Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a Korean Standard Brain Atlas on the basis of Statistical and Probabilistic Approach and Visualization tool for Functional image analysis

The probabilistic anatomical maps are used to localize the functional neuro-images and morphological variability. The quantitative indicator is very important to inquire the anatomical position of an activated legion because functional image data has the low-resolution nature and no inherent anatomical information. Although previously developed MNI probabilistic anatomical map was enough to localize the data, it was not suitable for the Korean brains because of the morphological difference between Occidental and Oriental. In this study, we develop a probabilistic anatomical map for Korean normal brain. Normal 75 blains of T1-weighted spoiled gradient echo magnetic resonance images were acquired on a 1.5-T GESIGNA scanner. Then, a standard brain is selected in the group through a clinician searches a brain of the average property in the Talairach coordinate system. With the standard brain, an anatomist delineates 89 regions of interest (ROI) parcellating cortical and subcortical areas. The parcellated ROIs of the standard are warped and overlapped into each brain by maximizing intensity similarity. And every brain is automatically labeledwith the registered ROIs. Each of the same-labeled region is linearly normalize to the standard brain, and the occurrence of each legion is counted. Finally, 89 probabilistic ROI volumes are generated. This paper presents a probabilistic anatomical map for localizing the functional and structural analysis of Korean normal brain. In the future, we'll develop the group specific probabilistic anatomical maps of OCD and schizophrenia disease.
Analyzing Behavior of Differential Drive Mobile Robot by Using Statistical Tools

Now a day's robotics provides better service to people anywhere in the world in every sector of life. This standard does not come in a day. Day by day the research on 'analyzing behavior' on robotics by roboticists, engineers, physicists, and mathematician made this standard of life. This paper is presented an analytical approach to comparing the performance of mobile robot. A scenario has been considered in which robot is in world coordinate frame along with a definite boundary. The test run has been taken by varying the microcontrollers and microcomputer. Then analyzed the data’s by graphical and numerical methods that predict a slope which confirms that more the advanced device has been used the robot reached the goal earlier.
Enabling Efficient Fuzzy Keyword Search over Encrypted Data in Cloud Computing

As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very frequently. This significant drawback makes existing techniques unsuitable in Cloud Computing as it greatly affects system usability, rendering user searching experiences very frustrating and system efficacy very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud data while maintaining keyword privacy. Fuzzy keyword search greatly enhances system usability by returning the matching files when users’ searching inputs exactly match the predefined keywords or the closest possible matching files based on keyword similarity semantics, when exact match fails. In our solution, we exploit edit distance to quantify keywords similarity and develop two advanced techniques on constructing fuzzy keyword sets, which achieve optimized storage and representation overheads. We further propose a brand new symbol-based trie-traverse searching scheme, where a multi-way tree structure is built up using symbols transformed from the resulted fuzzy keyword sets. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.
Large-Scale Global and Simultaneous Inference: Estimation and Testing in Very High Dimensions

Due to rapid technological advances, researchers are now able to collect and analyze ever larger data sets. Statistical inference for big data often requires solving thousands or even millions of parallel inference problems simultaneously. This poses significant challenges and calls for new principles, theories, and methodologies. This review provides a selective survey of some recently developed methods and results for large-scale statistical inference, including detection, estimation, and multiple testing. We begin with the global testing problem, where the goal is to detect the existence of sparse signals in a data set, and then move to the problem of estimating the proportion of nonnull effects. Finally, we focus on multiple testing with false discovery rate (FDR) control. The FDR provides a powerful and practical approach to large-scale multiple testing and has been successfully used in a wide range of applications. We discuss several effective data-driven procedures and also present efficient strategies to handle various grouping, hierarchical, and dependency structures in the data.
Development of two versions of Chinese hearing in noise test: the Cantonese and mandarin hearing in noise test

