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Visual tracking using sparse representation
, Article 2012 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2012, 12 December 2012 through 15 December 2012, Ho Chi Minh City ; 2012 , Pages 304-309 ; 9781467356060 (ISBN) ; Jourabloo, A ; Jamzad, M ; Manzuri Shalmani, M. T ; Sharif University of Technology
2012
Abstract
In this work we present a sparse dictionary learning method, specifically tuned to solve the tracking problem. Recently, sparse representation has drawn much attention because of its genuineness and strong mathematical background. In this paper we present an online method for dictionary learning which is desirable for problems such as tracking. Online learning methods are preferable because the whole data are not available at the current time. The presented method tries to use the advantages of the generative and discriminative models to achieve better performance. The experimental results show our method can overcome many tracking challenges
Incremental RotBoost algorithm: An application for spam filtering
, Article Intelligent Data Analysis ; Volume 19, Issue 2 , April , 2015 , Pages 449-468 ; 1088467X (ISSN) ; Beigy, H ; Sharif University of Technology
IOS Press
2015
Abstract
Incremental learning is a learning algorithm that can get new information from new training sets without forgetting the acquired knowledge from the previously used training sets. In this paper, an incremental learning algorithm based on ensemble learning is proposed. Then, an application of the proposed algorithm for spam filtering is discussed. The proposed algorithm called incremental RotBoost, assumes the environment is stationary. It trains new weak classifiers for newly arriving data, which are added to the ensemble of classifiers. To evaluate the performance of the proposed algorithm, several computer experiments are conducted. The results of computer experiments show the ability of...
Designing a collaborative digital library to improve educational systems accompanied by a perspective from Iranian scholar attitudes
, Article International Conference on Enterprise Information Systems and Web Technologies 2010, EISWT 2010, 12 July 2010 through 14 July 2010 ; 2010 , Pages 132-140 ; 9781617820656 (ISBN) ; Firouz Abadi, A. D ; Khalkhali, I ; Shafiei, M. S ; Vojdanijahromi, R ; Sharif University of Technology
Abstract
In this article, a universal collaborative and competitive approach is introduced for deployment of digital collections in an ideal Digital Library for future's educational system. A hierarchical structure is proposed to be used for browsing and searching within mass of digital contents provided for union of curriculums worldwide. The collaborative and open-source aspects of the system guarantee the growth of the Digital Library. On the other hand, the competitive and reviewing aspects guarantee the accuracy of the novel library contents. Two experiments confirm the need for such a universal Digital Library worldwide to enhance learning capabilities, increase accessibility, avoid redundancy...
A novel concept drift detection method in data streams using ensemble classifiers
, Article Intelligent Data Analysis ; Volume 20, Issue 6 , 2016 , Pages 1329-1350 ; 1088467X (ISSN) ; Beigy, H ; Zaremoodi, P ; Sharif University of Technology
IOS Press
2016
Abstract
Concept drift, change in the underlying distribution that data points come from, is an inevitable phenomenon in data streams. Due to increase in the number of data streams' applications such as network intrusion detection, weather forecasting, and detection of unconventional behavior in financial transactions; numerous researches have recently been conducted in the area of concept drift detection. An ideal method for concept drift detection should be able to rapidly and correctly identify changes in the underlying distribution of data points and adapt its model as quickly as possible while the memory and processing time is limited. In this paper, we propose a novel explicit method based on...
Incremental evolving domain adaptation
, Article IEEE Transactions on Knowledge and Data Engineering ; Volume 28, Issue 8 , 2016 , Pages 2128-2141 ; 10414347 (ISSN) ; Soleymani Baghshah, M ; Gheisari, M ; Sharif University of Technology
IEEE Computer Society
Abstract
Almost all of the existing domain adaptation methods assume that all test data belong to a single stationary target distribution. However, in many real world applications, data arrive sequentially and the data distribution is continuously evolving. In this paper, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced. We assume that the available data for the source domain are labeled but the examples of the target domain can be unlabeled and arrive sequentially. Moreover, the distribution of the target domain can evolve continuously over time. We propose the Evolving Domain Adaptation (EDA) method that first finds a new feature space...
