Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of land-use structure fractal dimension based on vector and raster data:A case study of Suzhou City

Land use system is the product of natural and human activities,and typically as a complex nonlinear dynamical system,its structure is irregular,unstable,complex and non-linear.Fractal theory,as a new technique,has been proved to be practical for analyzing irregular and nonlinear objects.The fractal dimension,one of the most important indices in fractal theory,is often calculated from raster data,but most land-use data are stored as vector data.Conversion of vector data to grid images to calculate fractal dimension may result in inaccurate values.Accuracy of the calculation on raster data is closely related to the grain size of the grid images.Taking a case study of the 1:100000 land use data of Suzhou City in 2008,this paper first analyzed the fractal characteristics of the study area by calculating the fractal dimension,investigated the scale effects of land use fractal dimension by changing the grain size of raster data,and then established a quantitative relationship between fractal dimension and the grain size,and lastly used the math model to calculate fractal dimensions from vector data based on the raster data.The results showed that land use structure of Suzhou City followed the general rules of fractal theory,which proved that this method was suitable for the analysis of the characteristics of land use system in such a rapidly urbanizing area.Furthermore,the overall land use degree of Suzhou City was high;human activities have different effects on the different land types.For example,under the influence of human activities the structures of arable land and construction land were relatively simple,but unused land and forest-grassland are quite complex.The morphology of water was less complex than other land use types,indicating that water was more affected by human activities such as water conservation facilities and irrigation ditches.The effect of the grain size on the fractal dimension in this area showed that the fractal dimension increased with expanding grain size,and the result of statistical analysis suggested that the relationship between fractal dimension and the grain size fit with the quadratic-polynomial-model which provided a bridge between the vector data and raster date for the calculation of the fractal dimension.If the vector data were viewed as raster data of 0 m grain size,vector fractal dimension can be calculated from raster data according to the quadratic-polynomial-model.The difference between the calculated results and the fractal dimension values directly using the vector data was minimal.Thus,fractal dimension of vector land use data(the grain size is 0 m×0 m) could be deduced by this relationship within the margin of error.
Validation of MAP-NET—A network analysis tool

Abstract MAP-NET (Measure and Analysis of Personal NETwork) is a computerised instrument for assessment of the personal networks of persons with substance use disorders and related problems, with information on individual, relational and structural levels, designed to give immediate feedback to be used in clinical interviews, and to provide relevant measures for research. The study investigates its validity and reliability, by using a combination of validation strategies including factor analysis and internal consistency of scales, convergent and predictive validity, sensitivity to social desirability and agreement with collateral interviews. The scales demonstrate satisfactory construct validity and acceptable to excellent internal consistency and convergent and predictive validity with relevant other scales. There is low sensitivity to social desirability and other bias. There is substantial agreement with collateral interviews on the most sensitive variables. Thus, MAP-NET has the qualities needed for clinical use and research.
Deconstructing persuasiveness of strategies in behaviour change systems using the ARCS model of motivation

ABSTRACT Persuasive technologies (PTs) motivate behaviour change using various persuasive strategies. However, there is still a dearth of knowledge on how PTs motivate behaviour change and how to design systems to increase their persuasiveness. To provide empirical insight into the mechanism through which PTs persuade, we conducted a large-scale study with 543 participants to investigate the relation between Attention, Relevance, Confidence, and Satisfaction constructs from the ARCS model of motivation and 10 strategies that are commonly used in persuasive systems design. Our results show that the ARCS constructs collectively explain between 82% and 91% of the variance in persuasiveness across the ten strategies. Relevance, followed by Attention, has the strongest association with persuasiveness. The result generalises across gender groups. Therefore, to increase a system’s persuasiveness, designers should focus on designing to increase relevance and to capture user’s attention, while also promoting confidence and a feeling of satisfaction. We contribute to Human–Computer Interaction (HCI) and Persuasive Technology by offering design guidelines for PTs to increase their persuasiveness and hence efficacy.
Correlation between the Sowing Date and the Yield of Maize on Chernozem Soil, in Connection with the Leaf Area Index and the Photosynthesis

Our sowing date experiment took place in the Demonstration Garden of Institution of Plant Sciences, Centre for Agricultural Sciences of University of Debrecen, in 2012-2014. The paper contains data of test year 2014. Our purpose, besides several other examinations, was to observe how sowing date influences the leaf area index and the activity of photosynthesis of maize hybrids, and how those factors affect fruiting. In the experiment we monitored the change of the leaf area index and the photosynthesis of hybrids with four different growing seasons. The results obtained confirm that not only the environmental and agricultural factors in the growing season have effect on the yield, but also other factors like the leaf area index and the photosynthesis are determinative parameters, and all those factors together, modifying the effects of each other, develop average yields. Keywords—Sowing date, hybrid, leaf area index, photosynthetic capacity.
Comparative Analysis of Clustering Techniques for Movie Recommendation

