Steven J. Simske

Bio: Steven J. Simske is an academic researcher from Colorado State University. The author has contributed to research in topics: Automatic summarization & Multi-document summarization. The author has an hindex of 42, co-authored 445 publications receiving 7445 citations. Previous affiliations of Steven J. Simske include Hewlett-Packard & University of Colorado Boulder.

NOSER: An algorithm for solving the inverse conductivity problem

Abstract: The inverse conductivity problem is the mathematical problem that must be solved in order for electrical impedance tomography systems to be able to make images. Here we show how this inverse conductivity problem is related to a number of other inverse problem. We then explain the workings of an algorithm that we have used to make images from electrical impedance data measured on the boundary of a circle in two dimensions. This algorithm is based on the method of least squares. It takes one step of a Newton's method, using a constant conductivity as an initial guess. Most of the calculations can therefore be done analytically. The resulting code is named NOSER, for Newton's One-Step Error Reconstructor. It provides a reconstruction with 496 degrees of freedom. The code does not reproduce the conductivity accurately (unless it differs very little from a constant), but it yields useful images. This is illustrated by images reconstructed from numerical and experimental data, including data from a human chest.

Assessing sentence scoring techniques for extractive text summarization

Abstract: Text summarization is the process of automatically creating a shorter version of one or more text documents. It is an important way of finding relevant information in large text libraries or in the Internet. Essentially, text summarization techniques are classified as Extractive and Abstractive. Extractive techniques perform text summarization by selecting sentences of documents according to some criteria. Abstractive summaries attempt to improve the coherence among sentences by eliminating redundancies and clarifying the contest of sentences. In terms of extractive summarization, sentence scoring is the technique most used for extractive text summarization. This paper describes and performs a quantitative and qualitative assessment of 15 algorithms for sentence scoring available in the literature. Three different datasets (News, Blogs and Article contexts) were evaluated. In addition, directions to improve the sentence extraction results obtained are suggested.

Changes in bone structure and mass with advancing age in the male C57BL/6J mouse.

Abstract: To determine whether the mouse loses bone with aging and whether the changes mimic those observed in human aging, we examined the changes in the tibial metaphysis and diaphysis in the male C57BL/6J mouse over its life span using microcomputed tomography (microCT). Cancellous bone volume fraction (BV/TV) decreased 60% between 6 weeks and 24 months of age. Loss was characterized by decreased trabecular number (Tb.N), increased trabecular spacing (Tb.Sp), and decreased connectivity. Anisotropy decreased while the structure model index increased with age. Cortical bone thickness increased between 6 weeks and 6 months of age and then decreased continuously to 24 months (-12%). Cortical bone area (Ct.Ar) remained constant between 6 and 24 months. Fat-free weight reached a peak at 12 months and gradually declined to 24 months. Total mass lost between 12 and 24 months reached 10%. Overall, the age-related changes in skeletal mass and architecture in the mouse were remarkably similar to those seen in human aging. Furthermore, the rapid early loss of cancellous bone suggests that bone loss is not just associated with old age in the mouse but rather occurs as a continuum from early growth. We conclude that the C57BL/6J male mouse maybe a useful model to study at least some aspects of age-related bone loss in humans.

Recognition of emotions in interactive voice response systems.

Abstract: This paper reports emotion recognition results from speech signals, with particular focus on extracting emotion features from the short utterances typical of Interactive Voice Response (IVR) applications. We focus on distinguishing anger versus neutral speech, which is salient to call center applications. We report on classification of other types of emotions such as sadness, boredom, happy, and cold anger. We compare results from using neural networks, Support Vector Machines (SVM), K-Nearest Neighbors, and decision trees. We use a database from the Linguistic Data Consortium at University of Pennsylvania, which is recorded by 8 actors expressing 15 emotions. Results indicate that hot anger and neutral utterances can be distinguished with over 90% accuracy. We show results from recognizing other emotions. We also illustrate which emotions can be clustered together using the selected prosodic features.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Combining Pattern Classifiers

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Optical tomography in medical imaging

Abstract: We present a review of methods for the forward and inverse problems in optical tomography. We limit ourselves to the highly scattering case found in applications in medical imaging, and to the problem of absorption and scattering reconstruction. We discuss the derivation of the diffusion approximation and other simplifications of the full transport problem. We develop sensitivity relations in both the continuous and discrete case with special concentration on the use of the finite element method. A classification of algorithms is presented, and some suggestions for open problems to be addressed in future research are made.