Miracle-dz/UniFAR-FTUs huggingface dataset:
Hadoop Optimization for Massive Image Processing: Case Study Face Detection

Face detection applications are widely used for searching, tagging and classifying people inside very large image databases. This type of applications requires processing of relatively small sized and large number of images. On the other hand, Hadoop Distributed File System (HDFS) is originally designed for storing and processing largesize files. Huge number of small-size images causes slowdown in HDFS by increasing total initialization time of jobs, scheduling overhead of tasks and memory usage of the file system manager (Namenode). The study in this paper presents two approaches to improve small image file processing performance of HDFS. These are (1) converting the images into single large-size file by merging and (2) combining many images for a single task without merging. We also introduce novel Hadoop file formats and record generation methods (for reading image content) in order to develop these techniques
A Steady-State Convergence Detection Method for Monte Carlo Simulation

In the direct simulation Monte Carlo (DSMC), exclusion of microscopic data sampled in the unsteady phase can accelerate the convergence and lead to more accurate results in the steady state problem. In this study, a new method for detection of the steady state onset, called Probabilistic Automatic Reset Sampling (PARS), is introduced. The new method can detect the steady state automatically and reset sample after satisfying the reset criteria based on statistics. The method is simple and does not need any user-specified inputs. The simulation results show that the proposed strategy can work well even in condition with constant number of particles inside the domain which was the main drawback of the previous methods.
Partial coordination. II. A preliminary evaluation and failure analysis

Partial coordination is a new method for cataloging documents forsubject access. It is especially designed to enhance the precision of documentsearches in online environments. This paper reports a preliminaryevaluation of partial coordination which shows promising results comparedwith full text retrieval. We also report the difficulties in empiricallyevaluating the effectiveness of automatic full-text retrieval in contrast tomixed methods such as partial coordination which combine humancataloging with computerized retrieval. Based on our study we proposeresearch in this area will substantially benefit from a common framework forfailure analysis and a common data set. This will allow information retrievalresearchers adapting "library style" cataloging to large electronic documentcollections, as well as those developing automated or mixed methods, todirectly compare their proposals for indexing and retrieval. This paperconcludes by suggesting guidelines for constructing such a testbed.
Digital video pre-processing with multi-dimensional attribute morphology

Digital video pre-processing aims to eliminate the image noise that reduces the overall compression performance while preserving the visual quality of the image sequence. This paper extends our work on attribute morphology pre-processing to multidimensional spatio-temporal filters using both the standard volumetric approach and a sliding window implementation. A novel combination of spatial and spatio-temporal filters, each with their own attribute limits, is introduced and produces the best overall peak signal to noise ratio performance. The recently proposed power attribute is also shown to out-perform the area attribute.
USER’S GUIDE FOR SNOPT 5.3: A FORTRAN PACKAGE FOR LARGE-SCALE NONLINEAR PROGRAMMING

SNOPT is a set of Fortran subroutines for minimizing a smooth function subject to constraints, which may include simple bounds on the variables, linear constraints and smooth nonlinear constraints. SNOPT is a general-purpose optimizer, designed to find locally optimal solutions for models involving smooth nonlinear functions. They are often more widely useful. (For example, local optima are often global solutions, and discontinuities in the function gradients can often be tolerated if they are not too close to an optimum.) Ideally, users should provide gradients. Unknown components are estimated by finite differences. SNOPT incorporates a sequential quadratic programming (SQP) method that obtains search directions from a sequence of quadratic programming subproblems. Each QP subproblem minimizes a quadratic model of a certain Lagrangian function subject to a linearization of the constraints. An augmented Lagrangian merit function is reduced along each search direction to ensure convergence from any starting point. SNOPT is most efficient if only some of the variables enter nonlinearly, or if the number of active constraints (including simple bounds) is nearly as large as the number of variables. SNOPT requires relatively few evaluations of the problem functions. Hence it is especially effective if the objective or constraint functions are expensive to evaluate. The source code for SNOPT is suitable for any machine with a reasonable amount of memory and a Fortran compiler. SNOPT may be called from a driver program (typically in Fortran, C or MATLAB).
The Agoraphilic algorithm: a new optimistic approach for mobile robot navigation