Movie recommendation is a subject with immense ambiguity. A person might like a movie but not a very similar movie. The present recommending systems focus more on just few parameters such as Director, cast and genre. A lot of Power intensive methods such as Deep Convolutional Neural Network (CNN) has been used which demands the use of Graphics processors that require more energy. We try to accomplish the same task using lesser Energy consuming algorithms such as clustering techniques. In this paper, we try to create a more generalized list of similar movies in order to provide the user with more variety of movies which he/she might like, using clustering algorithms. We will compare how choosing different parameters and number of features affect the cluster's content. Also, compare how different algorithms such as K-mean, Hierarchical, Birch and mean shift clustering algorithms give a varied result and conclude which method will suit for which scenarios of movie recommendations. We also conclude on which algorithm clusters stray data points more efficiently and how different algorithms provide different advantages and disadvantages.
RADIOMETRIC QUALITY AND DSM GENERATION ANALYSIS OF CARTOSAT-1 STEREO IMAGERY

CARTOSAT-1, launched in May 2005, is equipped with two cameras, Aft and Fore, tilted along-track by -5° and +26° respectively, allowing the acquisition of stereo data with a geometrical resolution of 2.5 m. The Institute “Area di Geodesia e Geomatica” at Sapienza Universita di Roma, Eurimage S.p.A. and the Institute of Geodesy and Photogrammetry, ETH Zurich participated in the ISPRS-ISRO Cartosat-1 Scientific Assessment Programme (C-SAP), in order to investigate the generation of Digital Surface Models (DSMs) from Cartosat-1 stereo scenes. This paper follows up on the previous work done by the Institutes and shows the analysis of a new standard stereopair belonging to the additional test field TS-Castelgandolfo, acquired on 18th July 2006 over the area of Castelgandolfo, Rome, Italy (30 x 30 km). The images are processed, in order to test both the image quality and the potential for DSM generation, following the procedures already established. The results obtained in all steps of the investigation, particularly those related to image quality, the orientation with different models and software and the effectiveness of image filtering for enhancing the DSM generation are presented and discussed.
Recognizing Named Entities in Text based User Reviews

Named Entity Recognition is a subtask in Information Extraction. In Named Entity Recognition (NER) we try to identify each of the words provided into some categories predefined by us. These categories or classes can be organization, name, time, place etc. Named Entity Recognition is a part of Natural Language Processing which is aims at making human and computer interactions more meaningful and efficient. NLP is nowadays being effectively being used in the area of automatic text summarization, cross language information retrieval, speech recognition and query named entity recognition. This paper focuses on Named entity recognition, how it is performed and a how apply NER to web search queries so that correct and more user personalized results are displayed by the web search engines.
RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 DETECTION OF TUMOR REGION USING FAST FUZZY CLUSTERING ALGORITHM

Magnetic Resonance Imaging (MRI) is a medical imaging technique for measuring the anatomy and functions of the body in detail to providing information of the brain and the brain stem. To understand the complex segmentation among the brain regions we propose a fuzzy clustering algorithm. The algorithm starts with globally denoising the brain images using Kernel algorithm. Next, region will be segmented using the FFC (Fast Fuzzy Clustering) algorithms and detecting the tumor region and type of disease will be detected. The results suggest that the proposed algorithms provide improved performance and segmentation accuracy compared to the existing algorithms
Analysis and Counter Measures of Various Attcks in Secure Network Protocols

: System Protocols are basic to the activity of the Internet and henceforth the security of these conventions is principal. Our work covers the security of three generally sent conventions: Domain Name System (DNS), Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). Our work demonstrates that the outline or usage of a few variations of these conventions are defenseless against assaults that come guarantee their key security highlights. In the greater part of the cases we incorporate exploratory outcomes showing the practicality of our assaults in reasonable system conditions. We propose various countermeasures for the assaults, some of which have just been executed by and by. We begin by depicting the structure of DNS and present various existing DNS security conventions. We at that point center around DepenDNS, a security convention that is proposed to ensure DNS customers against store harming assaults. We show that DepenDNS suffers from operational deficiencies, and is powerless against reserve harming and disavowal of administration assaults. We at that point give a review of Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS), and draw the likenesses and differences between the two conventions . We depict the cushioning prophet idea and present various ongoing assaults against TLS.
Spatial Autocorrelation Patterns of Datum Land Prices of Cities in a Region

