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Updates found with 'datasets'

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Updates found with 'datasets'

JAVA/DOT NET PROJECTS ABSTRACT 2016-2017 MINING USER-AWARE RARE SEQUENTIAL TOPIC PATTERNS IN DOCUMENT STREAMS ABSTRACT: Textual documents created and distributed on the Internet are ever changing in various forms. Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. In this paper, in order to characterize and detect personalized and abnormal behaviors of Internet users, we propose Sequential Topic Patterns (STPs) and formulate the problem of mining User-aware Rare Sequential Topic Patterns (URSTPs) in document streams on the Internet. They are rare on the whole but relatively frequent for specific users, so can be applied in many real-life scenarios, such as real-time monitoring on abnormal user behaviors. We present a group of algorithms to solve this innovative mining problem through three phases: preprocessing to extract probabilistic topics and identify sessions for different users, generating all the STP candidates with (expected) support values for each user by pattern-growth, and selecting URSTPs by making user-aware rarity analysis on derived STPs. Experiments on both real (Twitter) and synthetic datasets show that our approach can indeed discover special users and interpretable URSTPs effectively and efficiently, which significantly reflect users’ characteristics.EXISTING SYSTEMS: Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. Taking advantage of these extracted topics in document streams, most of existing works analyzed the evolution of individual topics to detect and predict social events as well as user behaviors. However, few researches paid attention to the correlations among different topics appearing in successive documents published by a specific user, so some hidden but significant information to reveal personalized behaviors has been neglected. And correspondingly, unsupervised mining algorithms for this kind of rare patterns need to be designed in a manner different from existing frequent pattern mining algorithms. Most of existing works on sequential pattern mining focused on frequent patterns, but for STPs, many infrequent ones are also interesting and should be discovered.PROPOSED SYSTEMS: In order to characterize and detect personalized and abnormal behaviors of Internet users, we propose Sequential Topic Patterns (STPs) and formulate the problem of mining User-aware Rare Sequential Topic Patterns (URSTPs) in document streams on the Internet. In order to characterize user behaviors in published document streams, we study on the correlations among topics extracted from these documents, especially the sequential relations, and specify them as Sequential Topic Patterns (STPs). Each of them records the complete and repeated behavior of a user when she is publishing a series. Topic mining in document collections has been extensively studied in the literature. Topic Detection and Tracking (TDT) task aimed to detect and track topics (events) in news streams with clustering-based techniques on keywords. The experiments conducted on both real (Twitter) and synthetic datasets demonstrate that the proposed approach is very effective and efficient in discovering special users as well as interesting and interpretable URSTPs from Internet document streams, which can well capture users’ personalized and abnormal behaviors and characteristics.ADVANATAGES: Taking advantage of these extracted topics in document streams, most of exist works analyzed the evolution of individual topics to detect and predict social events as well as user behaviors. In order to find significant STPs, a document stream should be divided into independent sessions in advance with the definition. A sketch map of session identification Each ellipse represents a session, and all the sessions in each line constitute a document subsequence for a specific user. we can conclude that the two algorithms have their respective advantages. Which one is appropriate for the real task reflects a trade-off between mining accuracy and execution speed, and should depend on the specific requirements of application scenarios.HARDWARE REQUIREMENTS: Hardware - Pentium Speed - 1.1 GHz RAM - 1GB Hard Disk - 20 GB Floppy Drive - 1.44 MB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGASOFTWARE REQUIREMENTS: Operating System : Windows Technology : Java and J2EE Web Technologies : Html, JavaScript, CSS IDE : My Eclipse Web Server : Tomcat Tool kit : Android Phone Database : My SQL Java Version : J2SDK1.5 CONCLUSION: Mining URSTPs in published document streams on the Internet is a significant and challenging problem. It formulates a new kind of complex event patterns based on document topics, and has wide potential application scenarios, such as real-time monitoring on abnormal behaviors of Internet users. In this paper, several new concepts and the mining problem are formally defined, and a group of algorithms are designed and combined to systematically solve this problem. The experiments conducted on both real (Twitter) and synthetic datasets demonstrate that the proposed approach is very effective and efficient in discovering special users as well as interesting and interpretable URSTPs from Internet document streams, which can well capture users’ personalized and abnormal behaviors and characteristics. As this paper puts forward an innovative research direction on Web data mining, much work can be built on it in the future.
