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In this paper, we proposed an approach for super-resolution land cover mapping on remote sensing images based on the deep learning technique, namely Convolutional Neural Network (CNN) by combining with the level set method (LSM). Here, the CNN is used to find the probabilities that a subpixel belonging to a land cover class, and the LSM is employed to fine tune the boundaries among land cover classes...
Bird sounds have been studied in recent years due to their significance in helping ornithologists, and ecologists to monitor birds activities, which reflect climate changes, biodiversity, and reserves local protection status. Within the increasingly collected large amount of bird sound data from experts and amateurs, how to handle, and employ the state-of-the-art deep learning methods to mining such...
The method presented extends a given regression neural network to make its performance improve. The modification affects the learning procedure only, hence the extension may be easily omitted during evaluation without any change in prediction. It means that the modified model may be evaluated as quickly as the original one but tends to perform better. This improvement is possible because the modification...
Deep learning has recently gained popularity in many machine learning applications, but a theoretical grounding for the strengths, weaknesses, and implicit biases of various deep learning methods is still a work in progress. Here, we analyze the role of spatial locality in Deep Belief Networks (DBN) and show that spatially local information is lost through diffusion as the network becomes deeper....
The study of Restricted Boltzmann Machine(RBM) attracts considerable attentions in recent years. RBM training algorithm is an unsupervised learning method with many applications, moreover, it is the basic module in deep learning. Maximizing the log-likelihood by gradient ascent method, RBM training algorithm can approximate the probability distribution underlying the observing data. For a simple RBM...
In a communication network, automatic short message service (SMS) spammer detection is a big challenge for a telecommunication operator nowadays, especially with the development of the rich communication services (RCS). Three main problems exist in the areas of research and real practice. They are (1) the whole-volume content based SMS spam detection techniques cannot be easily used on the side of...
Deep learning is a new era of machine learning research, where many layers of information processing stages are exploited for unsupervised feature learning. Using multiple levels of representation and abstraction, it helps a machine to understand about data (e.g., images, sound and text) more accurately. Many deep learning models have been proposed for solving the problem of different applications...
Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. Researchers with the help of machine learning algorithms normally find the best classifier that distinguishes a spam from a benign mail called ham. It is necessary to evaluate the performance of any new spam classifier using standard data sets. The public corpora of email data sets that are available...
Security levels used in organizations today are typically course-grained, broad and distinct, using security levels such as "Confidential" and Secret". However, current research is advocating a move towards more fine-grained security models, e.g. Such as Attribute-Based Access Control, where information objects and end-users are characterized in terms of complex meta-data. One idea...
Current works on differentiating between posed and spontaneous facial expressions usually use features that are handcrafted for expression category recognition. Till now, no features have been specifically designed for differentiating between posed and spontaneous facial expressions. Recently, deep learning models have been proven to be efficient for many challenging computer vision tasks, and therefore...
One possible solution of the investigation of the cell fate decision and its function is the study of cell morphology. Bright-field imaging analysis allow us to use a labeling free and non-invasive approach to measure the morphological dynamics during cellular reprogramming, which includes induced pluripotent stem cells (iPSCs), and trans-differentiated neural progenitor cells (NPCs) from somatic...
As part of a human-robot interaction project, the gestural modality is one of many ways to communicate. In order to develop a relevant gesture recognition system associated to a smart home butler robot, our methodology is based on an IQ game-like Wizard of Oz experiment to collect spontaneous and implicitly produced gestures in an ecological context where the robot is the referee. These gestures are...
Email communication carrying malicious attachments or links is often used as an attack vector for initial penetration of the targeted organization. Existing defense solutions prevent executables from entering organizational networks via emails, therefore recent attacks tend to use non-executable files such as PDF. Machine learning algorithms have recently been applied for detecting malicious PDF files...
Email classification is an important topic in literature attempting to correctly classify user emails and filter out spam emails. In this paper, we identify some challenges regarding this topic and propose an effective email classification model based on both data reduction and disagreement-based semi-supervised learning. In particular, the main objective of the data reduction is to select an optimum...
Computer-based medical systems play a very important role in medical applications because they can strongly support the physicians in the decision making process. The large amount of data nowadays available, although collected from high quality sources, usually contain irrelevant, redundant, or noisy information, suggesting that not all the training instances are useful for the classification task...
In this paper, new active learning methods are proposed to filter Chinese spam. It is time-consuming and expensive to label the spam emails in the large datasets. Active learning methods can conspicuously reduce labeling cost by identifying informative examples and speed up online Logistic Regression filter. The experiments illustrate that our methods not only decrease the number of label requests,...
This paper proposes a system of using machine learning algorithms to extract communication session information, such as conference bridge number and participant code, from users' emails or appointments. Our system can then use the retrieved information to easily setup a communication session, for example, dialing conference bridge number and participant code, as well as popping up web conference links...
The increased number of documents in digital format available on the Web and its useful information for different purposes entail an essential need to organize them. However, this task must be automated in order to save costs and manpower. In the community research, the main approach to face this problem is based on the application of machine learning techniques. This article studies the main machine...
Anti-spam technology always employs machine learning to identify spam emails. Unfortunately, the email samples used to establish machine learning models are always not in a ideal status: there are too many spam emails compared with normal ones, which may lead to biased machine learning models and unsatisfactory performance in prediction. Besides, there are too many email samples, which lead to unaffordable...
In internet era, though emails turn into one of the most popular way for communication, spam emails also bother people seriously. As a result, research on email filtering has become a hot topic with much effort put into this area. Unfortunately, in the real-world application, the large-scale training email dataset which differs from the assumption made in experiment challenges both efficiency and...
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