Structure learning of sparse GGMS over multiple access networks
, Article IEEE Transactions on Communications ; Volume 68, Issue 2 , 2020 , Pages 987-997 ; Karamzade, A ; Mirzaeifard, R ; Motahari, S. A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from a dataset distributed across multiple local machines. The local machines can communicate with the central machine through a wireless multiple access channel. In this paper, we are interested in designing effective strategies where reliable learning is feasible under power and bandwidth limitations. Two approaches are proposed: Signs and Uncoded methods. In the Signs method, the local machines quantize their data into binary vectors and an optimal channel coding scheme is used to reliably send the vectors to the central machine where the structure is learned from the received...
PEDM: Pre-ensemble decision making for malware identification and web files
, Article 6th International Conference on Web Research, ICWR 2020, 22 April 2020 through 23 April 2020 ; 2020 , Pages 33-37 ; Hazrati Fard, S. M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Connecting your system or device to an insecure network can create the possibility of infecting by the unwanted files. Malware is every malicious code that has the potential to harm any computer or network. So, detecting harmful files is a crucial duty and an important role in any system. Machine learning approaches use a variety of features such as Opcodes, Bytecodes, and System-calls to achieve accurate malware identification. Each of these feature sets provides a unique semantic view, while, considering the effect of altogether is more reliable to detect attacks. Malware can disguise itself in some views, but hiding in all views will be much more difficult. Multi-View Learning (MVL) is an...
Examining the ε-optimality property of a tunable FSSA
, Article 6th IEEE International Conference on Cognitive Informatics, ICCI 2007, Lake Tahoe, CA, 6 August 2007 through 8 August 2007 ; October , 2007 , Pages 169-177 ; 1424413273 (ISBN); 9781424413270 (ISBN) ; Iraji, R ; Sefidpour, A. R ; Manzuri Shalmani, M. T ; Sharif University of Technology
2007
Abstract
In this paper, a new fixed structure learning automaton (FSSA), with a tuning parameter for amount of its rewards, is presented and its behavior in stationary environments will be studied. This new automaton is called TFSLA (Tunable Fixed Structured Learning Automata). The proposed automaton characterizes by star shaped transition diagram and each branch of the star contains N states associated with a particular action. TFSLA is tunable, so that the automaton can receive reward flexibly, even when it accepted penalty according to its previous action. Experiments show that TFSLA converges to the optimal action faster than some older FSSAs (e.g. Krinsky and Krylov) and the analytic examination...
Another approach to detection of abnormalities in MR-images using support vector machines
, Article ISPA 2007 - 5th International Symposium on Image and Signal Processing and Analysis, Istanbul, 27 September 2007 through 29 September 2007 ; 2007 , Pages 98-101 ; 9789531841160 (ISBN) ; Dehestani Ardekani, R ; Torabi, M ; Fatemizadeh, E ; Sharif University of Technology
2007
Abstract
In this paper we will address two major problems in mammogram analysis for breast cancer in MR-images. The first is classification between normal and abnormal cases and then, discrimination between benign and malignant in cancerous cases. Our proposed method extracts textural and statistical descriptive features that are fed to a learning engine based on the use of Support Vector Machine learning framework to categorize them. The obtained results show excellent accuracy in both classification problems, that proves the appropriate combination of our features and selecting powerful classifier i.e. Support Vector Machine leads us to a brilliant outcome
Utilizing distributed learning automata to solve stochastic shortest path problems
, Article International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems ; Volume 14, Issue 5 , 2006 , Pages 591-615 ; 02184885 (ISSN) ; Meybodi, M. R ; Sharif University of Technology
2006
Abstract
In this paper, we first introduce a network of learning automata, which we call it as distributed learning automata and then propose some iterative algorithms for solving stochastic shortest path problem. These algorithms use distributed learning automata to find a policy that determines a path from a source node to a destination node with minimal expected cost (length). In these algorithms, at each stage distributed learning automata determines which edges to be sampled. This sampling method may result in decreasing unnecessary samples and hence decreasing the running time of algorithms. It is shown that the shortest path is found with a probability as close as to unity by proper choice of...
