1,209 research outputs found
Horizontal and vertical distribution of Atlantic Salmon (Salmo salar) in semi-closed cage system
Masteroppgave i havbruk - Universitetet i Nordland, 201
Contention resolution in wi-fi 6-enabled internet of things based on deep learning
Internet of Things (IoT) is expected to vastly increase the number of connected devices. As a result, a multitude of IoT devices transmit various information through wireless communication technology, such as the Wi-Fi technology, cellular mobile communication technology, low-power wide-area network (LPWAN) technology. However, even the latest Wi-Fi technology is still ready to accommodate these large amounts of data. Accurately setting the contention window (CW) value significantly affects the efficiency of the Wi-Fi network. Unfortunately, the standard collision resolution used by IEEE 802.11ax networks is nonscalable; thus, it cannot maintain stable throughput for an increasing number of stations, even when Wi-Fi 6 has been designed to improve performance in dense scenarios. To this end, we propose a CW control strategy for Wi-Fi 6 systems. This strategy leverages deep learning to search for optimal configuration of CW under different network conditions. Our deep neural network is trained by data generated from a Wi-Fi 6 simulation system with some varying key parameters, e.g., the number of nodes, short interframe space (SIFS), distributed interframe space (DIFS), and data transmission rate. Numerical results demonstrated that our deep learning scheme could always find the optimal CW adjustment multiple by adaptively perceiving the channel competition status. The finalized performance of our model has been significantly improved in terms of system throughput, average transmission delay, and packet retransmission rate. This makes Wi-Fi 6 better adapted to the access of a large number of IoT devices. © 2014 IEEE
Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs
Distributed online learning has been proven extremely effective in solving
large-scale machine learning problems over streaming data. However, information
sharing between learners in distributed learning also raises concerns about the
potential leakage of individual learners' sensitive data. To mitigate this
risk, differential privacy, which is widely regarded as the "gold standard" for
privacy protection, has been widely employed in many existing results on
distributed online learning. However, these results often face a fundamental
tradeoff between learning accuracy and privacy. In this paper, we propose a
locally differentially private gradient tracking based distributed online
learning algorithm that successfully circumvents this tradeoff. We prove that
the proposed algorithm converges in mean square to the exact optimal solution
while ensuring rigorous local differential privacy, with the cumulative privacy
budget guaranteed to be finite even when the number of iterations tends to
infinity. The algorithm is applicable even when the communication graph among
learners is directed. To the best of our knowledge, this is the first result
that simultaneously ensures learning accuracy and rigorous local differential
privacy in distributed online learning over directed graphs. We evaluate our
algorithm's performance by using multiple benchmark machine-learning
applications, including logistic regression of the "Mushrooms" dataset and
CNN-based image classification of the "MNIST" and "CIFAR-10" datasets,
respectively. The experimental results confirm that the proposed algorithm
outperforms existing counterparts in both training and testing accuracies.Comment: 21 pages, 4 figure
A Global Optimization Algorithm for Generalized Quadratic Programming
We present a global optimization algorithm for solving generalized quadratic programming (GQP), that is, nonconvex quadratic programming with nonconvex quadratic constraints. By utilizing a new linearizing technique, the initial nonconvex programming problem (GQP) is reduced to a sequence of relaxation linear programming problems. To improve the computational efficiency of the algorithm, a range reduction technique is employed in the branch and bound procedure. The proposed algorithm is convergent to the global minimum of the (GQP) by means of the subsequent solutions of a series of relaxation linear programming problems. Finally, numerical results show the robustness and effectiveness of the proposed algorithm
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Inferring Document Readability by Integrating Eye Movement Features
Capturing user’s emotional state is an emerging way for implicit relevance feedback in information retrieval (IR). Recently, EEG-based emotion recognition has drawn increasing attention. However, a key challenge is effective learning of useful features from EEG signals. In this paper, we present our on-going work on using Deep Belief Network (DBN) to automatically extract high-level features from raw EEG signals. Our preliminary experiment on the DEAP dataset shows that the learned features perform comparably to the use of manually generated features for emotion recognition
Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions
Temporal and numerical expression understanding is of great importance in
many downstream Natural Language Processing (NLP) and Information Retrieval
(IR) tasks. However, much previous work covers only a few sub-types and focuses
only on entity extraction, which severely limits the usability of identified
mentions. In order for such entities to be useful in downstream scenarios,
coverage and granularity of sub-types are important; and, even more so,
providing resolution into concrete values that can be manipulated. Furthermore,
most previous work addresses only a handful of languages. Here we describe a
multi-lingual evaluation dataset - NTX - covering diverse temporal and
numerical expressions across 14 languages and covering extraction,
normalization, and resolution. Along with the dataset we provide a robust
rule-based system as a strong baseline for comparisons against other models to
be evaluated in this dataset. Data and code are available at
\url{https://aka.ms/NTX}.Comment: Technical Repor
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