Miracle-dz/UniFAR-FTUs huggingface dataset:
Infrared Image Enhancement using H Bounds for Surveillance Applications

In this paper, two algorithms have been presented to enhance the infrared (IR) images. Using the autoregressive moving average model structure and optimal bounds, the image pixels are mapped from the IR pixel space into normal optical image space, thus enhancing the IR image for improved visual quality. Although -based system identification algorithms are very common now, they are not quite suitable for real-time applications owing to their complexity. However, many variants of such algorithms are possible that can overcome this constraint. Two such algorithms have been developed and implemented in this paper. Theoretical and algorithmic results show remarkable enhancement in the acquired images. This will help in enhancing the visual quality of IR images for surveillance applications.
COMPUTER SIMULATION OF ULTRASONIC SCATTERING AND TEXTURE IN B-MODE IMAGES JiminZhang Echo Ultrasound Reedsville,

Diagnostic ultrasound has been widely used in clinical applications, such as soft tissue abnormality detection and blood flow detection. However, due to the complexity of ultrasonic wave propagation and scattering in biological tissues which generally involves wave reflection, refraction, scattering, absorption and other wave phenomena, it has always been a rather difficult task to interpret the wave mechanics through the received scattering signals.
Study on Phonation Featur Tibetan and

This paper is concerned with the study of phonation features of Mongolian, Tibetan and Yi language, which are languages in China. Among these languages, Tibetan and Yi are tone languages, which belong to Sino-Tibetan language family. Mongolian is not a tone language, which belongs to Altaic language family. The method of acoustical analysis includes: 1) inverse-filtering, which is used to extract glottal source from the speech sound; 2) spectrum tilt (ST) calculating, which is used to quantitate the spectrum feature by dB/oct; and 3) parameter extracting, which is used to extract the parameters of fundamental frequency (F0), speed quotient (SQ) and open quotient (OQ). The result shows: 1) the differences of phonation features exist among these three languages; 2) there are great differences between the phonations of male and female; 3) the correlation of the phonation parameters is studied, from which the nature of these parameters can be described and defined.
Comparative bio-efficacy of newer insecticides and bio-pesticides against leafhopper, Empoasca kerri Pruthi of groundnut in semi-arid region of India

In an investigation carried out during Kharif 2018, bio-efficacy of nine insecticides and bio-pesticides (including control) were tested against major sucking pest Leafhopper, Empoasca kerri Pruthi of groundnut at S.K. N. College of Agriculture, Jobner (Rajasthan). On the basis of mean per cent reduction in leafhopper population the treatment of imidacloprid 17.8SL (84.31%) was found most effective, followed by thiamethoxam 25WG (81.66%) and acetamiprid 20SP (80%) and were also statistically at par with each other in their efficacy. Bio-pesticides Metarhizium anisopliae 1.15 WP (33.56%) and NSKE (Neem Seed Kernel Extract) (49.68%) were proved least effective, however both were differed significantly with each other. The descending order of effectiveness of treatments were as imidacloprid > thiamethoxam > acetamiprid > methyl demeton > fipronil > acephate > NSKE > Metarhizium anisopliae. The highest pod yield was obtained from the plot treated with insecticide imidacloprid (27.16 qha) followed by thiamethoxam (26.58 qha) and acetamiprid (26.11 qha) and were statistically at par with each other.
Scientific Progress as Diffusion on a Social Network : an Empirical Case Study

Although the spread of scientific theories is a prerequisite for scientific progress, little is known about the way in which science spreads within the scientific community. To understand and stimulate scientific progress it is therefore key to understand how a scientific hypothesis diffuses in the scientific community. In this paper, I analyze the diffusion of one particular scientific hypothesis, namely the hypothesis on the strength of weak ties, introduced by Granovetter in 1973. From data on 6296 scientists and their reference behaviour, a network is constructed that reveals the diffusion process. It is found that the hypothesis is used by different communities of scientists, each with their own key-narrative into which the hypothesis is fit. The diffusion within the communities shares similarities with the diffusion of an innovation in which innovators, early adapters and a majority can be distinguished. Furthermore, it is found each community contains a hub scientist that is disproportionally often referenced and is largely responsible for carrying the hypothesis into his scientific subfield. As a result, each community can be represented by its hub scientists. These results suggest a clear diffusion pattern that can be studied further. Not only can these insight be used to encourage scientific progress, they also give valuable input for the study of diffusion in general.
Survey on : Rule Based Phonetic Search for Slavic Surnames

There are different applications in NLP (Natural Language Processing) where searching of surname plays an important role. This paper is the survey about solution to searching algorithms for the databases of communications service providers, person registries, social networks or genealogy. This paper overcomes the problem of phonetic algorithm for Slovak and (territorial) neighbouring languages (Czech, Polish, Ukrainian, Russian, German, Hungarian, Jewish) surnames. This solution provides high precision and recall for searching surnames in these languages.
Nondefault Bond Spread and Market Trading Liquidity ∗

