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关键词： Training   Feature extraction   Redundancy   Quantization (signal)   Computer science   Amplitude modulation   Cameras   Person re-identification   hashing   self distillation   mutual learning  

摘要： Person Re-identification (ReID) aims to retrieve the pedestrian with the same identity across different views. Existing studies mainly focus on improving accuracy, while ignoring their efficiency. Recently, several hash based methods have been proposed. Despite their improvement in efficiency, there still exists an unacceptable gap in accuracy between these methods and real-valued ones. Besides, few attempts have been made to simultaneously explicitly reduce redundancy and improve discrimination of hash codes, especially for short ones. Integrating Mutual learning may be a possible solution to reach this goal. However, it fails to utilize the complementary effect of teacher and student models. Additionally, it will degrade the performance of teacher models by treating two models equally. To address these issues, we propose a salience-guided iterative asymmetric mutual hashing (SIAMH) to achieve high-quality hash code generation and fast feature extraction. Specifically, a salience-guided self-distillation branch (SSB) is proposed to enable SIAMH to generate hash codes based on salience regions, thus explicitly reducing the redundancy between codes. Moreover, a novel iterative asymmetric mutual training strategy (IAMT) is proposed to alleviate drawbacks of common mutual learning, which can continuously refine the discriminative regions for SSB and extract regularized dark knowledge for two models as well. Extensive experiment results on five widely used datasets demonstrate the superiority of the proposed method in efficiency and accuracy when compared with existing state-of-the-art hashing and real-valued approaches. The code is released at https://***/Vill-Lab/SIAMH.

关键词： Computer science   Artificial intelligence  

摘要： Sign language is the primary form of communication among Deaf and Hard of Hearing(DHH) individuals. Due to the absence of speaking capability, voice-controlled assistants such as Apple Siri or Amazon Alexa are not readily available to a DHH individual. An automated sign language recognizer can work as an interface between a DHH individual and the voice-controlled digital devices. Recognizing world-level sign gestures is the first step of an automated sign language recognition system. These gestures are characterized by fast, highly articulate motion of the upper body, including arm movements with complex hand shapes. The primary challenge of a world-level sign language recognizer (WLSLR) is to capture the hand shapes and their motion components. Additional challenges arise due to the resolution of the available video, differences in the gesture speed, and large variations in the gesture performing style across individual subjects. In this dissertation, we study different methods with the goal of improving video-based WLSLR systems. Towards this goal, we introduced a multi-modal American Sign Language (ASL) dataset,GMU-ASL51. This publicly available dataset features multiple modalities and 13; 107 word-level ASL sign videos. We implemented machine learning methods using only video input and a fusion of videos and body pose data. Usually, word-level sign videos have a varying number of frames, roughly ranging from 10 to 200, based on the source and type of the sign videos. To utilize the frame-wise representation of hand shapes, we implemented Recurrent Neural Network (RNN) models using per-frame hand-shape features extracted from a pre-trained Convolutional Neural Network (CNN). To further improve hand-shape representation, we proposed a hand-shape annotation method. This method can quickly annotate hand-shape images and simultaneously train a CNN model. We later used this model as a hand-shape feature extractor for the downstream sign recognition task. Most of the i

关键词： Computer science   Engineering  

关键词： Applied mathematics   Computer science   Artificial intelligence  

关键词： Electrical engineering   Computer science   Artificial intelligence   Robotics  