Machine learning approach for carrier surface design in carrier-based dry powder inhalation
, Article Computers and Chemical Engineering ; Volume 151 , 2021 ; 00981354 (ISSN) ; Alishiri, M ; Lau, R ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
In this study, a machine learning approach was applied to evaluate the impact of operating and design variables on dry powder inhalation efficiency. Emitted dose and fine particle fraction data were extracted from the literature for a variety of drug and carrier combinations. Carrier surface properties are obtained by image analysis of SEM images reported. Models combining artificial neural network and genetic algorithm were developed to determine the emitted dose and fine particle fraction. Design strategies for the carrier surface were also proposed to enhance the fine particle fractions. © 2021 Elsevier Ltd
SELM: Software engineering of machine learning models
, Article 20th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2021, 21 September 2021 through 23 September 2021 ; Volume 337 , 2021 , Pages 48-54 ; 09226389 (ISSN); 9781643681948 (ISBN) ; Besharati, M. R ; Hourali, M ; Sharif University of Technology
IOS Press BV
2021
Abstract
One of the pillars of any machine learning model is its concepts. Using software engineering, we can engineer these concepts and then develop and expand them. In this article, we present a SELM framework for Software Engineering of machine Learning Models. We then evaluate this framework through a case study. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning with less processing hardware resources and a smaller training dataset. This issue highlights the importance of an interdisciplinary approach to machine learning. Therefore, in this article, we have provided interdisciplinary teams' proposals for machine learning. © 2021...
Accelerating federated edge learning
, Article IEEE Communications Letters ; Volume 25, Issue 10 , 2021 , Pages 3282-3286 ; 10897798 (ISSN) ; Balef, A. R ; Dinh, C. T ; Tran, N. H ; Ngo, D. T ; Anh Le, T ; Vo, P. L ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
Transferring large models in federated learning (FL) networks is often hindered by clients' limited bandwidth. We propose $ extsf {FedAA}$ , an FL algorithm which achieves fast convergence by exploiting the regularized Anderson acceleration (AA) on the global level. First, we demonstrate that FL can benefit from acceleration methods in numerical analysis. Second, $ extsf {FedAA}$ improves the convergence rate for quadratic losses and improves the empirical performance for smooth and strongly convex objectives, compared to FedAvg, an FL algorithm using gradient descent (GD) local updates. Experimental results demonstrate that employing AA can significantly improve the performance of FedAvg,...
One-shot federated learning: Theoretical limits and algorithms to achieve them
, Article Journal of Machine Learning Research ; Volume 22 , 2021 , Pages 1-47 ; 15324435 (ISSN) ; Sharifnassab, A ; Jamaloddin Golestani, S ; Sharif University of Technology
Microtome Publishing
2021
Abstract
We consider distributed statistical optimization in one-shot setting, where there are m machines each observing n i.i.d. samples. Based on its observed samples, each machine sends a B-bit-long message to a server. The server then collects messages from all machines, and estimates a parameter that minimizes an expected convex loss function. We investigate the impact of communication constraint, B, on the expected error and derive a tight lower bound on the error achievable by any algorithm. We then propose an estimator, which we call Multi-Resolution Estimator (MRE), whose expected error (when B ≥ d log mn where d is the dimension of parameter) meets the aforementioned lower bound up to a...
New dictionary learning methods for two-dimensional signals
, Article 28th European Signal Processing Conference, EUSIPCO 2020, 24 August 2020 through 28 August 2020 ; Volume 2021-January , 2021 , Pages 2021-2025 ; 22195491 (ISSN); 9789082797053 (ISBN) ; Parsa, J ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
European Signal Processing Conference, EUSIPCO
2021
Abstract
By growing the size of signals in one-dimensional dictionary learning for sparse representation, memory consumption and complex computations restrict the learning procedure. In applications of sparse representation and dictionary learning in two-dimensional signals (e.g. in image processing), if one opts to convert two-dimensional signals to one-dimensional ones, and use the existing one-dimensional dictionary learning and sparse representation techniques, too huge signals and dictionaries will be encountered. Two-dimensional dictionary learning has been proposed to avoid this problem. In this paper, we propose two algorithms for two-dimensional dictionary learning. According to our...
