Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of a visual discrimination model to detect compression artifacts in virtual pathology images

A major issue in telepathology is the extremely large and growing size of digitized "virtual" slides, which can require several gigabytes of storage and cause significant delays in data transmission for remote image interpretation and interactive visualization by pathologists. Compression can reduce this massive amount of virtual slide data, but reversible (lossless) methods limit data reduction to less than 50%, while lossy compression can degrade image quality and diagnostic accuracy. "Visually lossless" compression offers the potential for using higher compression levels without noticeable artifacts, but requires a rate-control strategy that adapts to image content and loss visibility. We investigated the utility of a visual discrimination model (VDM) and other distortion metrics for predicting JPEG 2000 bit rates corresponding to visually lossless compression of virtual slides for breast biopsy specimens. Threshold bit rates were determined experimentally with human observers for a variety of tissue regions cropped from virtual slides. For test images compressed to their visually lossless thresholds, just-noticeable difference (JND) metrics computed by the VDM were nearly constant at the 95th percentile level or higher, and were significantly less variable than peak signal-to-noise ratio (PSNR) and Structural Similarity (SSIM) metrics. Our results suggest that VDM metrics could be used to guide the compression of virtual slides to achieve visually lossless compression while providing 5 to 12 times the data reduction of reversible methods.
Economic impact of the Internet: study of cybercafés in Gaborone, Botswana

This paper discusses the findings of a study that was carried out to determine the economic impact of cybercafes in Gaborone, Botswana. The research design was a survey. The results from the study showed that the cybercafe sector in Gaborone, Botswana, has grown by 1,300 per cent since 2001. The sector is contributing to the gross domestic product of Botswana largely through employment, government taxation and earning the country foreign exchange through e‐commerce and e‐business transactions. However all was not going well for cybercafes as they were faced with problems of competition, high tariffs, low bandwidth, and high equipment costs, among others. The study recommended the need for the coordination of cybercafe sector in order to make its growth less haphazard; reduction of taxes on computers; enactment of cyber law, modernization and improvement of bandwidth.
Estimating risk levels for vulnerability categories using CVSS

Objective and automated means for security measurement are becoming essential for security management. The security level of any system can be measured in terms of risk level posed by the presence of vulnerabilities in it. The process can be further improved, if well classified vulnerability datasets are used. With classified vulnerability data, multiple vulnerabilities of same genre can be addressed simultaneously that in turn increases objectivity and scope of security management. In this paper, we proposed an approach to measure severity level of vulnerability categories and develop metrics to estimate risk levels of vulnerability categories. The proposed approach re-evaluate and unify risk levels of vulnerabilities present in a vulnerability category based on vulnerability characteristics, vulnerability population, availability of patches and age of vulnerability to estimate risk level of category. Developed metrics are applied on real vulnerability data repository by NVD and risk levels estimated for 23 vulnerability categories under which NVD classify vulnerability data.
A Cost-Minimization Model for Multicharacteristic Component Inspection

Abstract In this paper a mathematical model is developed for determining the optimal number of inspections for multicharacteristic components where failure can be catastrophic. The model optimizes the expected total cost per accepted component resulting from (1) type I errors, (2) type II errors, (3) cost of added inspection, and (4) ordering of quality characteristics for inspection. The model considers components with several characteristics to be inspected. Failure to meet the quality requirements of any one characteristic results in the rejection of the component. Taking into consideration all three costs referred to above, a mathematical expression for expected total cost per accepted component is obtained. Also, an expression is developed for finding the optimal sequence of characteristic inspection. Finally, a computational procedure is outlined to determine the optimal sequence of characteristic inspection and the optimal number of inspections using the two expressions stated above.
An Image Retrieval Algorithm Based on Region Segmentation

Visual image, as a kind of rich content and performance of multimedia information, has been tremendously popular for a long time. Image retrieval technology is complicated than text retrieval, due to text-based image retrieval is often need manual annotation, so very laborious and individual subjective factors are there. In order to solve these problems, this paper puts forward a kind of image retrieval algorithm based on improved region segmentation. First, use of image segmentation technology, dividing the image into several regions, then to match each region and the being tested image, and obtained retrieval results in the end. It can be seen through experiment, the user only needs to submit a retrieval image, so it can greatly reduce the user's retrieval burden, and improve the efficiency of retrieval.
Megahertz processing rate for Fourier domain optical coherence tomography using a graphics processing unit