摘要： Object Detection is a core computer vision problem and can facilitate many image understanding problems. Object Detection has witnessed significant progress in the past decade especially after deep learning is successfully applied to this field. Most of the detection models focus on generalizing the trained model to unseen samples of the seen classes. However, we argue that generalizing the detection model to unseen classes is also important in applications. In many emerging applications like robotics exploration and autonomous driving, the detection models are usually deployed on the embedded system. When the systems are exposed to a new environment where it is desirable to detect new unseen classes, the embedded system does not support extra training. Therefore, it is desirable that the trained detectors can be generalized to unseen classes without extra training. In this dissertation, we focus on the object detector's generalization in two aspects: 1) generalization to unseen classes, and 2) generalization to unseen samples of seen classes. To generalize the detector to unseen classes, we present a generalized framework to model the relationship between classes. We propose to model joint visual and semantic relationships between classes so that the detector trained using seen class data can be generalized to detect unseen classes. Then, we study the challenges which hinder the detection model to generalize to unseen samples of seen classes and presents solutions to them. This dissertation presents a generalized framework to model relationships between different classes so that the model can be generalized to unseen classes. Different from most existing models which consider each class independently, the relationship between classes is modeled. In the proposed framework, each class is represented by a fixed-length vector which is also called prototype, and different classes may partially share the prototypes. So, different classes can help each other by learning t

关键词： Computer science   Artificial intelligence  

摘要： Networks have an increasing influence on our modern life, making Cybersecurity an important field of research. Cybersecurity techniques mainly focus on antivirus software, firewalls and intrusion detection systems (IDSs), etc. These techniques protect networks from both internal and external attacks. This research is composed of three different essays. It highlights and improves the applications of machine learning techniques in the Cybersecurity domain. Since the feature size and observations of the cyber incident data are increasing with the growth of internet usage, conventional defense strategies against cyberattacks are getting invalid most of the time. On the other hand, the applications of machine learning tasks are getting better consistently to prevent cyber risks in a timely manner. For the last decade, machine learning and Cybersecurity have converged to enhance risk elimination. Since the cyber domain knowledge and adopting machine learning techniques do not align on the same page in the case of deployment of data-driven intelligent systems, there are inconsistencies where it is needed to bridge the gap. We have studied the most recent research works in this field and documented the most common issues regarding the implementation of machine learning algorithms in Cybersecurity. According to these findings, we have conducted research and experiments to improve the quality of service and security strength by discovering new approaches.

关键词： Electrical engineering   Computer engineering   Computer science   Artificial intelligence   Multimedia communications  

摘要： Wireless cyber-physical systems (CPS) have been progressively adopted in many applications such as smart industrial control systems, intelligent vehicular transportation, unmanned aerial vehicles (UAV), etc. Despite the CPS huge potential benefits in paving the path to develop new applications, the open and broadcast nature of the wireless communication medium has made these systems vulnerable to cyber attacks. In this thesis, we propose a family of novel online machine learning algorithms which can be employed to defend against jamming attacks in wireless CPS, and wireless communication systems in general. In addition, we study the problem of fast detection and identification of intruding consumer UAVs and propose a new method which exploits wireless network traffic information and utilizes machine learning techniques to identify the UAVs in a timely manner. More specifically, in this thesis, we discuss four research projects which briefly are summarized as follows. 1) We study security of remote state estimation in wireless CPS where a sensor sends its measurements to the remote state estimator over a multi-channel wireless link in presence of a jamming attacker. We propose a novel online learning-based policy which can be employed by the sensor to jointly choose the transmission channel and power to defend against the attack. We theoretically prove that the proposed algorithm achieves a sublinear order-optimal learning regret bound in time. 2) We focus on the security of multi-channel wireless communication systems with a scenario in which the jammer always successfully attacks on the acknowledgment link and the transmitter loses throughput due to dynamic channel switching latency. We model this problem as self-unaware bandits with arm switching costs problem and propose two novel online learning algorithms with theoretical performance guarantees. We prove a sublinear regret upper bound for both algorithms and bound the switching costs such that it can improve th

关键词： Morphology   Chemistry   Analytical chemistry   Atomic physics   Biology   Computer science   Mathematics   Optics   Physics  

