Describing clothing appearance with semantic attributes is an appealing technique for many important applications. In this paper, we propose a fully automated system that is capable of generating a list of nameable attributes for clothes on human body in unconstrained images. We extract low-level features in a pose-adaptive manner, and combine complementary features for learning attribute classifiers. Mutual dependencies between the attributes are then explored by a Conditional Random Field to further improve the predictions from independent classifiers. We validate the performance of our system on a challenging clothing attribute dataset, and introduce a novel application of dressing style analysis that utilizes the semantic attributes produced by our system.

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Describing clothing by semantic attributes

The tracking of objects using distributed multiple sensors is an important field of work in the application areas of autonomous robotics, military applications, and mobile systems. In this survey, we review a number of computationally intelligent methods that are used for developing robust tracking schemes through sensor data fusion. The survey discusses the application of the various algorithms at different layers of the JDL model and highlights the weaknesses and strengths of the approaches in the context of different applications

Approaches to Multisensor Data Fusion in Target Tracking: A Survey

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.

The use of machine learning algorithms in recommender systems: A systematic review

This book is a strategic look at consumer behavior in order to guide successful marketing activities. The Wheel of Consumer Analysis is the organizing factor in the book. The four major parts of the wheel are consumer affect and cognition, consumer behavior, consumer environment, and marketing strategy. URI http://dspace.fue.edu.eg/xmlui/handle/123456789/4317 Description Includes bibliographical references and index. xxii, 778 p. : Collections Books Summaries [1560] DSpace software copyright © 2002-2016 DuraSpace Contact Us | Send Feedback All rights reserved to Consumer behavior: Building marketing strategy  

Consumer Behavior: Building Marketing Strategy

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of a recommender system using a machine learning algorithm often has problems and open questions that must be evaluated, so software engineers know where to focus research efforts. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for software engineering research. The study concludes that Bayesian and decision tree algorithms are widely used in recommender systems because of their relative simplicity, and that requirement and design phases of recommender system development appear to offer opportunities for further research.

The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review

A computer-implemented method of managing resources in a virtual machine environment can include determining a specification of provisioning success corresponding to each of multiple jobs in the virtual machine environment, determining a prediction of resource needs corresponding to each of the jobs, and determining a resource specification corresponding to each of the jobs based on the specification of provisioning success and the prediction of resource needs.

Statistical packing of resource requirements in data centers

A power control system in a data center has a plurality of physical servers, each server having a local controller, at least one virtual server coupled to at least some of the physical servers, and a central controller to control task loading on the physical servers through the virtual servers. A method of controlling power consumption in a data center includes receiving inputs from local controllers residing on loads, the inputs including data about power consumption on the loads, receiving as an input at least one quality of service requirement, and allocating tasks to at least one server based upon the quality of service and the power consumption on the loads.

Integrated energy savings and business operations in data centers

This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. State-space models of physical phenomena such as those arising from tracking multiple interacting targets, while commonly used in signal processing and control, suffer from the curse of dimensionality as the number of phenomena of interest increases. Furthermore, mapping an inference algorithm onto a distributed sensor network must appropriately allocate scarce sensing and communication resources. We address the state-space explosion problem by developing a distributed state-space model that switches between factored and joint state spaces as appropriate. We develop a collaborative group abstraction as a mechanism to effectively support the information flow within and across subspaces of the state-space model, which can be efficiently supported in a communication-constrained network. The approach has been implemented and demonstrated in a simulation of tracking multiple interacting targets.

Distributed state representation for tracking problems in sensor networks

One embodiment of the present invention provides a system that facilitates interaction between two entities located away from each other. The system includes a virtual reality system (114), an augmented reality system (120), and an object-state-maintaining mechanism. During operation, the virtual reality system (114) displays an object associated with a real-world object. The augmented reality system (120) displays the object based on a change to the state of the object. The object-state-maintaining mechanism determines the state of the object and communicates a state change to the virtual reality system, the augmented reality system, or both. A respective state change of the object can be based on one or more of: a state change of the real-world object; a user input to the virtual reality system or the augmented reality system; and an analysis of an image of the real-world object.

Spatial association between virtual and augmented reality

One embodiment of the present invention provides a system for facilitating social networking based on fashion-related information. During operation, the system receives fashion-related information from a user. Next, the system extracts the user's fashion preferences from the received information and compares the user's fashion preference with other users' fashion preferences. Finally, the system groups users based on similarity of their fashion preferences.

Maurice Chu

Papers

Statistical packing of resource requirements in data centers

Integrated energy savings and business operations in data centers

Distributed state representation for tracking problems in sensor networks

Spatial association between virtual and augmented reality

Method for facilitating social networking based on fashion-related information