Data Mining Practical Machine Learning Tools and Techniques

In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we review 300 references on video retrieval, indicating when text-only solutions are unsatisfactory and showing the promising alternatives which are in majority concept-based. Therefore, central to our discussion is the notion of a semantic concept: an objective linguistic description of an observable entity. Specifically, we present our view on how its automated detection, selection under uncertainty, and interactive usage might solve the major scientific problem for video retrieval: the semantic gap. To bridge the gap, we lay down the anatomy of a concept-based video search engine. We present a component-wise decomposition of such an interdisciplinary multimedia system, covering influences from information retrieval, computer vision, machine learning, and human–computer interaction. For each of the components we review state-of-the-art solutions in the literature, each having different characteristics and merits. Because of these differences, we cannot understand the progress in video retrieval without serious evaluation efforts such as carried out in the NIST TRECVID benchmark. We discuss its data, tasks, results, and the many derived community initiatives in creating annotations and baselines for repeatable experiments. We conclude with our perspective on future challenges and opportunities.

/pdf/concept-based-video-retrieval-1iy430iazc.pdf

Concept-Based Video Retrieval

With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while the wireless infrastructure deployed in the environment determines the target’s location by analyzing its impact on wireless signals. This article is intended to offer a comprehensive state-of-the-art survey on wireless indoor localization from the device perspective. In this survey, we review the recent advances in both modes by elaborating on the underlying wireless modalities, basic localization principles, and data fusion techniques, with special emphasis on emerging trends in (1) leveraging smartphones to integrate wireless and sensor capabilities and extend to the social context for device-based localization, and (2) extracting specific wireless features to trigger novel human-centric device-free localization. We comprehensively compare each scheme in terms of accuracy, cost, scalability, and energy efficiency. Furthermore, we take a first look at intrinsic technical challenges in both categories and identify several open research issues associated with these new challenges.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5886&context=sis_research

A Survey on Wireless Indoor Localization from the Device Perspective

Computer-aided education systems are now seeking to provide each student with personalized materials based on a student's individual knowledge. To provide suitable learning materials, tracing each student's knowledge over a period of time is important. However, predicting each student's knowledge is difficult because students tend to forget. The forgetting behavior is mainly because of two reasons: the lag time from the previous interaction, and the number of past trials on a question. Although there are a few studies that consider forgetting while modeling a student's knowledge, some models consider only partial information about forgetting, whereas others consider multiple features about forgetting, ignoring a student's learning sequence. In this paper, we focus on modeling and predicting a student's knowledge by considering their forgetting behavior. We extend the deep knowledge tracing model [17], which is a state-of-the-art sequential model for knowledge tracing, to consider forgetting by incorporating multiple types of information related to forgetting. Experiments on knowledge tracing datasets show that our proposed model improves the predictive performance as compared to baselines. Moreover, we also examine that the combination of multiple types of information that affect the behavior of forgetting results in performance improvement.

Augmenting Knowledge Tracing by Considering Forgetting Behavior

Images of document pages have different characteristics than images of natural scenes, and so the sharpness measures developed for natural scene images do not necessarily extend to document images primarily composed of text. We present an efficient and simple method for effectively estimating the sharp-ness/blurriness of document images that also performs well on natural scenes. Our method can be used to predict the sharpness in scenarios where images are blurred due to camera-motion (or hand-shake), defocus, or inherent properties of the imaging system. The proposed method outperforms the perceptually-based, no-reference sharpness work of [1] and [4], which was shown to perform better than 14 other no-reference sharpness measures on the LIVE dataset.

Sharpness estimation for document and scene images

DOTS (Dynamic Object Tracking System) is an indoor, real-time, multi-camera surveillance system, deployed in a real office setting. DOTS combines video analysis and user interface components to enable security personnel to effectively monitor views of interest and to perform tasks such as tracking a person. The video analysis component performs feature-level foreground segmentation with reliable results even under complex conditions. It incorporates an efficient greedy-search approach for tracking multiple people through occlusion and combines results from individual cameras into multi-camera trajectories. The user interface draws the users. attention to important events that are indexed for easy reference at a later time. Different views within the user interface provide spatial information for easier navigation. Our system, with over twenty video cameras installed in hallways and other public spaces in our office building, has been in constant use for almost a year.

/pdf/dots-support-for-effective-video-surveillance-2umgzqnmhy.pdf

DOTS: support for effective video surveillance

FACT is an interactive paper system for fine-grained interaction with documents across the boundary between paper and computers. It consists of a small camera-projector unit, a laptop, and ordinary paper documents. With the camera-projector unit pointing to a paper document, the system allows a user to issue pen gestures on the paper document for selecting fine-grained content and applying various digital functions. For example, the user can choose individual words, symbols, figures, and arbitrary regions for keyword search, copy and paste, web search, and remote sharing. FACT thus enables a computer-like user experience on paper. This paper interaction can be integrated with laptop interaction for cross-media manipulations on multiple documents and views. We present the infrastructure, supporting techniques and interaction design, and demonstrate the feasibility via a quantitative experiment. We also propose applications such as document manipulation, map navigation and remote collaboration.

FACT: fine-grained cross-media interaction with documents via a portable hybrid paper-laptop interface

In this paper we describe the analysis component of an indoor, real-time, multi-camera surveillance system. The analysis includes: (1) a novel feature-level foreground segmentation method which achieves efficient and reliable segmentation results even under complex conditions, (2) an efficient greedy search based approach for tracking multiple people through occlusion, and (3) a method for multi-camera handoff that associates individual trajectories in adjacent cameras. The analysis is used for an 18 camera surveillance system that has been running continuously in an indoor business over the past several months. Our experiments demonstrate that the processing method for people detection and tracking across multiple cameras is fast and robust.

http://www.saiip-vision.org/tyang/papers/TaoYang_ICME07.pdf

Francine Chen

Papers

Augmenting Knowledge Tracing by Considering Forgetting Behavior

Sharpness estimation for document and scene images

DOTS: support for effective video surveillance

FACT: fine-grained cross-media interaction with documents via a portable hybrid paper-laptop interface

Robust People Detection and Tracking in a Multi-Camera Indoor Visual Surveillance System