摘要： Infrared Matrix Assisted Laser Desorption Electrospray Ionization Mass Spectrometry Imaging (IR-MALDESI-MSI) is a hybrid of matrix assisted laser desorption with electrospray post ionization that enables high resolution spatial information to be combined with the ambient characteristics of electrospray. It has been used to complement other MSI techniques by analyzing samples and biomolecules that are not commonly accessible using the dominant techniques of matrix assisted laser desorption ionization (MALDI) and ESI. The yield fluctuation of artemisinin due to its glandular trichome (GT)-specific biosynthesis has remained an unsolved problem in meeting the global demand for this primary treatment for malaria. Detailed IR-MALDESI image profiling revealed that artemisinin, artemisinic acid, and arteannuin B three metabolites and dihydroartemisinin are localized in non-GT cells of leaves of inbred A. annua plants. Finding that non-GT cells can express the artemisinin biosynthetic pathway led to a revision of the current dogma of artemisinin biosynthesis in A. annua and may expedite innovation of novel metabolic engineering approaches for high and stable production of artemisinin. Quantitative MSI was performed on young Arabidopsis thaliana seedlings to understand how mutations affect metabolite accumulation in plant development. A new method for the quantification of auxin‐related compounds was created using stable‐ isotope‐labelled (SIL) indole‐3‐acetic acid (IAA) doped into agarose substrate. Relative quantification of auxin-related compounds in several A. thaliana genotypes was performed post‐acquisition by normalization to SIL‐IAA in the agarose. SIL-IAA added to agarose was found to remain stable, with repeatability and abundance features comparable to those of other compounds previously used for relative quantification in IR‐ MALDESI analyses. Acetonitrile (ACN)/water and methanol (MeOH)/water solvent gradients from 5 to 95% organic solvent were varied across 2.25

关键词： Principal components analysis   Deep learning   Algorithms   Clustering   Sentiment analysis   Ablation   Crowdsourcing   Visualization   Image retrieval   Artificial intelligence   Computer science  

摘要： In recent years, deep learning has shown tremendous success in different applications, however these modes mostly need a large labeled dataset for training their parameters. In this work, we aim to explore the potentials of efficient learning frameworks for training deep models on different problems in the case of limited supervision or noisy labels. For the image clustering problem, we introduce a new deep convolutional autoencoder with an unsupervised learning framework. We employ a relative entropy minimization as the clustering objective regularized by the frequency of cluster assignments and a reconstruction loss. In the case of noisy labels obtained by crowdsourcing platforms, we proposed a novel deep hybrid model for sentiment analysis of text data like tweets based on noisy crowd labels. The proposed model consists of a crowdsourcing aggregation model and a deep text autoencoder. We combine these sub-models based on a probabilistic framework rather than a heuristic way, and derive an efficient optimization algorithm to jointly solve the corresponding problem. In order to improve the performance of unsupervised deep hash functions on image similarity search in big datasets, we adopt generative adversarial networks to propose a new deep image retrieval model, where the adversarial loss is employed as a data-dependent regularization in our objective function. We also introduce a balanced self-paced learning algorithm for training a GAN-based model for image clustering, where the input samples are gradually included into training from easy to difficult, while the diversity of selected samples from all clusters are also considered. In addition, we explore adopting discriminative approaches for unsupervised visual representation learning rather than the generative algorithms, such as maximizing the mutual information between an input image and its representation and a contrastive loss for decreasing the distance between the representations of original and augmente

关键词： Computer science   Artificial intelligence   Mathematics  

摘要： Machine learning approaches are widely used in natural language analysis. Previous research has shown that similar techniques can be applied in the analysis of computer programming (artificial) languages. In this thesis, we focus on identifying the authors of computer programs by using machine learning techniques. We extend these techniques to determine which features capture the writing style of authors in the classification of a computer program according to the author's identity. We then propose a novel approach for computer program author identification. In this method, program features from the text documents are combined with authors' sociological features (gender and region) to develop the classification model. Several experiments have been conducted on two datasets composed of computer programs written in C++, and the results are encouraging. According to the experimental results, the author's identity can be predicted with a 75% accuracy rate.