Two Chinese versions of the Hearing In Noise Test(HINT) were developed for assessing speech understanding ability of Cantonese and Mandarin speakers - the Cantonese Hearing In Noise Test (CHINT)and the Mandarin Hearing In Noise Test(MHINT)A total of 168 subjects with normal hearing . thresholds participated in the studies. Sentence materials that represent daily conversational speech were created and recorded. These sentences were equated for difficulty by adjusting their presentation levels in noise. Twelve 20-sentence lists with equal phonemic content were created. Using these lists,Reception Thresholds for Sentences(RTSs), defined as the signal-to-noise ratio at which sentences were repeated correctly 50 % of the time,were obtained. RTSs were measured under headphones in quiet and in three noise conditions. For the noise conditions,speech was presented from the front and noise was processed to simulate origination from the front,the right and the left. The noise was fixed at 65 Db(A)and the speech level was adjusted adaptively. Interlist or test-retest reliability was high,suggesting that consistent results can be obtained using any list. Confidence intervals were measured and reported. Norms obtained using the CHINT and MHINT are comparable to those for the English HINT. [
Exactly sparse information filters for simultaneous localization and mapping

NO FULL TEXT AVAILABLE. Access is restricted indefinitely. – – This thesis is concerned with computationally efficient solutions to the simultaneous localization and mapping (SLAM) problem. The setting for the SLAM problem is that of a robot with a known kinematic model, equipped with on-board sensors, moving through an environment consisting of a population of features. The objective of the SLAM problem is to estimate the position and orientation of the robot together with the locations of all the features. Extended Kalman Filter (EKF) based SLAM solutions widely discussed in the literature require the maintenance of a large and dense covariance matrix. Recently, Extended Information Filter (ElF) based SLAM solutions have attracted significant attention due to the recognition that the associated information matrix can be made sparse. However, existing algorithms for ElF-based SLAM have a number of disadvantages, such as estimator inconsistency, a long state vector or information loss, as a consequence of the strategies used for achieving the sparseness of the information matrix. Furthermore, some important practical issues such as the efficient recovery of the state estimate and the associated covariance matrix need further work. The contributions of this thesis include three new exactly sparse information filters for SLAM: one is achieved by decoupling the localization and mapping processes in SLAM; the other two are aimed at SLAM in large environments through joining many small scale local maps. In the first algorithm, D-SLAM, it is shown that SLAM can be performed in a decoupled manner in which the localization and mapping are two separate yet concurrent processes. This formulation of SLAM results in a new and natural way to achieve the sparse information matrix without any approximation. In the second algorithm, the relative information present in each local map is first extracted, and then used to build a global map based on the D-SLAM framework. Both these algorithms, while computationally efficient, incur some information loss. The third algorithm that modifies the global map state vector by incorporating robot start and end poses of each local map, completely avoids the information loss while maintaining the sparseness of the information matrix and associated computational advantages. Two efficient methods for recovering the state estimate and the associated covariance matrix from the output of the ElF are also proposed. These methods exploit the gradual evolution of the SLAM information matrix, and allow the ElF-based SLAM algorithms proposed in this thesis to be implemented at a computational cost that is linearly proportional to the size of the map.
ASF Correction in Long-wave Time Service

The Additional Secondary Factor(ASF) is the major factor affecting accuracy of long-wave time service.while its theoretical complexity and experimental costs too high,not easy to project promotion.The area of the North China Plain was selected as a research example,and a new method which establish ASF data grid and use interpolation algorithm to study the ASF correction was presented.The results obtained are consistent with expected.The experimental results show that: Established regional ASF data grid can help quickly correction ASF for practical engineering applications.
A refined classification approach by integrating Landsat Operational Land Imager (OLI) and RADARSAT-2 imagery for land-use and land-cover mapping in a tropical area

