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The coordinates along any fixed direction(s), of points on the sphere $S^{n-1}(\sqrt{n})$, roughly follow a standard Gaussian distribution as $n$ approaches infinity. We revisit this classical result from a nonstandard analysis perspective, providing a new proof by working with hyperfinite dimensional spheres. We also set up a nonstandard theory for the asymptotic behavior of integrals over varying domains in general. We obtain a new proof of the Riemann--Lebesgue lemma as a by-product of this theory. We finally show that for any function $f \co \mathbb{R}^k \to \mathbb{R}$ with finite Gaussian moment of an order larger than one, its expectation is given by a Loeb integral integral over a hyperfinite dimensional sphere. Some useful inequalities between high-dimensional spherical means of $f$ and its Gaussian mean are obtained in order to complete the above proof. A review of the requisite nonstandard analysis is provided., 32 pages; edited to include content from a submitted version that was revised after first review

Background: Results of small trials indicate that fluoxetine might improve functional outcomes after stroke. The FOCUS trial aimed to provide a precise estimate of these effects.Methods: FOCUS was a pragmatic, multicentre, parallel group, double-blind, randomised, placebo-controlled trial done at 103 hospitals in the UK. Patients were eligible if they were aged 18 years or older, had a clinical stroke diagnosis, were enrolled and randomly assigned between 2 days and 15 days after onset, and had focal neurological deficits. Patients were randomly allocated fluoxetine 20 mg or matching placebo orally once daily for 6 months via a web-based system by use of a minimisation algorithm. The primary outcome was functional status, measured with the modified Rankin Scale (mRS), at 6 months. Patients, carers, health-care staff, and the trial team were masked to treatment allocation. Functional status was assessed at 6 months and 12 months after randomisation. Patients were analysed according to their treatment allocation. This trial is registered with the ISRCTN registry, number ISRCTN83290762.Findings: Between Sept 10, 2012, and March 31, 2017, 3127 patients were recruited. 1564 patients were allocated fluoxetine and 1563 allocated placebo. mRS data at 6 months were available for 1553 (99·3%) patients in each treatment group. The distribution across mRS categories at 6 months was similar in the fluoxetine and placebo groups (common odds ratio adjusted for minimisation variables 0·951 [95% CI 0·839–1·079]; p=0·439). Patients allocated fluoxetine were less likely than those allocated placebo to develop new depression by 6 months (210 [13·43%] patients vs 269 [17·21%]; difference 3·78% [95% CI 1·26–6·30]; p=0·0033), but they had more bone fractures (45 [2·88%] vs 23 [1·47%]; difference 1·41% [95% CI 0·38–2·43]; p=0·0070). There were no significant differences in any other event at 6 or 12 months.Interpretation: Fluoxetine 20 mg given daily for 6 months after acute stroke does not seem to improve functional outcomes. Although the treatment reduced the occurrence of depression, it increased the frequency of bone fractures. These results do not support the routine use of fluoxetine either for the prevention of post-stroke depression or to promote recovery of function.Funding: UK Stroke Association and NIHR Health Technology Assessment Programme.

Cloud computing has significantly benefited todays environment of IT industry through its mobility, sustainability, security, cost savings and several other important features. The risk of data loss on cloud in case of dealing with hardware and software is relatively small. However, the issue of data security becomes imminent when we are storing personal data on the cloud which is not transparent to users. In this paper, we introduce a new entity in terms of a virtual machine that provides services and assurance beyond service level agreement (SLA). In the proposed model, a role of data handling and security is assured with association of third party auditor (TPA) by the virtual machine. We further demonstrate the applied technique for encryption, decryption and integrity verification modules. We also upgrade the entropy of the advanced encryption standard (AES) with a variant of secret sharing scheme in the environment of cloud simulator.

An approach is suggested for automating fish identification and measurement using stereo Baited Remote Underwater Video footage. Simple methods for identifying fish are not sufficient for measurement, since the snout and tail points must be found, and the stereo data should be incorporated to find a true measurement. We present a modular framework that ties together various approaches in order to develop a generalized system for automated fish detection and measurement. A method is also suggested for using machine learning to improve identification. Experimental results indicate the suitability of our approach.