Presents a summary of research which has been conducted at Monash University-Gippsland Campus. The objective of this research project is to develop a new efficient methodology for mobile robot navigation. The new "optimistic" algorithm is based on free space attraction rather than looking for obstacles to avoid. It utilizes a single attractive force, generated by the surrounding local free-space, as opposed to the classical artificial potential field whereby attractive and repulsive forces are employed. Adaptive fuzzy logic is then utilized to limit the 'free-space' force so as to promote the movement toward the goal. To demonstrate the algorithms ability, several simulations plus some preliminary experimental results are presented.
Integration of DICOM and openEHR standards

The standard format for medical imaging storage and transmission is DICOM. openEHR is an open standard specification in health informatics that describes the management and storage, retrieval and exchange of health data in electronic health records. Considering that the integration of DICOM and openEHR is beneficial to information sharing, on the basis of XML-based DICOM format, we developed a method of creating a DICOM Imaging Archetype in openEHR to enable the integration of DICOM and openEHR. Each DICOM file contains abundant imaging information. However, because reading a DICOM involves looking up the DICOM Data Dictionary, the readability of a DICOM file has been limited. openEHR has innovatively adopted two level modeling method, making clinical information divided into lower level, the information model, and upper level, archetypes and templates. But one critical challenge posed to the development of openEHR is the information sharing problem, especially in imaging information sharing. For example, some important imaging information cannot be displayed in an openEHR file. In this paper, to enhance the readability of a DICOM file and semantic interoperability of an openEHR file, we developed a method of mapping a DICOM file to an openEHR file by adopting the form of archetype defined in openEHR. Because an archetype has a tree structure, after mapping a DICOM file to an openEHR file, the converted information is structuralized in conformance with openEHR format. This method enables the integration of DICOM and openEHR and data exchange without losing imaging information between two standards.
Impacts of strategic management of technology in a conceptual framework of enterprise practice

In high velocity and turbulent business environment strategic management of technology is a managerial challenge. In this paper the impacts of strategic management of technology are outlined in a conceptual framework with a perspective to utilisation of the framework. The elements of the strategic management of technology consist of structures, objectives and impacts. The impacts are classified as tangible, business model, knowledge based, transitional and dynamic, social and environmental impacts. As a comprehensive model the framework is proposed to apply for practitioners in systematic development of strategic management of technology, and to provide a frame of reference for scholars.
Promoting sexual and reproductive health for persons with disabilities: WHO/UNFPA guidance note

The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers' products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use. The text of this publication is available in a number of different formats. It is on the Department's website in a screen reader-friendly PDF at: http://www.
An adaptive ARQ scheme using pragmatic TCM

We propose an adaptive automatic repeat request (ARQ) scheme using pragmatic trellis-coded modulations (TCMs) and soft-decision Viterbi decoding. The scheme adapts to the changing channel noise condition by selecting the TCM with a code rate which best matches the current channel signal-to-noise ratio (SNR). A simple and yet efficient algorithm for estimating channel SNR is also proposed. <<ETX>>
HISTORICAL ORIGINS OF INFORMATION BEHAVIOR RESEARCH BY REFERENCE PUBLICATION YEAR SPECTROSCOPY

Using a quantitative method named reference publication year spectroscopy (RPYS), this research tried to determine the historical roots of information behavior research. RPYS pave the way for determining the significant years and works in information behavior. Using scientometrics method, the initial data of this study have been extracted from the Web of Science. Using RPYS software, the revised data were analyzed and visualized in Excel. The distribution of cited references in information behavior revealed three peaks within 19th century. Moreover, our analysis identified 6 peaks between 1900 to 1969 in the field of information behavior, occurred respectively in 1948, 1954, 1957, 1960, 1965, and 1967. Based on the study findings, it seems that information behavior research has been shaped intellectually by fields such as psychology, quantitative and qualitative methodologies, etc. Moreover, it has been influenced by some theories and theoretical works.
Incremental feature building and classification for image segmentation