We developed the ultra high-speed processing of FD-OCT images using a low-cost graphics processing unit (GPU) with many stream processors to realize highly parallel processing. The processing line rates of half range FD-OCT and full range FD-OCT were 1.34 MHz and 0.70 MHz for a spectral interference image of 1024 FFT size x 2048 lateral A-scans, respectively. A display rate of 22.5 frames per second for processed full range images was achieved in our OCT system using an InGaAs line scan camera operated at 47 kHz.
An easy-to-use mismatch model for the MOS transistor

In this paper, a physics-based mismatch model is presented. It is demonstrated on a 0.18-/spl mu/m technology that a simple mismatch model can still be used to characterize deep-submicron technologies. The accuracy of the model is examined and found to be within 20% in the strong inversion region. Bulk bias dependence is modeled in a physical way. To extract the mismatch parameters, a weighted fit is introduced. It is shown that the width and length dependence of the mismatch parameters is given by the Pelgrom model.
Fuzzy-genetic algorithms and mobile robot navigation among static obstacles

The paper describes a fuzzy genetic algorithm in which a fuzzy logic controller (FLC) is used with genetic algorithms (GAs) to find obstacle-free paths in a number of find-path problems of a mobile robot. In this algorithm, an obstacle-free direction for the movement of a robot locally is created using an FLC and the extent of travel along obstacle-free direction is determined by a GA. Here, the fuzzy logic approach is used to create initial population and GA crossover and mutation operators. This algorithm is found to perform better than the popular steepest descent approach. The proposed algorithm also finds solutions close to the best known tangent graph with A* algorithm from the accuracy point of view. However, the proposed algorithm finds a near-optimal solution faster than the tangent graph and A* algorithm. Moreover, the proposed approach shows how genetic operators can be modified with problem-specific information to create a search algorithm which is efficient for the particular application.
Maximum Likelihood Decoding and Burst Error Correction

Abstract : A brief discussion of basic encoding and decoding on noisy channels is presented to provide a background for the experimental portion of this research. A partitioned 3 state Gilbert model is used to model a burst channel and a method of calculating error sequence probabilities using this model is introduced. Error sequence probability calculations are made using a (7.3) maximal length code and a (15,7) BCH code. Observations are made about the general type of decoding rule to use to give the lowest probability of decoding error on burst channels when using an interleaving technique.
The Catastrophic Antiphospholipid Antibody Syndrome: An Unusual Presentation of Spontaneous Acute Intracranial Haemorrhage

Background: Catastrophic Antiphospholipid syndrome (CAPS) is a scarcely understood entity with a high mortality approaching 50%The rapid deterioration is triggered by a deadly cytokine storm, which is difficult to recognize and almost impossible to treat in time. Various regimens have been advocated, which include administration of monoclonal anti-phospholipid antibodies and pulses of high dose methyl prednisolone. However, these treatment modalities are only effective if the disease is diagnosed early and instituted. Clinical presentation: We present an unusual case of multiple spontaneous intracranial haematomas in a young individual, with evidence of severe refractory coagulopathy in a background of multisystem dysfunction. A retrospective analysis of the subsequent rapid deterioration and resultant fatality provided us with important learning points for early diagnosis and management of triggers which can initiate and lead to a cascade of events that eventually end in death. Conclusion: CAPS is a fatal disease which should be considered when dealing with spontaneous intracerebral bleeds with multisystem dysfunction in the background of coagulation derangements. Rapid treatment with steroids and monoclonal antibodies may be helpful in halting the progression of the disease and eventually help in repressing a fatal outcome. Identifying the trigger factors, that usually initiate the downhill cascade, are a priority, and avoiding such provocative factors is an important aspect of management. This will help in the prevention of morbidity and eventual mortality in such patients.
Application of GPU to computational multiphase fluid dynamics

The MARS (Multi-interfaces Advection and Reconstruction Solver) [1] is one of the surface volume tracking methods for multi-phase flows. Nowadays, the performance of GPU (Graphics Processing Unit) is much higher than the CPU (Central Processing Unit). In this study, the GPU was applied to the MARS in order to accelerate the computation of multi-phase flows (GPU-MARS), and the performance of the GPU-MARS was discussed. From the performance of the interface tracking method for the analyses of one-directional advection problem, it is found that the computing time of GPU(single GTX280) was around 4 times faster than that of the CPU (Xeon 5040, 4 threads parallelized). From the performance of Poisson Solver by using the algorithm developed in this study, it is found that the performance of the GPU showed around 30 times faster than that of the CPU. Finally, it is confirmed that the GPU showed the large acceleration of the fluid flow computation (GPU-MARS) compared to the CPU. However, it is also found that the double-precision computation of the GPU must perform with very high precision.
Factors Influencing Knowledge Sharing in Information Technology Projects