We examine the relationship between the nondefault component of corporate bond spread and liquidity measures constructed from intraday transactions data, with the default component controlled by the term structure of credit default swap (CDS). We explicitly control for the unobservable firm heterogeneity and conventional bond characteristics, in order to identify the stochastic liquidity effects over time across bonds issued by the same firm. We find a clear positive significant relationship between the illiquidity of intraday trading and nondefault bond spread. These trading liquidity effects identify a unique component of basis spreads that is uncorrelated with conventional liquidity proxy variables, particularly for highly-rated investment-grade bonds. Furthermore, nondefault bond spread is relatively high at the short end of the maturity and increasing with the bond age. The impacts of transaction based liquidity measures are robust to whether swap yield or Treasury yield is used as risk-free rate. JEL Classifications: G12, G13, G14
A coupled pelagic-benthic-sympagic biogeochemical model for the Bering Sea: documentation and validation of the BESTNPZ model (v2019.08.23) within a high-resolution regional ocean model

Abstract. The Bering Sea is a highly productive ecosystem, supporting a variety of fish, seabird, and marine mammal populations as well as large commercial fisheries. Due to its unique shelf geometry and the presence of seasonal sea ice, the processes controlling productivity in the Bering Sea ecosystem span the pelagic water column, the benthic sea floor, and the sympagic sea ice environments. The BESTNPZ model has been developed to simulate the lower trophic level processes throughout this region. Here, we present a version of this lower trophic level model coupled to a three-dimensional regional ocean model for the Bering Sea. We quantify the model's ability to reproduce key physical features of biological importance as well as its skill in capturing the seasonal and interannual variations in primary and secondary productivity. We find that the ocean model demonstrates considerable skill in replicating observed horizontal and vertical patterns of water movement, mixing, and stratification, as well as the temperature and salinity signatures of various water masses throughout the Bering Sea. It is also able to capture the mean seasonal cycle of primary production observed on the data-rich eastern middle shelf. However, its ability to replicate domain-wide patterns in nutrient cycling, primary production, and zooplankton community composition, particularly with respect to the interannual variations that are important in a fisheries management context, remains limited.
Analysing Non-Linear Flutter Vibrations Using System Dynamic Approach

The objective of this work is to investigate the dynamic behaviour of aero-elastic vibrations in the presence of non-linear stiffness such as free-play mechanism, softening or hardening stiffness. A closed loop dynamic system is proposed to represent the phenomenon of flow-structure interaction. In this approach a transfer function for generating the aerodynamic forces based on the structural response is constructed in the feedback loop of the dynamic system with the aid of Padé rational function. The effects of the non-linear factors therefore can be included conveniently in time domain simulation and the stability of limit cycle oscillations (LCO) can be analysed accurately.
Simulation of Hydro-Climatic Variables by AOGCMs, Evaluating the Range of Changes in Variables, Due to Climate Change, in Upper Dez (Dez Oliya) Basin

Introduction Climate change, in addition to increasing temperature, causes changes in the duration, intensity, form, and timing of rainfall in different parts of the Earth, which can cause droughts and floods. It also changes the volume, duration, and runoff duration, which will bring about many developments and changes in the water-resource management (Kamal and Massah Bavani, 2009). In order to reduce inconsistencies, studying the impacts of climate change on water resources is necessary. One of the most widely used models for these future studies is Atmosphere-ocean General Circulation Models (AOGCMs) (Wilby and Harris, 2006). In general, most studies present a high degree of uncertainty as a result of using AOGCMs in the simulation of climate change and hydrology parameters. the outputs of simulated water-resource models under climate change could be relied on when the uncertainties are taken into account at all stages (Semenov and Stratonovitch, 2010). In order to innovate the research method, in this study 10 AOGCMs under three greenhouse gas emission scenarios (A1B, A2, and B1) were used in the simulated range of hydro-climatic variables through climatic models and downscaling methods in the Dez Oliya basin during 2040-2069. In Summary, when reviewing the range of hydro-climatic variable changes in the future period, it could be observed that the uncertainty of AOGCMs under all three emission scenarios is greater than the downscaling methods.
MATHEMATICAL MODELLING OF BLOOD FLOW IN A STENOSED ARTERY UNDER MHD EFFECT THROUGH POROUS MEDIUM

Abstract In this investigation, a mathematical model for studying oscillatory flow of blood in a stenosed artery under the influence of transverse magnetic field through porous medium has been developed. The equations of motion of blood flow are solved analytically. The analytical expressions for axial velocity, volumetric flow rate, pressure gradient, resistance to blood flow and shear stress have been derived. These expressions reveal significant alterations in blood flow due to stenosis. It is seen that magnetic field significantly controls the flow patterns. We have incorporated the magnetic field perpendicular to the flow of blood. The concept of porous medium is also taken into consideration which takes care of the suction factor. The effects of various parameters particularly magnetic number and porosity constant on the blood flow through stenosis have been examined. To validate the analytical results, numerical experiment is performed. The results obtained in the investigation are in reasonably good agreement with experimental findings existing in the literature.
Elmore-based Interconnect Delay Models