Active Learning in Image Retrieval
, M.Sc. Thesis Sharif University of Technology ; Rabiei, Hamid Reza (Supervisor)
Abstract
Image retrieval, simply put, is the process of finding images in a predefined set , that are similar to an image specified by the user. In particular, the user inputs an image as query, and expects to see images similar to the query. Our purpose is to retrieve the images, by means of visual features, without any use of latent information such as tags and annotations.Afer the first round of retrieval, the answers can become more accurate, by means of user feedbacks. In this state, using active learning methods may be usefull. By using active data selection, we hope to achieve the answer faster. Learning based on manifold assumption, is another means which may be used in image retrieval....
Online Distance Metric Learning
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Distance Metric Learning algorithms have been widely used in Machine Learning methods recently. In these algorithms a distance function between objecs (data points) is learned based on their labels or similarity and dissimilarity constraints. Recent works have shown that a good precision is obtained in classification or clustering methods which use these functions. Since in the current systems many of data points do not exist at the beginning and are added to the training set as the algorithm is run, online methods are needed to update learned metric due to new data.
In this thesis, we proposed a new online distance metric learning method that has higher performance than existing...
In this thesis, we proposed a new online distance metric learning method that has higher performance than existing...
Continual Learning Algorithms Inspired by Human Learning
, M.Sc. Thesis Sharif University of Technology ; Soleymani Baghshah, Mahdieh (Supervisor)
Abstract
Despite the remarkable success of deep learning algorithms in recent years, it still has a long way to reach the status of human natural intelligence and to acquire the expected self-autonomy. As a result, many researchers in this field have focused on the development of these algorithms while taking inspiration from human cognitive behaviors. One of the disadvantages of current algorithms is the lack of their ability to learn in a continual manner while deployed in the environment. More precisely, deep learning models are not able to gradually gather knowledge from the environment and if they are in a situation of limited access to data, they will suffer from catastrophic forgetting; a...
Employing humanoid robots for teaching english language in Iranian junior high-schools [electronic resource]
, Article International Journal of Humanoid Robotics ; Vol. 11, No. 3 (2014) 1450022 ; Meghdari, Ali ; Ghazisaedy, Maryam ; Sharif University of Technology
Abstract
This paper presents the effect of robotics assisted language learning (RALL) on the vocabulary learning and retention of Iranian English as foreign language (EFL) junior high school students in Tehran, Iran. After taking a vocabulary pre-test, 46 beginner level female students at the age of 12, studying in their first year of junior-high participated in two groups of RALL (30 students) and non-RALL (16 students) in this study. The textbook used was the English book (Prospect-1) devised by the Iranian Ministry of Education for 7th graders, and the vocabulary taught and tested (pre-test and post-test) were taken from this book. Moreover, the treatment given by a teacher accompanied by a...
Solving a new mixed integer non-linear programming model of the multi-skilled project scheduling problem considering learning and forgetting effect on the employee efficiency
, Article IEEE International Conference on Industrial Engineering and Engineering Management ; 18 November , 2014 , Pages 400-404 ; ISSN: 21573611 ; ISBN: 9781479909865 ; Shadrokh, S ; Sharif University of Technology
Abstract
In this paper, we propose a new mathematical programming model to tackle the multi-skilled project scheduling problem. Considering the effect of learning and forgetting on the human skills, an exponential learning function has been developed by assuming the efficiency of employees performing activities to be dynamic. Taking the nonlinearity of the function into account, we use separable programming to acquire an appropriate linear approximation for it. Moreover, the proposed model allows us to relax some of the binary variables linearly without any modifications. At the end, the final model is tested with some instances and is solved by CPLEX in order to confirm the implemented...