We studied the spatial autocorrelation and its pattern of datum land prices of cities in a region,and took Hubei Province as a case study.We employ Moran's I,local Moran's I and Moran's I correlogram to analyze the spatial autocorrelation degree and its change along with contiguity order.We use semi-variance statistic of land price based on route distance to find the spatial autocorrelation scale.We also adopt spatial clustering based on a kind of composite distance to probe into the clustering characteristic of land prices.In the case study,we find datum prices of the cities in Hubei province has faint spatial autocorrelation degree at the first and the second order contiguity.The scale of the autocorrelation is about 200 km in route distance.The spatial distribution of city land prices is a kind of hierarchcal structure.We find that land prices of cities are spatially related with the development,land profit and urban populations by principal component analysis and stepwise linear regression analysis,and exploit the mechanism of the spatial autocorrelation of urban land prices.
Systematic evaluation of CNN on land cover classification from remotely sensed images

In using the convolutional neural network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to validate the effectiveness of the CNN architecture, i.e. AlexNet on land cover classification, based on four remotely sensed land-use land-cover (LULC) datasets. In addition to the evaluation of the impact of a range of parameters in the evaluation tests, the influence of a set of hyperparameters on the classification performance will be assessed. The parameters include the epoch values, batch size, convolutional filter size and input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters. We first explore the number of epochs under several selected batch size values. The impact of the first layer kernel size of the convolutional filters also was evaluated. Moreover, testing assorted sizes of the input images provided insight of the influence of the size of the convolutional filters and the image sizes. To generalize the validation, four remote sensing datasets, AID, RDS, UCMerced and RSCCN7, which have different land coverage and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as the number of classes, amount of labelled data and texture patterns. A specifically-designed, interactive, deep-learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training, evaluation and testing. These results provide opportunities toward better classification performance in various applications, such as hyperspectral multi-temporal agricultural LULC.
Auto-calibration and synchronization of camera and MEMS-sensors

This article describes our ongoing research on auto-calibration and synchronization of camera and MEMS-sensors. The research is applicable on any system that consists of camera and MEMS-sensors, such as gyroscope. The main task of our research is to find such parameters as the focal length of camera and the time offset between sensor timestamps and frame timestamps, which is caused by frame processing and encoding. This auto-calibration makes possible to scale computer vision algorithms (video stabilization, 3D reconstruction, video compression, augmented reality), which use frames and sensor’s data, to a wider range of devices equipped with a camera and MEMS-sensors. In addition, auto-calibration allows completely abstracting from the characteristics of a particular device and developing algorithms that work on different platforms (mobile platforms, embedded systems, action cameras) independently of concrete device’s characteristics as well. The article describes the general mathematical model needed to implement such a functionality using computer vision techniques and MEMS-sensors readings. The authors present a review and comparison of existing approaches to auto-calibration and propose own improvements for these methods, which increase the quality of previous works and applicable for a general model of video stabilization algorithm with MEMS-sensors.
External Quality Assessment of Institutions and Instruments Using a Linear Mixed Model

Jinsook Lim, Sungho Won, Suyeon Park, Jimyung Kim, Sun Hoe Koo, and Gye Choel Kwon Department of Laboratory Medicine, Chungnam National University Hospital, Daejeon; Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul; Department of Biostatistics, Soonchunhyang University Seoul Hospital, Seoul, Korea Background: External quality assessment (EQA) uses a standard deviation index (SDI), based on a peer group, to evaluate laboratory performance. However, evaluations using peer group SDIs often have limited applicability, because they are not statistically valid unless the number of institutions in the same peer group is large. The present study proposes a statistical model for simultaneously evaluating the performance of all participating institutions, as well as the performance of instruments on the market. Methods: By assuming that proficiency test results were affected by the manufacturer, the instrument, and the institution, the effects of those factors were estimated using a linear mixed model. We used these effect estimates to calculate manufacturer, instrument, and institution SDIs. Using simulation, we evaluated the false positive rates and efficiencies of the proposed linear mixed model. Results: Simulations showed that the linear mixed model empirical type I error rates preserved the nominal significance level. This model was also more statistically efficient than the peer group SDI. Rates of unacceptability were lower when using institution SDI than they were when using peer group SDI. Additional outliers that could not be evaluated using the current system were detected by the institution SDI statistic. The instrument SDI statistic detected outliers among different instrument groups. Conclusions: Institution and instrument SDIs are robust and efficient tools for EQA, and they can replace the currently used system of peer group SDI. (J Lab Med Qual Assur 2016;38:43-51)
Towards Automation of Tipping and Cueing Between Small Satellites in a Constellation