ABSTRACT Producing accurate land-use and land-cover (LULC) mapping is a long-standing challenge using solely optical remote-sensing data, especially in tropical regions due to the presence of clouds. To supplement this, RADARSAT images can be useful in assisting LULC mapping. The fusion of optical and active remote-sensing data is important for accurate LULC mapping because the data from different parts of the spectrum provide complementary information and often lead to increased classification accuracy. Also, the timeliness of using synthetic aperture radar (SAR) fills information gaps during overcast or hazy periods. Therefore, this research designed a refined classification procedure for LULC mapping for tropical regions. Determining the best method for mapping with a specific data source and study area is a major challenge because of the wide range of classification algorithms and methodologies available. In this study, different combinations and the potential of Landsat Operational Land Imager (OLI) and RADARSAT-2 SAR data were evaluated to select the best procedure for LULC classification. Results showed that the best filter for SAR speckle reduction is the 5 × 5 enhanced Lee. Furthermore, image-sharpening algorithms were employed to fuse Landsat multispectral and panchromatic bands and subsequently these algorithms were analysed in detail. The findings also confirmed that Gram–Schmidt (GS) performed better than the other techniques employed. Fused Landsat data and SAR images were then integrated to produce the LULC map. Different classification algorithms were adopted to classify the integrated Landsat and SAR data, and the maximum likelihood classifier (MLC) was considered the best approach. Finally, a suitable classification procedure was designed and proposed for LULC as mapping in tropical regions based on the results obtained. An overall accuracy of 98.62% was achieved from the proposed methodology. The proposed methodology is a useful tool in industry for mapping purposes. Additionally, it is also useful for researchers, who could extend the method for different data sources and regions.
Improved PageRank Algorithm for Ordering Web Pages

This paper analyzed Google's algorithm on PageRank and presented some disadvantages of this algorithm. Those disadvantages are preferring to old pages, ignoring special sites and inaccurate judge of hyperlinks pointed out from one page. Furthermore, the improved algorithm was described. The experiments show that the consideration on evaluating the importance of pages can make an improvement over the original algorithm.
CONTOUR BASED FOREGROUND MOVING OBJECT EXTRACTION IN VIDEO SEGMENTATION

Video segmentation plays an important role in the MPEG-4 standard for multimedia applications. In this paper the video segmentation is done by detecting the edge of the objects of the each frames and keep tracking it. Then morphology motion filter is used to find the edges of the foreground objects. Next, a filling technique is used to connect edge information to generate final object. Though there is no need for the moving camera situation in the surveillance system, it can deal with both still and moving camera situations. The object based or shape based retrieval is better than the other video on MPEG-4 standard. The main objective of selecting the edge based video segmentation is, the object can be easily stored, indexed and retrieved in the MPEG-4 standard for multimedia applications. The edge detection process serves to simplify the analysis of images by drastically reducing the amount of data to be processed, while at the same time preserving useful structural information about object boundaries. Keywords: Video segmentation, MPEG-4, Edge detection
An online approach for feature selection for classification in big data

Feature selection (FS), also known as attribute selection, is a process of selection of a subset of relevant features used in model construction. This process or method improves the classification accuracy by removing irrelevant and noisy features. FS is implemented using either batch learning or online learning. Currently, the FS methods are executed in batch learning. Nevertheless, these techniques take longer execution time and require larger storage space to process the entire dataset. Due to the lack of scalability, the batch learning process cannot be used for large data. In the present study, a scalable efficient Online Feature Selection (OFS) approach using the Sparse Gradient (SGr) technique was proposed to select the features from the dataset online. In this approach, the feature weights are proportionally decremented based on the threshold value, which results in attaining zeros for the insignificant features’ weights. In order to demonstrate the efficiency of this approach, an extensive set of experiments was conducted using 13 real-world datasets that range from small to large size. The results of the experiments showed an improved classification accuracy of 15%, which is considered to be significant when compared with the existing methods.
“I Can Actually Be a Super Sleuth”

Utilizing qualitative data gleaned from focus groups with adolescent girls participating in a cybersecurity summer program (N = 38, mean age = 16.3), this study examines the following research questions: (a) How do adolescent girls perceive the cybersecurity field? (b) What are the promising practices that engage girls in cybersecurity education? Guided by ecological and social role theories, findings reveal that single-sex collaborative settings with encouraging and supportive teachers and female mentors are practices that contribute to girls’ increased interest in the field of cybersecurity. Findings also suggest that an emphasis on creative and collaborative problem-solving processes and the real-world application inherent to cybersecurity are likely to increase girls’ engagement in the field. Results have implications for educators, researchers, and policy makers aiming to close gender gaps in the field of computer science and build interest in cybersecurity, an area of critical national need.
Robust joint nearest subspace for hyperspectral image classification

