun Wei, Keshou Wu and Guanfeng Liu
There is relatively little research on deep learning for anomaly detection within the field of deep learning. Existing deep anomaly detection methods focus on the learning of feature reconstruction, but such methods mainly learn new feature representatio...
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Francisco Silva, Tania Pereira, Julieta Frade, José Mendes, Claudia Freitas, Venceslau Hespanhol, José Luis Costa, António Cunha and Hélder P. Oliveira
Lung cancer late diagnosis has a large impact on the mortality rate numbers, leading to a very low five-year survival rate of 5%. This issue emphasises the importance of developing systems to support a diagnostic at earlier stages. Clinicians use Compute...
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Florent Poux and Roland Billen
Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In ...
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Xiaojuan Wang and Weilan Wang
As there is a lack of public mark samples of Tibetan historical document image characters at present, this paper proposes an unsupervised Tibetan historical document character recognition method based on deep learning (UD-CNN). Firstly, using the Tibetan...
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Jean-Sébastien Dessureault and Daniel Massicotte
This paper examines the critical decision process of reducing the dimensionality of a dataset before applying a clustering algorithm. It is always a challenge to choose between extracting or selecting features. It is not obvious to evaluate the importanc...
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