This paper describes a seven-node thermal model for transient heat transfer analysis of a solar powered stratospheric airship in floating condition. The solar array is modeled as a three node system, viz., outer layer, solar cell and substrate. The envelope is also modeled in three nodes, and the contained gas is considered as the seventh node. The heat transfer equations involving radiative, infra-red and conductive heat are solved simultaneously using a fourth order Runge-Kutta Method. The model can be used to study the effect of solar radiation, ambient wind, altitude and location of deployment of the airship on the temperature of the solar array. The model has been validated against some experimental data and numerical results quoted in literature. The effect of change in the value of some operational parameters on temperature of the solar array, and hence on its power output is also discussed. (C) 2017 COSPAR. Published by Elsevier Ltd. All rights reserved.

Cervical cancer is an issue of foremost importance globally, specifically affecting the developing nations. Significant advances have taken place with regard to diagnosis of cervical cancer, especially with screening. Appropriate screening measures can thus reduce the incidence of cervical cancer. The most desirable screening technique should be less invasive, easy to perform, cost-effective and cover a wide range of diagnostic icons. Manual liquid based cytology (MLBC) can be considered as one of the suitable technique for screening with the above-mentioned benefits. The aim of the current study was to compare two cervical screening techniques on the basis of different morphological parameters and staining parameters by using modified acetic acid Pap staining to see the possibility of reducing time economy involved in conventional Pap staining (CPS). The study was conducted on a total 88 cases and all were analyzed with both MLBC and CPS. Forty eight cases that were regarded as satisfactory on the basis of Bethesda system by both methods were further recruited for investigation. Their morphological parameters and staining quality were compared and scored according to a scoring system defined in the study. Quality indices was calculated for both staining procedures and smear techniques.

The aim of this study is to investigate the relationship between debt threshold and GDP per capita growth by using Panel Threshold Regression. The sample of the study is G7 (Advanced Countries) from the period of 1995 to 2015. The results suggested that the scale of debt threshold is ambiguous in this study because debt threshold has not significant effect on GDP per capita growth at the threshold level of 62.47%. The magnitude of debt effect is not same on below and above threshold level. If debt is below and above from 62.47%, its significant positive effect on GDP growth at 0.70% and 0.47% respectively. Also, explanatory variables are used in the study include inflation, trade to GDP, gross saving to GNI, government final consumption expenditure and total investment to GDP. There is a mix result of explanatory variables in the study as inflation and trade to GDP have positive effect on GDP per capita growth but does not produce significant result while gross saving and total investment to GDP have significant positive effect on GDP growth. At last, government final consumption expenditure has significant negative effect on GDP growth.

This paper proposes a method that uses both spectral and spatial information to segment remote sensing hyperspectral images. After a hyperspectral image is over-segmented into superpixels, a deep Convolutional Neural Network (CNN) is used to perform superpixel-level labelling. To further delineate objects from a hyperspectral scene, this paper attempts to combine the properties of CNN and Conditional Random Field (CRF). A mean-field approximation algorithm for CRF inference is used and formulated with Gaussian pairwise potentials as Recurrent Neural Network. This combined network is then plugged into the CNN which leads to a deep network that has robust characteristics of both CNN and CRF. Preliminary results suggest the usefulness of this framework to a promising extent.

The present study aimed to prepare mucoadhesive microparticles of furazolidone (antimicrobial agent) using chitosan as a mucin adsorptive polymer and spray drying technology. This study recognized the potential use of furazolidone to directly target stomach (for example against Helicobacter pylori in order to afford low resistant anti helicobacter agent as compared to commonly used antibiotics). Therefore, local mucoadhesive drug delivery systems in the stomach by using easily tailored approach of spray drying was used in this study. In order to get sufficient drug release for effective period of time at the pH conditions of stomach under the influence of H. pylori , the study was conducted at two different pH levels (1.3 and 4.5). The amount of glutaraldehyde (GTA) as a crosslinking agent was also studied to get appropriate particles size, and drug release. By increasing the amount of GTA, particles size and the release were decreased at pH 1.3 as well as at pH 4.5. The pH of the media also showed significant effect on the drug release, i.e. by decreasing the pH the release was increased. Similarly, crosslinking agent showed negative effect on mucin adsorption. However, by reducing the pH from 4.5 to 1.3 the mucin adsorption was also reduced; therefore mucoadhesion increases at pH 4.5 which is desirable for targeting H. pylori as normal stomach pH is expected to increase by the effect of H. pylori . Hence, the data of this research can be considered for future in vivo study.