Image segmentation problems can be solved with classica- tion algorithms. However, their use is limited to features derived from intensities of pixels or patches. Features such as contiguity of two regions cannot be considered without prior knowledge of one of the two class la- bels. Instead of stacking various classication algorithms, we describe an incremental classier that works in a space where features are progressively evaluated. Experiments on articial images demonstrate the capabilities of this incremental scheme.
Semi-supervised classification of emotional pictures based on feature combination

Can the abundant emotions reflected in pictures be classified automatically by computer? Only the visual features extracted from images are considered in the previous researches, which have the constrained capability to reveal various emotions. In addition, the training database utilized by previous methods is the subset of International Affective Picture System (IAPS) that has a relatively small scale, which exerts negative effects on the discrimination of emotion classifiers. To solve the above problems, this paper proposes a novel and practical emotional picture classification approach, using semi-supervised learning scheme with both visual feature and keyword tag information. Besides the IAPS with both emotion labels and keyword tags as part of the training dataset, nearly 2000 pictures with only keyword tags that are downloaded from the website Flickr form an auxiliary training dataset. The visual feature of the latent emotional semantic factors is extracted by probabilistic Latent Semantic Analysis (pLSA) model, while the text feature is described by binary vectors on the tag vocabulary. A first Linear Programming Boost (LPBoost) classifier which is trained on the samples from IAPS combines the above two features, and aims to label the other training samples from the internet. Then the second SVM classifier which is trained on all training images using only visual feature, focuses on the test images. In the experiment, the categorization performance of our approach is better than the latest methods.
Disappearing Cryptography: Information Hiding: Steganography and Watermarking (2nd Edition)

From the Publisher: Disappearing Cryptography, Second Edition describes how to take words, sounds, or images and hide them in digital data so they look like other words, sounds, or images. When used properly, this powerful technique makes it almost impossible to trace the author and the recipient of a message. Conversations can be submerged in the flow of information through the Internet so that no one can know if a conversation exists at all. This full revision of the best-selling first edition describes a number of different techniques to hide information. These include encryption, making data incomprehensible; steganography, embedding information into video, audio, or graphics files; watermarking, hiding data in the noise of image or sound files; mimicry, "dressing up" data and making it appear to be other data, and more. The second edition also includes an expanded discussion on hiding information with spread-spectrum algorithms, shuffling tricks, and synthetic worlds. Each chapter is divided into sections, first providing an introduction and high-level summary for those who want to understand the concepts without wading through technical explanations, and then presenting greater detail for those who want to write their own programs. To encourage exploration, the author's Web site contains implementations for hiding information in lists, sentences, and images. About the Author: Peter Wayner is a writer living in Baltimore and is the author of Digital Cash and Agents at Large (both Academic Press). His writings appear in numerous academic journals as well as the pages of more popular forums such as MacWorld and the New York Times. He has taught various computer science courses at Cornell University and Georgetown University.
Instructing Industrial Robots Using High-Level Task Descriptions

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they have to become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor- controlled robot tasks. The robot motions are expressed using constraints, and a number of simple constrained motions can be combined into a robot skill. The skill can be stored in a database together with a semantic description, which en- ables reuse and reasoning. The main contributions of the thesis are 1) develop- ment of ontologies for robot devices and skills, 2) a user interface that provides programming support for task descriptions in unstructured natural language and 3) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user. These parameters are described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in four peer-reviewed papers. The first covers knowledge-based instruction and the system architecture. The two following papers describe the natural language programming feature of the system as well as a description of the user interface. The fourth and last paper describes the code generation step, thus connecting the high-level language instructions to real-time executable code. (Less)
Implementing image analysis in laboratory‐based experimental systems for ecology and evolution: a hands‐on guide

