Simulation And Analysis For Gaussian Jitter Effects Of Geosynchronous Satellite,
2021
1. School of Electronics and Information Engineering, Beihang University, Beijing 100191, China; ;2. Key Laboratory of Electronics and Information Technology for Complex Aerospace System, National Space Science Center, Chinese Academy of Sciences, Beijing 101499, China;
Simulation And Analysis For Gaussian Jitter Effects Of Geosynchronous Satellite, Yulun Li, Yang Wei, Jiayi Guo
Journal of System Simulation
Abstract: Ultrahigh-spatial-resolution and high-temporal-revisit appear to be realizable for future space-borne imaging missions. By taking advantage of satellites on the geosynchronous orbit, the wide-area continuous monitoring capacity to ground target zones becomes possible. Focus on the imaging target displacement, it is necessary to study the platform jitter during the conceptual modeling phase. Discrete Event System Specification (DEVS) is employed to abstract the observation area as an event generator. The attitude stabilization is digitalized and both theoretical analysis and simulation experiments of Gaussian-excited signals are provided. Simulation results show that the distributions of attitude responses preserve the normality. The offset distance …
Three-Dimensional Adaptive Neural Network Guidance Law Against Maneuvering Targets,
2021
Beijing Institute of Electronic System Engineering, Beijing 100854, China;
Three-Dimensional Adaptive Neural Network Guidance Law Against Maneuvering Targets, Yujie Si, Xiong Hua, Zhe Li
Journal of System Simulation
Abstract: The problem of designing a three-dimensional nonlinear guidance law accounting for saturation nonlinearity is concentrated to attack maneuvering targets. To solve the physical constraints of missile actuators, an anti-disturbance and anti-saturation terminal sliding mode guidance law is provided based on radial basis functions neural networks and adaptive method. The guidance law is bounded and ensures that the system state is uniformly ultimately bounded. Compared with the traditional anti-saturation guidance law, it has the advantages of fast convergence speed and high precision. Numerical simulations are introduced to demonstrate the effectiveness and superiority of the designed composite guidance law in theory.
Negative Influence Minimization Algorithm For Social Networks,
2021
1. College of Command Control Engineer, Army Engineering University, Nanjing 211117, China; ;
Negative Influence Minimization Algorithm For Social Networks, Yang Yi, Chunxiao Wu, He Ming, Zhou Bo
Journal of System Simulation
Abstract: While positive information is spreading in social networks, there is still a large amount of negative information spreading in the network. Aiming at the fact that there is few researches on suppressing the spread of negative information, a negative influence minimization algorithm for social networks is proposed. When negative information appears in social networks and some initial nodes are infected, the behavior of nodes propagating information depends on its coordination game with neighbor nodes. The objective function with minimal influence is used to find the K optimal blocking nodes, and finally the size of the final infected node is …
Improving Robot Darwinian Particle Swarm Optimization Using Quantum-Behaved Swarm Theory For Robot Exploration And Communication,
2021
Universiti Malaya
Improving Robot Darwinian Particle Swarm Optimization Using Quantum-Behaved Swarm Theory For Robot Exploration And Communication, Duaa Mehiar
Student Works (2020-2029)
Despite the significance of the Robotic Darwinian Particle Swarm Optimization (RDPSO) algorithm on swarm-robot exploration and communication, there remain notable gaps such as premature and slow convergence, collisions between robots, and communication breaks and constraints. The quantum computing theory has several advantages that can improve the searching capabilities of PSO-based algorithms. However, there has yet an attempt to adopt quantum behaviour onto robot-based system such as the RDPSO. In this study, a new algorithm called the Quantum Robot Darwinian Particle Swarm Optimization (QRDPSO) is contributed with the hypothesis that quantum behaving particles can address the RDPSO main gaps; i.e. to …
Deep Learning-Based Breast Cancer Detection And Classification Using Histopathology Images,
2021
Universiti Malaya
Deep Learning-Based Breast Cancer Detection And Classification Using Histopathology Images, Ghulam Murtaza
Student Works (2020-2029)
Cancer disease is drastically increasing worldwide over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of abnormal deaths. For a confident diagnosis of BrC, histopathology (Hp) images are usually suggested by the doctors. BrC detection is a diagnostic test for benign (non-cancerous) and malignant (cancerous) breast tumors (BrT). Once the BrT is diagnosed, then it needs to be classified for subtypes of benign and malignant to start specific treatment. Several studies developed BrC detection and classification models using Hp images. However, the existing models required high computational resources, long training …
Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning Via Importance Sampling,
2021
Beijing University of Posts and Telecommunications
Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning Via Importance Sampling, Yugang Ji, Mingyang Yin, Hongxia Yang, Jingren Zhou, Vincent W. Zheng, Chuan Shi, Yuan Fang
Research Collection School Of Computing and Information Systems