In computer vision tasks such as, for example, object recognition, semantically accurate segmentation of a particular object of interest (OOI) is a critical step. Due to the OOI consisting of visually different fragments, traditional segmentation algorithms that are based on the identification of homogeneous regions usually do not perform well. In order to narrow this gap between low-level visual features and high-level semantics, some recent methods employ machine learning to generate more accurate models of the OOI. The main contribution of this paper is the inclusion of spatial relationships among the OOI fragments into the model. For this purpose, we employ Bayesian networks as a probabilistic approach for learning the spatial relationships which, in turn, becomes evidence that is used for the process of segmenting future instances of the OOI. The algorithm presented in this paper also uses multiple instance learning to obtain prototypical descriptions of each fragment of the OOI based on low-level visual features. The experimental results on both artificial and real image datasets indicate that the addition of spatial relationships improves segmentation performance.

Automated understanding of human facial expression is an active and concerning research topic. It is expected that in near future full-fledged understanding of human facial expression will enable machines to behave more intelligently. In this paper we proposed a system for automatic facial expression recognition. A consistent combination of Self-Organizing Map (SOM), Learning Vector Quantization (LVQ) and Naive Bayes classifier is developed to recognize facial expression from Cohn Kanade (CK) and Japanese Female Facial Expression (JAFFE) database. Satisfactory experimental results yield the possibility of using this system in real world applications. Proposed methodology shows an accuracy rate of 81.5% for CK dataset and 87.2% accuracy rate for JAFFE dataset.

Road sign recognition is considered to be one of the most fascinating and interesting field of research in intelligent vehicle and machine learning. Road signs are typically placed either by the roadside or above roads. They provide important information in order to make driving safer and easier. This paper proposes an algorithm that recognizes Bangla road sign with a better percentage. The algorithm starts with capture image from real video scene, text detection from images, character segmentation and recognition of characters through shape matrix. The constructed feature vectors for each individual Bangla road sign are learned into a neural network which later classifies new instance of Bangla road sign. The promising preliminary experimental results indicate a positive potential of our algorithm.

Tremendous developments in the telecommunications sector in Bangladesh has been experienced recently and with this overwhelming success, each and every part of Bangladesh is now under cellular coverage. This success motivated us to develop a more reliable and transparent economic infrastructure through the use of cellular services. We proposed a multilayer network-supported framework that completely eliminates the need for paper money for all kinds of economic transactions. This paper proposes an extension of the framework by introducing embedded intelligence through the use of machine learning in order to facilitate the concept of data mining in our application. Because data mining is an important step in knowledge discovery, we can extract hidden and non-trivial information from raw data and predict a pattern that adds additional intelligence in our framework to make it more transparent and free of corruption. In this paper, we have utilized the significant functionalities of decision-tree classifiers in order to predict a transaction pattern for every concerned entity followed by handling all kinds of transactions by electronic means, and continuously monitoring them over the entire network. Satisfactory experimental results on an artificially created data set support the possibilities in real-world implementation.

Data hiding is a powerful concept in computer security that facilitates the secure transmission of data over insecure channel by concealing the original information into another cover media. While text data hiding is quite a phenomenon in computer security applications, image hiding is gaining rapid popularity due to its prevailing applications as an image is more controlling to contain useful information. In this paper, we have carefully investigated the concept of steganography by incorporating image hiding within another image with a secure structural digital signature framework. Our proposed work includes the initial image preprocessing tasks through filtering of the host image followed by embedding of the secret image and description of the image data within the host image. Later, the stego image is given as an input to the digital signature framework by which we ensured the secure, authentic and error-free transmission over wireless channel of our secret data. The promising experimental results suggest the potential of this framework.