The use of low-fidelity sensors of satellites in a constellation for accurate surface target detection has the potential to lower costs while increasing flexibility, replacement time, and fault tolerance. This thesis investigates the possibility of utilizing an array of satellites with a heterogeneous mix of sensor types to optimize the validation process of surface target detection. Generation of synthetic scenes allows identification and extraction of optical features that are useful in remote sensing practices. Features of interest are generated from specified resolutions, representing different sensor types using Rochester Institute of Technology’s Digital Imaging Remote Sensing Image Generation platform. Synthetic images are jittered to varying degrees to represent the pointing stability, which is one measure of performance of low-fidelity sensors. These synthetic images are utilized to train an artificial intelligence platform to automatically detect targets of interest on the earth’s surface. The Berkeley Caffe Convolution-Based Deep Learning open source platform is trained and employed to automatically detect features of interest. Berkeley Caffe is fast, powerful, and wellsupported with deployed projects by major corporations including Pinterest, Google, and others, which makes it particularly valuable for our experiments. The main contributions for this research effort are four-fold: (1) Generation of a dataset which includes synthetic imagery, (2) Schematic of a Tip and Cue Communication Protocol, (3) A satellite imagery classifier with 92.1% test set accuracy, and (4)Successfully modeled uncorrelated jitter impact on classification performance. Through these experiments, we demonstrate that remote sensing platforms can provide features of interest to an artificial intelligence platform to increase overall feature identification
THE REASON ANALYSIS OF MUDSTONE CREEP ON CASING DAMAGE

After high pressure waterflooding in oilfield,absorbed mudstone begin to creep and make nonuniform ground stress act on cement mantle and casing.This paper mainly discusses the additive pressure distributing features of casing and cement mantle together with the reason of casing damage on account of mudstone creep by the method of numerical simulation,put forward that nonuniform ground stress produce stress concentration around the wall of well,cause cement mantle and casing shrinkage and deformation is the direct reason of casing damage ,and the distributing of the first interface pressure is opposition to the orientation of the ground stress and casing deformation trend,that prove cement mantle react on changing pressure boundary condition of casing. $$$$
Application of GIS technology in calculation of river water energy

For calculation of river water energy,it is very important to determine the mean annual flow and fall of reach.The application of GIS technology and DEM data facilitates the acquisition of runoff and elevation data.Taking the upper reach of the Salween River in the northwest of Myanmar for example,DEM data such as Hydrolk,SRTM3 are used in GIS platform to analyze and calculate the water potential and cascade development scheme.
INFLUENCE OF SEASONAL FACTOR ON SAFETY OF GROUNDING SYSTEM

The seasonal factor affects the safety of grounding system, and causes changes in the grounding resistance, the step and touch voltages on the ground surface above the grounding system. The influences of seasonal factor on the safety of grounding system were analyzed in numerical method. In the raining season, the grounding resistance is reduced; but the touch voltage is probably increased. Hence, the influence of raining season on the safety must be considered. In the freezing season, the grounding resistance is increased, as 1.7 to 3.0 times as the normal value when the resistivity of frozen soil is very large. When the thickness of frozen soil layer exceeds the burial depth of grounding system, the grounding resistance, and the step and touch voltages would be increased, and the safety is largely decreased. In highly frozen region, the best solution to ensure the safety is to choose a suitable burial depth which exceeds the thickness of frozen soil layer.
Enhancing EEG Signals in Brain Computer Interface Using Intrinsic Time-Scale Decomposition

A brain–computer interface (BCI) provides a link between the human brain and a computer. The EEG signal is nonlinear and non-stationary. Feature extraction is one of the most important steps in any BCI system; as such, enhancement to the existing methods has been incorporated in this work. For this, we propose a four-class movement imaginations of the right hand, left hand, both hands, and both feet, and develop feature extraction methods utilizing an intelligent method based on intrinsic time-scale decomposition (ITD) and Artificial neural networks (ANN). Based on the processed electroencephalography (EEG) data recorded from nine subjects, ITD accurately classified and discriminated the four classes of motor imagery; the average accuracy achieved is 92.20%.
Trust Based Cyber-Physical Systems Network Design

Cyber-physical systems (CPS) extensively share information with each other, work collaboratively over Internet of Things, and seamlessly integrated with human society. Designing CPS requires the new consideration of design for connectivity where security, privacy, and trust are of the main concerns. Particularly trust can affect system behavior in a networked environment. In this paper, trustworthiness is quantitatively measured by the perceptions of ability, benevolence, and integrity. Ability indicates the capabilities of sensing, reasoning, and influence in a society. Benevolence measures the genuineness of intention and reciprocity in information exchange. Integrity captures the system predictability and dependability. With these criteria, trust-based CPS network design and optimization are demonstrated.