Experimental laboratory systems (ELS) are widely applied research tools to test theoretical predictions in ecology and evolution. Combining ELS with automated image analysis could significantly boost information acquisition due to the ease at which abundance and morphological data is collected. Despite the advantages of image analysis, the technology has not been fully adopted yet, presumably due to the difficulties of technical implementation. The tools needed to integrate image analysis in ELS are nowadays readily available: digital camera equipment is purchased at limited costs and free software solutions which allow sophisticated image processing and analysis exist. Here, we give a concise description how to integrate these pieces into a largely automated image analysis workflow. We provide researchers with necessary background information on the principles of image analysis, explaining how to standardize image acquisition and how to validate the results to reduce bias. Three cross‐platform and open‐source software solutions for image analysis are compared: ImageJ, the EBImage package in R, and Python with the SciPy/scikit image libraries. The relative strengths and limitations of each solution are compared and discussed. In addition, a set of test images and three scripts are provided in the Online Supplementary Material to illustrate the use of image analysis and help biologists to implement image analysis in their own systems. To demonstrate the reliability and versatility of a validated image analysis workflow, we introduce our own Tetrahymena thermophila ELS. Then, examples from evolutionary ecology are provided showing the advantages of image analysis to study different ecological questions, aiming at both the population and individual level. Experimental laboratory systems that integrate the advantages of image analysis extend their application and versatility compared with regular ELS. Such improvements are necessary to understand complex processes such as eco‐evolutionary feedbacks, community dynamics and individual behaviour in ELS.
Extracting and re-using design patterns from genetic algorithms using case-based reasoning

This paper proposes a scheme in which case-based reasoning techniques are employed to extract design patterns from a genetic algorithm used to optimize combinational circuits at the gate level. The approach seems to be able to (implicitly) rediscover several of the traditional Boolean rules used for circuit simplification and it also (implicitly) finds new simplification rules. Also, the paper illustrates how the approach can be used to reduce convergence times of a genetic algorithm using previously found solutions as cases to solve similar problems.
Interplay between Probabilistic Classifiers and Boosting Algorithms for Detecting Complex Unsolicited Emails

This paper presents the performance comparison of probabilistic classifiers with/without the help of various boosting algorithms, in the Email Spam classification domain. Our focus is on complex Emails, where most of the existing classifiers fail to identify unsolicited Emails. In this paper we consider two probabilistic algorithms i.e. "Bayesian" and "Naive Bayes" and three boosting algorithms i.e. "Bagging", "Boosting with Re-sampling" and "AdaBoost". Initially, the Probabilistic classifiers were tested on the "Enron Dataset" without Boosting and thereafter, with the help of Boosting algorithms. The Genetic Search Method was used for selecting the most informative 375 features out of 1359 features created at the outset. The results show that, in identifying complex Spam massages, "Bayesian classifier" performs better than "Naive Bayes" with or without boosting. Amongst boosting algorithms, "Boosting with Resample" has brought significant performance improvement to the "Probabilistic classifiers".
New segmented image coding method for color images

For the compression of color images, the lossy compression standard JPEG has two major shortcomings. Firstly, it codes the color components separately. This reduces the possible compression ratio. Secondly, the image quality at very low bit rate is seriously degraded by blocking artifacts. The paper describes an extension of an existing segmented image coding (SIC) method for monochrome images to images containing a limited number of colors. The new technique codes the colors more efficiently and performs better at high compression. After conversion to the YUV color space, the corresponding luminance image is compressed using monochrome SIC. At the decoder, this image is reconstructed and the gray-values are translated into colors. Because different colors in the image can have the same gray-values and because SIC is lossy, some gray-values may be wrongly reconstructed in the decompressed image. To prevent the introduction of foreign colors in a region, i.e. colors which are not present in the region in the original image, bit vectors are constructed indicating which colors are present. The decoder uses this information to replace foreign colors by colors of the region. The bit vectors are compressed using a lossless bi-level coding scheme. The paper presents experimental results which show that the new method produces a much better subjective image quality than JPEG at high compression due to the absence of block distortion.
Specifications and Analysis of the Korean Sentiment Analysis Corpus

This paper describes the two year endeavor of constructing the Korean Sentiment Analysis Corpus (KOSAC), focusing on the theoretical background and the analysis of the corpus itself. Our aim is to provide a solid theoretical background for the corpus which reflects the characteristics of the Korean language and includes approximately 7,744 sentences taken from news articles. The corpus annotation scheme, based on the MPQA, is described along with the statistics of features specified in the corpus. The analysis of the corpus can be a starting point for how to utilize the corpus not only for sentiment analysis but also for semantic or pragmatic work in terms of speaker’s attitude and emotional expressions.