In computer vision, semantically accurate segmentation of an object is considered to be a critical problem. The different looking fragments of the same object impose the main challenge of producing a good segmentation. This leads to consider the high-level semantics of an image as well as the low-level visual features which require computationally intensive operations. This demands to optimize the computations as much as possible in order to reduce both computational and communication complexity. This paper proposes a framework which can be used to perform segmentation for a particular object by incorporating optimization in subsequent steps. The algorithm proposes an optimized K-means algorithm for image segmentation followed by balance calculations in multiple instance learning and topological relations with relative positions to identify OOI regions. Finally, a bayesian network is incorporated to contain the learned information about the model of the OOI. The preliminary experimental results suggest a significant drop in the complexity.

An effective input dataset, valid pattern-spotting ability, good discovered pattern evaluation is required in order to analyze, predict and discover previously unknown knowledge from a large data set. The criteria of significance, novelty and usefulness need to be fulfilled in order to evaluate the performance of the prediction and classification of data. Thankfully data mining, an important step in this process of knowledge discovery extract hidden and non-trivial information from raw data through useful methods such as decision tree classification. But due to the enormous size, high-dimensionality and heterogeneous nature of the data sets, the traditional decision tree classification algorithms sometimes do not perform well in terms of computation time. This paper proposes a framework that uses a parallel strategy to optimize the performance of decision tree induction and cross-validation in order to classify data. Moreover, an existing pruning method is incorporated with our framework to overcome the overfitting problem and enhancing generalization ability along with reducing cost and structural complexity. Experiments on ten benchmark data sets suggest significant improvement in computation time and better classification accuracy by optimizing the classification framework.

Bangladesh has achieved a tremendous growth in the telecommunication sector recently in spite of various deficiencies regarding socio-economic infrastructure. Following a number of years of huge development in this sector, each and every parts of Bangladesh is now under cellular coverage and the teledensity is expected to reach at maximum satisfactory level soon. This success motivates us towards a more reliable & transparent economic infrastructure through the use of cellular services and intelligent software systems. This paper proposes multilayer network-supported framework which completely eliminates the need and use of paper notes for all kinds of economic transactions. However, the proposed framework is not a threat at all to the present financial institutions and their fundamental mechanisms. Rather, it would be more transparent and free of corruptions as reliable and efficient monitoring of transactions will be possible by respective authorities. This proposed framework will handle all kinds of transactions by electronic means e.g. Push Pull services offering Short Message Service(SMS), road-side booths containing a web interface. Under this distributed system, each and every entity of the entire economic infrastructure will makes it flexible, robust, secure and lawful. The satisfactory experimental results on a small case scenario supports its potential possibilities in real-world implementation.

Semantically accurate segmentation of an object of interest (OOI) is a critical step in computer vision tasks. In order to bridge the gap between low-level visual features and high-level semantics, a more complete model of the OOI is needed. To this end, we revise the concept of directional spatial templates and introduce regional directional spatial templates as a means of including spatial relationships among OOI regions into the model. We present an object segmentation algorithm that learns a model which includes both visual and spatial information. Given a training set of images containing the OOI, each image is oversegmented into visually homogeneous regions. Next, Multiple Instance Learning identifies regions that are likely to be part of the OOI. For each pair of such regions and for each relationship, a regional template is formed. The computational cost of template generation is reduced by sampling the reference region with a pixel set that is descriptive of its shape. Experiments indicate that regional templates are an effective way of including spatial information into the model which in turn results in a very significant improvement in segmentation performance.

We present a Bayesian network-based tunable image segmentation algorithm that can be used to segment a particular object of interest (OOI). In tasks such as object recognition, semantically accurate segmentation of the OOI is a critical step. Due to the OOI consisting of different-looking fragments, traditional image segmentation algorithms that are based on the identification of homogeneous regions tend to oversegment. The algorithm presented in this paper uses Multiple Instance Learning to learn prototypical representations of each fragment of the OOI and a Bayesian network to learn the spatial relationships that exist among those fragments. The Bayesian network, as a probabilistic graphical model, in turn becomes evidence that is used for the process of semantically accurate segmentation of future instances of the OOI. The key contribution of this paper is the inclusion of domain-specific information in terms of spatial relationships as an input to a conventional Bayesian network structure learning algorithm. Preliminary results indicate that the proposed method improves segmentation performance.