Even though today's corporations recognize they need to understand modern project management techniques, many researchers continue to provide evidence of poor success in information technology (IT) projects. Given that project performance is known to be positively associated with project knowledge, a better understanding of how to effectively manage knowledge in IT projects should have considerable practical significance for increasing the chances of project success. Using a combined qualitative/quantitative method of data collection in multiple case studies spanning four continents and comprising a variety of organizational types, the focus of this current research centered on the question of why individuals working within IT project teams might be motivated towards, or inhibited from, sharing their knowledge and experience in their activities, procedures, and processes. The research concluded with the development of a new theoretical model of knowledge sharing behavior, the Alignment Model of Motivational Focus. This model suggests that an individual's propensity to share knowledge and experience is a function of perceived personal benefits and costs associated with the activity, balanced against the individual's alignment to a group of institutional factors. These factors are identified as alignments to the project team, to the organization, and, dependent on the circumstances, to either the professional discipline or community of practice to which the individual belongs.
A Clustering Method for Web Mining Based on Probabilistic Latent Semantic Indexing

Exploring an intranet or internet database enables us to discover useful knowledge. In this process, a search engine plays a pivotal role. To this end, various search engines have been proposed to heighten information accuracy by exploiting key content relations in semantic web resources. But a general-purpose search engine always includes useless or irrelevant web pages in the search results. The next generation of web architecture, known as Semantic Web, can build a layered architecture to possibly mitigate this deficiency by decreasing the noisy data in a searched result. The objective of this paper is to propose a Probabilistic Latent Semantic Indexing (PLSI) method used in semantic web search engines. The method can better return appropriate information for user queries; in particular, a novel ranking strategy is provided to measure the relevance score of an annotated set of web results by considering user queries, data annotation, and the underlying ontology.
Mass Transfers in a Fluidized Bed Bioreactor using Alginate Beads for a Future Bioartificial Liver

Fluidized bed bioreactor with alginate beads may be an alternative to hollow fiber cartridge to host hepatocytes for bioartificial liver purposes. After the bioreactor design and the characterization of fluid mechanics, the present study was aimed at analyzing bi-directional mass transfers of calibrated species between external fluid and empty beads. Static (batch) and dynamic (fluidized bed bioreactor) experimental conditions were analyzed. A simple modelling approach permitted the definition of mass transfer coefficients. The motion of beads within the bioreactor clearly enhanced mass transfer kinetics, but did not alter the amount exchanged. The shear enhanced diffusion coefficient for VitB12 was 20 times higher in the fluidized bed bioreactor than under batch conditions, proving the efficiency of such a device.
FPGA implementation of block truncation coding algorithm for gray scale and color images

This paper presents a field programmable gate array (FPGA) implementation for video compression using block truncation coding (BTC) image compression technique [E. Delp et al., 1979]. The implementation exploits the inherent parallelism of the BTC algorithm to provide efficient algorithm-to-architecture mapping. The implementation is shown for gray scale images and promoted to color ones. The Xilinx VirtexE BTC implementation has shown to provide about 23.4 /spl times/ 10/sup 6/ of pixels per second which is about 3500 times faster than an Intel Pentium III 550 MHz processor.
Exact Analysis of a Two Stage Production Line with Parallel Unreliable Machines

This paper proposes a new analytical model of manufacturing system with two parallel machine workstations. The machines in the system are non-identical unreliable machines, and the processing times, failure times and repair times of the machines are assumed to be exponentially distributed. In this paper we for the first time give an exact model of two workstation parallel machine system. Case studies show the accuracy of the proposed model.
Compiling for EPIC architectures

Designing compilers for Explicitly Parallel Instruction Computing (EPIC.) architectures presents challenges substantially different from those encountered in designing compilers for traditional sequential architectures. These challenges are addressed not only by employing new optimizations that are specific to EPIC, but also by employing new ways to architect compilers. EPIC architectures provide features that allow compilers to take a proactive role in exploiting instruction level parallelism. Compiler technology is intimately intertwined with the target processor architecture, and compiler architects must solve new analysis and optimization problems to achieve the highest levels of performance. When complex optimizations are uniformly applied to large applications, the resulting slow compile speeds are unacceptable. Demanding requirements to produce high-quality code at high compile speed shapes the fundamental structure of EPIC compilers.
A novel probabilistic lower bound on mutual information applied to a category-based framework for spike train discrimination