In real-world problems, heterogeneous entities are often related to each other through multiple interactions, forming a Heterogeneous Interaction Graph (HIG in short). While modeling HIGs to deal with fundamental tasks, graph neural networks present an attractive opportunity that can make full use of the heterogeneity and rich semantic information by aggregating and propagating information from different types of neighborhoods. However, learning on such complex graphs, often with millions or billions of nodes, edges, and various attributes, could suffer from expensive time cost and high memory consumption. In this paper, we attempt to accelerate representation learning on large-scale HIGs by adopting …
Role Of Artificial Intelligence In The Internet Of Things (Iot) Cybersecurity,
2021
Old Dominion University
Role Of Artificial Intelligence In The Internet Of Things (Iot) Cybersecurity, Murat Kuzlu, Corinne Fair, Ozgur Guler
Engineering Technology Faculty Publications
In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these …
3d Object Detection, Instance Segmentation And Classification From 3d Range And 2d Color Images,
2021
CUNY Graduate Center
3d Object Detection, Instance Segmentation And Classification From 3d Range And 2d Color Images, Xiaoke Shen
Dissertations, Theses, and Capstone Projects
We address the problem of 3D object detection and instance segmentation by proposing a novel object segmentation and detection system. First, we detect 2D objects based on RGB, Depth only, or RGB-D images. A 3D convolutional-based system, named Frustum VoxNet, is proposed. This system 1) generates frustums from 2D detection results, 2) proposes 3D candidate voxelized images for each frustum, and uses a 3D convolutional neural network (CNN) based on these candidates voxelized images to perform the 3D instance segmentation and object detection. Although the volumetric data representation is widely used for 3D object classification, there are fewer works on …
A New Feature Selection Method Based On Class Association Rule,
2021
CUNY Graduate Center
A New Feature Selection Method Based On Class Association Rule, Sami A. Al-Dhaheri
Dissertations, Theses, and Capstone Projects
Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may …
Speech Enhancement Using Speech Synthesis Techniques,
2021
CUNY Graduate Center
Speech Enhancement Using Speech Synthesis Techniques, Soumi Maiti
Dissertations, Theses, and Capstone Projects
Traditional speech enhancement systems reduce noise by modifying the noisy signal to make it more like a clean signal, which suffers from two problems: under-suppression of noise and over-suppression of speech. These problems create distortions in enhanced speech and hurt the quality of the enhanced signal. We propose to utilize speech synthesis techniques for a higher quality speech enhancement system. Synthesizing clean speech based on the noisy signal could produce outputs that are both noise-free and high quality. We first show that we can replace the noisy speech with its clean resynthesis from a previously recorded clean speech dictionary from …
Norm-Based Generalisation Bounds For Deep Multi-Class Convolutional Neural Networks,
2021
Singapore Management University
Norm-Based Generalisation Bounds For Deep Multi-Class Convolutional Neural Networks, Antoine Ledent, Waleed Mustafa, Yunwen Lei, Marius Kloft
Research Collection School Of Computing and Information Systems
We show generalisation error bounds for deep learning with two main improvements over the state of the art. (1) Our bounds have no explicit dependence on the number of classes except for logarithmic factors. This holds even when formulating the bounds in terms of the Frobenius-norm of the weight matrices, where previous bounds exhibit at least a squareroot dependence on the number of classes. (2) We adapt the classic Rademacher analysis of DNNs to incorporate weight sharing—a task of fundamental theoretical importance which was previously attempted only under very restrictive assumptions. In our results, each convolutional filter contributes only once …
Fine-Grained Generalization Analysis Of Vector-Valued Learning,
2021
Singapore Management University
Fine-Grained Generalization Analysis Of Vector-Valued Learning, Liang Wu, Antoine Ledent, Yunwen Lei, Marius Kloft
Research Collection School Of Computing and Information Systems
Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although there is some generalization analysis on different specific algorithms under the empirical risk minimization principle, a unifying analysis of vector-valued learning under a regularization framework is still lacking. In this paper, we initiate the generalization analysis of regularized vector-valued learning algorithms by presenting bounds with a mild dependency on the output dimension and a fast rate on the sample size. Our discussions relax the existing assumptions on the restrictive constraint of hypothesis spaces, smoothness of loss …
Model Uncertainty Guides Visual Object Tracking,
2021
Singapore Management University
Model Uncertainty Guides Visual Object Tracking, Lijun Zhou, Antoine Ledent, Qintao Hu, Ting Liu, Jianlin Zhang, Marius Kloft
Research Collection School Of Computing and Information Systems
Model object trackers largely rely on the online learning of a discriminative classifier from potentially diverse sample frames. However, noisy or insufficient amounts of samples can deteriorate the classifiers' performance and cause tracking drift. Furthermore, alterations such as occlusion and blurring can cause the target to be lost. In this paper, we make several improvements aimed at tackling uncertainty and improving robustness in object tracking. Our first and most important contribution is to propose a sampling method for the online learning of object trackers based on uncertainty adjustment: our method effectively selects representative sample frames to feed the discriminative branch …
Relative And Absolute Location Embedding For Few-Shot Node Classification On Graph,