ABSTRACT This letter presents a novel hyperspectral image (HSI) classification method based on robust joint nearest subspace and contextual prototype learning. First, we present a robust joint nearest subspace method to solve the HSI classification problem by exploiting a set-to-class distance with robust distance metric to consider both spectral and spatial characteristics effectively. Second, we develop an objective function to learn contextual prototypes robustly and present an iteration algorithm to solve it. Based on the learned contextual prototypes, the HSI classification performance can be further improved. Finally, we conduct numerous experiments to validate the effects of different parameters and components and to compare the proposed method with other algorithms on three popular data sets. The experimental results show that the proposed method achieves better performance than other competing algorithms.
Security Analysis of Information Flow for an Object-Oriented Language with Exception Handling

In this paper, we propose a type system for verifying that secret data don’t leak from an object-oriented program with exception handling. Our type system is an extention of the type system [4] which is for a object-oriented language without exception handling. It analyzes information flow taking into account the control flow caused by the exception handling. We show that our type system is sound, that is, all well-typed programs are secure from data leakage.
Study on Spectrum Sensing Based on Maximum Entropy Spectral Estimation

pectrum sensing is the key technology of cognitive radio systems.It must have the short-term nature and the high frequency resolution to reduce the mutual interference between systems.A method of spectrum sensing based on Maximum entropy spectral estimation(MESE) is proposed.Theoretical analysis and simulation results show that the proposed method has the advantage of short-time sensing,high frequency resolution,and low system complexity,which can be used as an optional scheme for the cognitive wireless communications system.
Research of location based on mixed algorithm of weighted centroid and DV-Hop in WSN

The node positioning technology is very important in the research of wireless sensor networks,so how to improve the positioning precision is the objective.The paper analysed the advantages and disadvantages of the distance vector-hop(DV-Hop) and the centroid algorithm,then put forward an algorithm that mixed the weighted centroid and the DV-Hop.Simulated the mixed algorithm in MATLAB.The experimental results show that the accuracy of the mixed algorithm is higher 20% than the DV-Hop and 15% than the centroid in the same experiment environment.
The Mathematical Theory Of Turbulence

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A real time solution for face logging

An intrusion logger is a video surveillance application designed to detect intrusion events and document them by storing times-tamped images in a log. Face loggers, in particular, are focused in grabbing imagery of intruders face, and are typically used to provide inputs to a face recognition system. Commonly, intrusion loggers are supposed to signal an alarm condition as the intrusion occurs, and are therefore required to run in real time and to work continuously for long time periods. In this paper we present a face logging solution capable of detecting and tracking several targets in real time, grabbing face images and evaluating their quality in order to store only the best for each detected target. An evaluation of the performance on a specifically designed test set is provided. (6 pages)
A COMPREHENSIVE SURVEY OF VARIOUS TOPIC MODELS USED IN BUG LOCALIZATION

In software engineering a fault is also called as a bug which leads to a program failure or generates wrong results. The problem can be caused by various factors such as wrong logic, insufficient effort etc. Bug localization technique plays a very vital role in order to find such errors and reduce the effort and time in order to maintain software. The aim of this survey is to look at different existing bug localization techniques so that it can help a software tester to recognize bugs in source code within less time and budget.
The Visual Story Creating The Visual Structure Of Film Tv And Digital Media

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Automatic extraction of moving object in video sequences

A key technique in MPEG 4 is extracting video objects from video sequences. This paper focuses on automatic extraction of moving objects in video sequences. Color segmentation and change detection are combined to extract moving objects in video sequences with pixel wise precision. Region growing in RGB space is used for color segmentation. Statistic modeling is used for motion detection. The change caused by noise is described with a Gaussian model. Then the change caused by the motion is separated from the noisy background because it does not obey the Gaussian model. Simulation results show that the automatic object extraction algorithm presented is promising.