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy A NOVEL PROBABILISTIC LOWER BOUND ON MUTUAL INFORMATION APPLIED TO A CATEGORY-BASED FRAMEWORK FOR SPIKE TRAIN DISCRIMINATION By Nathan D. VanderKraats December 2009 Chair: Arunava Banerjee Major: Computer Engineering We propose a framework by which the information transmission capacity of feedforward networks of spiking neurons can be measured for any discrete set of stimulus classes, or categories, using a statistical classifier to decode stimulus information. Network performance is measured using mutual information (MI), which is estimated using both a naı̈ve approach inspired by Fano’s inequality and one which levies an intermediate, real-valued data representation common in many classification techniques. We establish the latter through a result of independent interest: a novel probabilistic lower bound on the MI between a binary and a continuous random variable. The bound is derived from the the Dvoretzky, Kiefer, and Wolfowitz inequality for the difference between an empirical cumulative distribution function and the true distribution function. Our framework is demonstrated on a model of the early human auditory system, using a variety of auditory categories and a phenomenological model of a 400-neuron auditory nerve simulated by the Meddis Inner-Hair Cell model. During classification, in addition to the standard spike count representation for a population of spike trains, we establish the utility of other novel feature spaces. We find that basic networks with random synaptic weights are surprisingly effective at transmitting stimulus information.
A fast match for continuous speech recognition using allophonic models

In a large vocabulary real-time speech recognition system, there is a need for a fast method for selecting a list of candidate words from the vocabulary that match well with a given acoustic input. The authors describe a highly accurate fast acoustic match for continuous speech recognition. The algorithm uses allophonic models and efficient search techniques to select a set of candidate words. The allophonic models are derived by constructing decision trees that query the context in which each phone occurs to arrive at an allophone in a given context. The models for all the words in the vocabulary are arranged in a tree structure and efficient tree search algorithms are used to select a list of candidate words using these models. Using this method, the authors are able to obtain over 99% accuracy in the fast match for a continuous speech recognition task which has a vocabulary of 5000 words. <<ETX>>
Operational land cover change detection using change vector analysis

This research study introduces the use of a change detection and classification algorithm that relies on the change vector analysis (CVA) method. Its implementation aims to ensure adequate response to operational production needs and allow optimized data processing over extended and environmentally complex areas. Automatic change class labelling relies on the use of a (3n+2)‐dimensional feature space, where n denotes the number of sensor bands. Such enhanced feature space allows for a finer and more accurate definition of change classes of the ‘from‐to’ type. Moreover, and to efficiently address the problem of change area overestimation, the proposed method takes into account specific evidence derived from the pixel's geographic neighbourhood, the latter defined as a 3×3 pixel kernel. The performance of this integrated algorithmic approach has been tested and validated in the framework of the CORINE Land Cover‐Greece 2000 and the ESA/GSE Forest Monitoring projects in three test sites located in the outskirts of the city of Ptolemais, Thasos island and the suburbs of Athens in Greece. Its implementation in such highly fragmented and dynamically changing landscape environments has resulted in qualified and accurate land cover change maps, achieving an overall level of classification accuracy of 88–96%. Compared to visual image interpretation, the method requires half the effort. In conclusion, the proposed method has proved effective and can be recommended for use in the framework of operational projects.
Decoding high-frequency components of the input signal from slowly spiking simulated neurons

It is well known that neurons in the sensorimotor cortex of primates tend to synchronously activate and generate a pulsating motor command, and such rhythmic cortical drive induces voluntary contraction of muscles. Using frequency analysis, numerous experimental data have revealed that even a single neuron in the motor system transmits a rhythmic signal with a frequency much higher than its own spike rate, although this seems odd when we consider the sampling theorem. To prove such physiological findings, we investigated the transmission mechanism of a neural signal using both mathematical and simulation approaches. For the mathematical consideration, we sampled an analytical signal as both an evenly spaced and unevenly spaced time series in order to simulate regularly and irregularly spiking neurons. The results of the discrete Fourier transform suggest that only data sampled at irregular intervals contained the exact spectrum of the input signal under the condition where the sampling rate was even lower than the input frequency. By observing the signal structure, we concluded that harmonics-cancellation was achieved due to irregular sampling intervals. We also simulated both regularly and irregularly spiking neurons using Hodgkin. Huxley's neural membrane model. When we applied sinusoidal current inflow of 135.0 Hz to the neuron, the neuron spiked with constant intervals (this case is comparable to the regularly sampled data). In this case, the spectrum did not reflect the input signal. However, in the case of sinusoidal input with frequency of 132.0 Hz, the spectrum showed the input frequency content. The neural spike rate (that is, the output frequency), was much lower than that of the input frequency. From these results, we concluded that fluctuation of spiking in neural output might play an important role for transmitting information in the input signal.