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Danh sách kết quả tìm kiếm tài liệu từ 1 đến 6 trong 6 tài liệu phù hợp.
Tài liệu phù hợp với tiêu chí tìm kiếm:
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  • Statistical learning for semantic parsing: A survey

    • Tác giả: Zhu, Q.;  Người hướng dẫn: -;  Người tham gia: Ma, X.; Li, X (2019)

  • A long-term goal of Artificial Intelligence (AI) is to provide machines with the capability of understanding natural language. Understanding natural language may be referred as the system must produce a correct response to the received input order. This response can be a robot move, an answer to a question, etc. One way to achieve this goal is semantic parsing. It parses utterances into semantic representations called logical form, a representation of many important linguistic phenomena that can be understood by machines. Semantic parsing is a fundamental problem in natural language understanding area. In recent years, researchers have made tremendous progress in this field. In this paper, we review recent algorithms for semantic parsing including both conventional machine learning ...
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  • Omnidirectional Feature Learning for Person Re-Identification

    • Tác giả: Wu, D.;  Người hướng dẫn: -;  Người tham gia: Yang, H.; Huang, D.; Yuan, C.; Qin, X.; Zhao, Y.; Zhao, X.; Sun, J. (2019)

  • Person re-identi cation (PReID) has received increasing attention due to it being an important role in intelligent surveillance. Many state-of-the-art PReID methods are part-based deep models. Most of these models focus on learning the part feature representation of a person's body from the horizontal direction. However, the feature representation of the body from the vertical direction is usually ignored. In addition, the relationships between these part features and different feature channels are not considered. In this paper, we introduce a multi-branch deep model for PReID. Speci cally, the model consists of ve branches. Among the ve branches, two branches learn the part features with spatial information from horizontal and vertical orientations; one branch aims to learn the i...
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  • On the Feasibility of Deep Learning in Sensor Network Intrusion Detection

    • Tác giả: Otoum, S.;  Người hướng dẫn: -;  Người tham gia: Kantarci, B.; Mouftah, H. T. (2019)

  • In this letter, we present a comprehensive analysis of the use of machine and deep learning (DL) solutions for IDS systems in wireless sensor networks (WSNs). To accomplish this, we introduce restricted Boltzmann machine-based clustered IDS (RBC-IDS), a potential DL-based IDS methodology for monitoring critical infrastructures by WSNs. We study the performance of RBC-IDS, and compare it to the previously proposed adaptive machine learning-based IDS: the adaptively supervised and clustered hybrid IDS (ASCH-IDS). Numerical results show that RBC-IDS and ASCH-IDS achieve the same detection and accuracy rates, though the detection time of RBC-IDS is approximately twice that of ASCH-IDS. Index Terms—Wireless sensor network, cybersecurity,
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  • Machine-Learning Detection Algorithms for Large Barkhausen Jumps in Cluttered Environment

    • Tác giả: Alimi, R.;  Người hướng dẫn: -;  Người tham gia: Ivry, A.; Fisher, E.; Weiss, E. (2019)

  • Modern magnetic sensor arrays conventionally use state-of-the-art low-power magnetometers such as parallel and orthogonal fluxgates. Low-power fluxgates tend to have large Barkhausen jumps that appear as a dc jump in the fluxgate output. This phenomenon deteriorates the signal fidelity and effectively increases the internal sensor noise. Even if sensors that are more prone to dc jumps can be screened out during production, the conventional noise measurement does not always catch the dc jumps because of their sparsity. Moreover, dc jumps persist in almost all the sensor cores although at a slower but still intolerable rate. Even if dc jumps could be easily setected in a shielded environment, when deployed in the presence of natural noise and clutter, it can be hard to positively detect ...
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  • Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks

    • Tác giả: Chen, Yuanyuan;  Người hướng dẫn: -;  Người tham gia: Lv, Yisheng; Wang, Fei-Yue (2019)

  • Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently proposed method of using synthetic and real data for data mining and data-driven process, in which we apply GANs to generate synthetic traffic data. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: 1) using the real data or the corrupt...
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  • Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G

    • Tác giả: Strodthoff, N.;  Người hướng dẫn: -;  Người tham gia: Göktepe,B.; Schierl, T.; Hellge, C.; Samek, W. (2019)

  • We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and lowlatency communication (URLLC). To this end, we propose machine learning methods to predict the outcome of the decoding process ahead of the end of the transmission. We discuss different input features and classification algorithms ranging from traditional methods to newly developed supervised autoencoders. These methods are evaluated based on their prospects of complying with the URLLC requirements of effective block error rates below 10 at small latency overheads. We provide realistic performance estimates in a system model incorporating scheduling effects to demonstrate the feasibility of E-HARQ across different signal-to-n...
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