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JAVA PROJECTS ABSTRACT 2016-2017 A LOCALITY SENSITIVE LOW-RANK MODEL FOR IMAGE TAG COMPLETION ABSTRACT: Tag-based image retrieval often used to increase performance to retrieving images with the help of search engines. Image retrieval based on user-provided image tags on the photo sharing websites. A requirement for effective searching and retrieval of images in rapid growing online image databases is that each image has accurate and useful annotation. Many visual applications have benefited from the outburst of web images, yet the imprecise and incomplete tags arbitrarily provided by users. In this paper, we propose a novel locality sensitive low-rank model for image tag completion, which approximates the global nonlinear model with a collection of local linear models. To effectively infuse the idea of locality sensitivity, a simple and effective pre-processing module is designed to learn suitable representation for data partition.In thi paper they used for BIRCH algorithm. BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets.An advantage of BIRCH is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). In most cases, BIRCH only requires a single scan of the database.Existing SystemThe user-labeled visual data, such as images which are uploaded and shared in Flickr, are usually associated with imprecise and incomplete tags. This will pose threats to the retrieval or indexing of these images, causing them difficult to be accessed by users. Unfortunately, missing label is inevitable in the manual labeling phase, since it is infeasible for users to label every related word and avoid all possible confusions, due to the existence of synonyms and user preference. Therefore, image tag completion or refinement has emerged as a hot issue in the multimedia community.Many visual applications have benefited from the outburst of web images, yet the imprecise and incomplete tags arbitrarily provided by users, as the thorn of the rose, may hamper the performance of retrieval or indexing systems relying on such data.Proposed SystemTo effectively infuse the idea of locality sensitivity, a simple and effective pre-processing module is designed to learn suitable representation for data partition, and a global consensus regularizer is introduced to mitigate the risk of overfitting. Meanwhile, low-rank matrix factorization is employed as local models, where the local geometry structures are preserved for the low-dimensional representation of both tags and samples. Extensive empirical evaluations conducted on three datasets demonstrate the effectiveness and efficiency of the proposed method, where our method outperforms pervious ones by a large margin.Advantages• We propose a locality sensitive low-rank model for image tag completion, which approximates the global nonlinear model with a collection of local linear models, by which complex correlation structures can be captured. • Several adaptations are introduced to enable the fusion of locality sensitivity and low-rank factorization, including a simple and effective pre-processing module and a global consensus regularizer to mitigate the risk of overfitting.Disadvantages• image tag completion or refinement has emerged as a hot issue in the multimedia community.• The existing completion methods are usually founded on linear assumptions, hence the obtained models are limited due to their incapability to capture complex correlation patterns.System RequirementsH/W System Configuration:-Processor - Pentium –IIISpeed - 1.1 GhzRAM - 256 MB(min)Hard Disk - 20 GBKey Board - Standard Windows KeyboardMouse - Two or Three Button MouseMonitor - SVGA S/W System Configuration Operating System :Windows95/98/2000/XP  Application Server : Tomcat5.0/6.X  Front End : HTML, Java, Jsp Scripts : JavaScript. Server side Script : Java Server Pages. Database Connectivity : Mysql.Conclusion In this paper we propose a locality sensitive low-rank model for image tag completion. The proposed method can capture complex correlations by approximating a nonlinear model with a collection of local linear models. To effectively integrate locality sensitivity and low-rank factorization, several adaptations are introduced, including the design of a pre-processing module and a global consensus regularizer. Our method achieves superior results on three datasets and outperforms pervious methods by a large margin.
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DOT NET PROJECTS ABSTRACT 2016-2017 A DATA AND TASK CO-SCHEDULING ALGORITHM FOR SCIENTIFIC CLOUD WORKFLOWS ABSTRACT: Cloud computing has emerged as a promising computational infrastructure for cost-efficient workflow execution by provisioning on-demand resources in a pay-as-you-go manner. While scientific workflows require accessing community-wide resources, they usually need to be performed in collaborative cloud environments composed of multiple datacenters. Although such environments facilitate scientific collaboration, the movements of input and intermediate datasets across geographically distributed data centers may cause intolerable latency that would hinder efficient execution of large-scale data-intensive scientific workflows. To address the problem, in this article we propose a novel multi-level K-cut graph partitioning algorithm to minimize the volume of data transfer across data centers while satisfying load balancing and fixed data constraints. The algorithm first contracts the fixed input datasets in the same data center and their consuming tasks, and coarsens the contracted graph to a predefined scale in a level-by-level manner. Then, aK-cut algorithm is used to partition the resulted graph into K parts such that the cut size is minimized. After that, the partitioned graphis projected back to the original workflow graph, during which the load balancing constraint is maintained. We evaluate our algorithm using three real-world workflow applications and the results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.