2021
Singapore Management University
Relative And Absolute Location Embedding For Few-Shot Node Classification On Graph, Zemin Liu, Yuan Fang, Chenghao Liu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Node classification is an important problem on graphs. While recent advances in graph neural networks achieve promising performance, they require abundant labeled nodes for training. However, in many practical scenarios there often exist novel classes in which only one or a few labeled nodes are available as supervision, known as few-shot node classification. Although meta-learning has been widely used in vision and language domains to address few-shot learning, its adoption on graphs has been limited. In particular, graph nodes in a few-shot task are not independent and relate to each other. To deal with this, we propose a novel model …
Terrace-Based Food Counting And Segmentation,
2021
Singapore Management University
Terrace-Based Food Counting And Segmentation, Huu-Thanh Nguyen, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper represents object instance as a terrace, where the height of terrace corresponds to object attention while the evolution of layers from peak to sea level represents the complexity in drawing the finer boundary of an object. A multitask neural network is presented to learn the terrace representation. The attention of terrace is leveraged for instance counting, and the layers provide prior for easy-to-hard pathway of progressive instance segmentation. We study the model for counting and segmentation for a variety of food instances, ranging from Chinese, Japanese to Western food. This paper presents how the terrace model deals with …
Adversarial Meta Sampling For Multilingual Low-Resource Speech Recognition,
2021
Singapore Management University
Adversarial Meta Sampling For Multilingual Low-Resource Speech Recognition, Yubei Xiao, Ke Gong, Pan Zhou, Guolin Zheng, Xiaodan Liang, Liang Lin
Research Collection School Of Computing and Information Systems
Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledgegrounded dialogue systems often require a large number of dialogue instances to learn as they fail to capture the correlations between different diseases and neglect the diagnostic experience shared among them. To address this issue, we propose a more natural and practical paradigm, i.e., low-resource medical dialogue generation, which can transfer the diagnostic experience from source diseases to target ones with a handful of data for adaptation. It is capitalized on a commonsense knowledge graph to characterize the …
Scalable Verification Of Quantized Neural Networks,
2021
Singapore Management University
Scalable Verification Of Quantized Neural Networks, Thomas A. Henzinger, Mathias Lechner, Dorde Zikelic
Research Collection School Of Computing and Information Systems
Formal verification of neural networks is an active topic of research, and recent advances have significantly increased the size of the networks that verification tools can handle. However, most methods are designed for verification of an idealized model of the actual network which works over real arithmetic and ignores rounding imprecisions. This idealization is in stark contrast to network quantization, which is a technique that trades numerical precision for computational efficiency and is, therefore, often applied in practice. Neglecting rounding errors of such low-bit quantized neural networks has been shown to lead to wrong conclusions about the network’s correctness. Thus, …
Deep Learning For Multi-Tissue Cancer Classification Of Gene Expressions,
2021
American University in Cairo
Deep Learning For Multi-Tissue Cancer Classification Of Gene Expressions, Tarek Khorshed
Theses and Dissertations
We contribute in saving the lives of cancer patients through early detection and diagnosis, since one of the major challenges in cancer treatment is that patients are diagnosed at very late stages when appropriate medical interventions become less effective and full curative treatment is no longer achievable. Cancer classification using gene expressions is extremely challenging given the complexity and high dimensionality of the data. Current classification methods typically rely on samples collected from a single tissue type and perform a prerequisite of gene feature selection to avoid processing the full set of genes. These methods fall short in taking advantage …
Efficient Cnn Building Blocks For Encrypted Data,
2021
International Institute of Information Technology & IBM Systems
Efficient Cnn Building Blocks For Encrypted Data, Nayna Jain, Karthik Nandakumar, Nalini K. Ratha, Sharath U. Pankanti, Uttam Kumar
Computer Vision Faculty Publications
Machine learning on encrypted data can address the concerns related to privacy and legality of sharing sensitive data with untrustworthy service providers, while leveraging their resources to facilitate extraction of valuable insights from otherwise non-shareable data. Fully Homomorphic Encryption (FHE) is a promising technique to enable machine learning and inferencing while providing strict guarantees against information leakage. Since deep convolutional neural networks (CNNs) have become the machine learning tool of choice in several applications, several attempts have been made to harness CNNs to extract insights from encrypted data. However, existing works focus only on ensuring data security and ignore security …
Generative Multi-Label Zero-Shot Learning,
2021
Inception Institute of Artificial Intelligence
Generative Multi-Label Zero-Shot Learning, Akshita Gupta, Sanath Narayan, Salman Khan, Fahad Shahbaz Khan, Ling Shao, Joost Van De Weijer
Computer Vision Faculty Publications
Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of …
