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Accurate estimation of detection/classification performance for sidescan sonar systems in Mine Counter-Measure (MCM) applications is important for informing mission tactics and adapting autonomous behaviors. The approach presented in this paper assumes that detection/classification performance can be estimated solely from historical data collected from similar surveys. This paper introduces an algorithm...
Arctic coastal morphology is increasingly affected by changes to the climate. As the season length for shorefast ice decreases and temperatures warm permafrost, coastlines are increasingly susceptible to erosion from storm waves. Such coastal erosion is significant since the majority of the population centers and infrastructure in the Arctic are located near the coasts. Stakeholders and decision makers...
Reading is one of the main paths to acquire knowledge, either done traditionally on paper media or practiced on electronic devices. Efficiency varies when different reading patterns are involved. It is the objective of this research to classify reading patterns from fixation data using machine learning techniques in an attempt to understand and evaluate the reading and learning process. In our experiment,...
The recent advance in deep learning technologies has provided many opportunities to the industries in developing smarter products, such as smart toys, smart cars and smart homes. Unfortunately, a common practical issue to these deep learning methods is the high requirements in computing power even for the prediction part. As the deep learning models' complexities increase, the memory requirements...
This paper extended Stacked Denoising Autoencoder to build a deep neural network which initialized the weight of neural network through the encoder's weight and used Dropout to reduce the error rate in fine-tuning stage. The neural network used the information of students in recent years as input data to train neural network, and predicted the possibility of dropout on the students during the semester...
The task of visual relationship recognition (VRR) is recognizing multiple objects and their relationships in an image. A fundamental difficulty of this task is class-number scalability, since the number of possible relationships we need to consider causes combinatorial explosion. Another difficulty of this task is modeling how to avoid outputting semantically redundant relationships. To overcome these...
The paper presents research in usability of web technologies for implementation of machine learning and clustering algorithms into embedded systems. The research work is divided into two main parts. The first part is devoted to designing backend system with fast C++ application for learning execution model. The second part of application is frontend based web application with PHP and AJAX to provide...
Facial micro-expression refers to split-second muscle changes in the face, indicating that a person is either consciously or unconsciously suppressing their true emotions and even mental health. Therefore, micro-expression recognition attracts increasing research efforts in both fields of psychology and computer vision. Existing research on micro-expression recognition has mainly used hand-crafted...
Image compression plays more and more important role in image processing. Image sparse coding with learned over-complete dictionaries shows promising results on image compression by representing images with dictionary atoms compactly. Within the sparse coding based compression framework, a sparse dictionary is first learned from training images in a predefined image library, and then an image is compressed...
Stochastic Gradient Descent (SGD) is the method of choice for large scale problems, most notably in deep learning. Recent studies target improving convergence and speed of the SGD algorithm. In this paper, we equip the SGD algorithm and its advanced versions with an intriguing feature, namely handling constrained problems. Constraints such as orthogonality are pervasive in learning theory. Nevertheless...
A multi-view multi-target correspondence framework employing deep learning on overlapping cameras for identity-aware tracking in the presence of occlusion is proposed. Our complete pipeline of detection, multi-view correspondence, fusion and tracking, inspired by AI greatly improves person correspondence across multiple wide-angled views over traditionally used features set and handcrafted descriptors...
Image classification is one of the critical tasks in hyperspectral remote sensing. In recent years, significant improvement have been achieved by various classification methods. However, mixed spectral responses from different ground materials still create confusions in complex scenes. In this regard, unmixing approaches are being successfully carried out to decompose mixed pixels into a collection...
Optical Character Recognition (OCR) in the scanned documents has been a well-studied problem in the past. However, when these characters come from the natural scenes, it becomes a much more challenging problem, as there exist many difficulties in these images, e.g., illumination variance, cluttered backgrounds, geometry distortion. In this paper, we propose to use a deep learning method that based...
This paper presents a novel local posture orientation-context descriptor, and proposes a FDDL(Fisher discriminant dictionary learning) method based on local orientation-preserving(LOP-FDDL) for sparse coding in action recognition task. To take full use of the information about the position of the local body-part related to the center of the torso, ant the spatial-temporal shape changes of the human...
This study explored the hidden biomedical information from knee MR images for osteoarthritis prediction. We have computed the Cartilage Damage Index (CDI) information from 36 informative locations on tibiofemoral compartment from 3D MR imaging reconstruction and used PCA analysis to process the feature set. The processed feature set and original raw feature set were severed as input to four machine...
Many rare and common genetic variants, including SNPs and CNVs, are reported to be associated with mental disorders, yet more remain to be discovered. However, despite the large amount of high-throughput genomics data, there is a lack of integrative methods to systematically prioritize variants that confer susceptibility to mental disorders in personal genomes. Here, we developed a computational tool:...
Ligand binding site prediction from protein structure plays an important role in various complex rational drug design efforts. Its applications include drug side effects prediction, docking prioritization in inverse virtual screening and elucidation of protein function in genome wide structural studies. Currently available tools have limitations that disqualify them from many possible use cases. In...
Molecularly targeted therapies significantly contribute to the efforts of personalized approaches for cancer diagnosis and chemotherapeutic treatment. One of a critical step to identify target molecules is to determine the most representative features for different patient's sub-groups. Breast cancer, one of the most heterogeneous cancer has five main subtypes, so accurately identify gene signatures...
In recent years, most breakthroughs in fields such as image and video processing were based on machine learning technologies that allow computers to recognize objects in images with nearly human precision. In some application domains, computers even surpassed human level performance. These breakthroughs result from an exponential increase of computational resources and digitization of society (massive...
Exponential growth in electronic health record (EHR) data has resulted in new opportunities and urgent needs to discover meaningful data-driven representations and patterns of diseases, i.e., computational phenotyping. Recent success and development of deep learning provides promising solutions to the problem of prediction and feature discovery tasks, while lots of challenges still remain and prevent...
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