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DOT NET PROJECTS ABSTRACT 2016-2017 PRIVACY-PRESERVING OUTSOURCED ASSOCIATION RULE MINING ON VERTICALLY PARTITIONED DATABASES ABSTRACT: Association rule mining and frequent item set mining are two popular and widely studied data analysis techniques for a range of applications. In this paper, we focus on privacy preserving mining on vertically partitioned databases. In such a scenario, data owners wish to learn the association rules or frequent item sets from a collective dataset, and disclose as little information about their (sensitive) raw data as possible to other data owners and third parties. To ensure data privacy, we design an efficient holomorphic encryption scheme and a secure comparison scheme. We then propose a cloud-aided frequent item set mining solution, which is used to build an association rule mining solution. Our solutions are designed for outsourced databases that allow multiple data owners to efficiently share their data securely without compromising on data privacy. Our solutions leak less information about the raw data than most existing solutions. In comparison to the only known solution achieving a similar privacy level as our proposed solutions, the performance of our proposed solutions is 3 to 5 orders of magnitude higher. Based on our experiment findings using different parameters and datasets, we demonstrate that the run time in each of our solutions is only one order higher than that in the best non-privacy-preserving data mining algorithms. Since both data and computing work are outsourced to the cloud servers, the resource consumption at the data owner end is very low.
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MATLAB PROJECTS ABSTRACT 2016-2017 LEARNING INVARIANT COLOR FEATURES FOR PERSON RE-IDENTIFICATION ABSTRACT: Matching people across multiple camera view known as person re-identification, is a challenging problem due to the change in visual appearance caused by varying lighting conditions. The perceived color of the subject appears to be different under different illuminations. Previous works use color as it is or address these challenges by designing color spaces focusing on a specific cue. In this paper, we propose an approach for learning color patterns from pixels sampled from images across two camera views. The intuition behind this work is that, even though varying lighting conditions across views affect the pixel values of same color, the final representation of a particular color should be stable and invariant to these variations, i.e. they should be encoded with the same values. We model color feature generation as a learning problem by jointly learning a linear transformation and a dictionary to encode pixel values. We also analyze different photometric invariant color spaces as well as popular color constancy algorithm for person re-identification. Using color as the only cue, we compare our approach with all the photometric invariant color spaces and show superior performance over all of them. Combining with other learned low-level and high-level features, we obtain promising results in VIPeR, Person Re-ID 2011 and CAVIAR4REID datasets.
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MATLAB PROJECTS ABSTRACT 2016-2017 MULTI-LEVEL CANONICAL CORRELATION ANALYSIS FOR STANDARD-DOSE PET IMAGE ESTIMATION ABSTRACT: Positron emission tomography (PET) images are widely used in many clinical applications such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both standard-dose and low-dose PET data into a common space and then performing patch based sparse representation. However, alone-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level Canonical Correlation Analysis (mCCA)scheme to solve this problem. Specifically, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. Additionally, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain datasets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.
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MATLAB PROJECTS ABSTRACT 2016-2017 WEAKLY SUPERVISED FINE-GRAINED CATEGORIZATION WITH PART-BASED IMAGE REPRESENTATION ABSTRACT: In this paper, we propose a fine-grained image categorization system with easy deployment. We do not use any object/part annotation (weakly-supervised) in the training or in the testing stage, but only class labels for training images. Fine-grained image categorization aims to classify objects with only subtle distinctions (e.g., two breeds of dogs that look alike). Most existing works heavily rely on object/part detectors to build the correspondence between object parts, which require accurate object or object part annotations at least for training images. The need for expensive object annotations prevents the wide usage of these methods. Instead, we propose to generate multistage part proposals from object proposals, select useful part proposals, and use them to compute a global image representation for categorization. This is specially designed for the weakly supervised fine-grained categorization task, because useful parts have been shown to play a critical role in existing annotation dependent works but accurate part detectors are hard to acquire. With the proposed image representation, we can further detect and visualize the key (most discriminative) parts in objects of different classes. In the experiments, the proposed weakly supervised method achieves comparable or better accuracy than state-of-the-art weakly-supervised methods and most existing annotation-dependent methods on three challenging datasets. Its success suggests that it is not always necessary to learn expensive object/part detectors in fine-grained image categorization.
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