Interconnects are playing a major role in determining the delay of today designs, being necessary to accurately generate fast estimates of interconnect delay in different level of abstractions. However being very pessimistic for nodes close to the driver, Elmore delay is the most widely used metric. This paper presents a comparative study about 3 different Elmore Delay Based methodologies. Results show that the interconnect delay estimation can be improved in relation to the traditional Elmore delay estimates without greatly increasing the execution times.
Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.
Controlling T he N on-parametric M odeling of Double Link Flexible Robotic Manipulator using Hybrid PID T uned by P-Type

The wide advancement in different field of life such as domestics and industries make an incredible demand for flexible robot controller. Numerous robot controller applications are categorized as multiple-input-multiple-output (MIMO) frameworks owing to multi-link structure. The design and tuning of multi-loop controllers to meet certain conditions are regularly the pullback factors since there are interaction between the controllers. The framework must be decoupled to diminish the interaction or to form the framework diagonally dominant. Additionally, the existence of vibration on flexible structure of robot controller must be treated at the same time. The ceaseless stress delivered by the vibration can lead to structural deterioration, fatigue, instability and performance degradation. In this way, the decrease of vibration on flexible structure of robot controller is of foremost significance. In spite of the fact that numerous analysts Abstract: Utilization of robotic manipulator with multi-link structure encompasses a great influence in most of the present industries. However, controlling the motion of multi-link manipulator has become a troublesome errand particularly once the flexible structure is employed. As of now, the framework utilizes the complicated arithmetic to resolve desired hub angle with the coupling result and vibration within the framework. Hence, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the enhancement on hub angle position and vibration concealment. The research utilised neural network because the model estimation supported NARX model structure. In the controllers’ development, this research focuses on selftuning controller. P-Type iterative learning algorithm (ILA) control theme was enforced to adapt the controller parameters to fulfill the required performances once there is changes to the system. The hybrid of proportionalintegral-derivate (PID) controller was developed for hub motion and end-point vibration suppression of every link respectively. The controllers were tested in MATLAB/Simulink simulation setting. The performance of the controller was compared with the fixed hybrid PID-PID controller in term of input tracking and vibration concealment. The results indicated that the proposed controller was effective to maneuver the double-link flexible robotic manipulator to the specified position with reduction of the vibration at the tip of the DLFRM structure.
Development of Defogging Algorithm for Image

Haze removal algorithm using dark channel prior (DCP) is an effective approach to recover haze-degraded images. However, the output images can sometimes have dim colour thus lose contrast & colour information. In this paper, we proposed an improved defogging algorithm based on DCP. By applying guided filter and multi-scale image fusion, we are able to combine the relevant information from several derived inputs together to restore a hazy image. Experiments show that this enhanced method improves the quality of output images in the case of mild haze and rather great scene depth. The output images not only are more visually-pleasing to naked eyes, but also provide more details to computer vision applications. Keywords-component: haze-degraded image; defogging algorithm; dark channel prior; Contrast Limited Adaptive Histogram Equalization (CLAHE); guided filter; Laplacian pyramid decomposition; image fusion
A Methodology for Evaluation of People Counting Methods based on Video Analysis

People counting based on video analysis may be very useful for many commercial applications, such as in the monitoring of public spaces or in sporting events. However, the methods in the literature tend to only check if the total counting is correct, independent of where each count happens. In this paper, we propose a methodology for the assessment of people counting methods (PCMs) based on video from cameras in a zenith position. Initially, it is required to manually indicate, in a given video, when each person passes into and out of the counting zone, generating the ground-truth data. From this reference data and the output of a PCM for a given video, we propose a greedy algorithm to solve the problem of matching the best tracked people from the reference to the output counting, as modeled in a bipartite graph. Once the matching is performed, the nonsaturated vertices, i.e., people, indicate false positive and negative countings of the method, while the saturated vertices indicate the true positive counting. From these figures, standard measures such as precision, recall and F−score can be automatically computed and may also identify where errors occur. In addition, the use of this methodology on a PCM brings benefits for comparison purposes and adjusting parameters.
Identification and Expression Analysis of Two allene oxide cyclase (AOC) Genes in Watermelon

Allene oxide cyclase (AOC, EC 5.3.99.6) catalyzes the most important step in the jasmonic acid (JA) biosynthetic pathway and mediates plant defense response to a wide range of biotic and abiotic stresses. In this study, two AOC genes were identified from watermelon. Sequence analysis revealed that each of ClAOC1 and ClAOC2 contained an allene oxide cyclase domain and comprised eight highly conserved β-strands, which are the typical characteristics of AOC proteins. Phylogenetic analysis showed that ClAOC1 and ClAOC2 were clustered together with AOCs from dicotyledon, with the closest relationships with JcAOC from Jatropha curcas and Ljaoc1 from Lotus japonicus. Different intron numbers were observed in ClAOC1 and ClAOC2, which may result in their functional divergence. qRT-PCR analysis revealed that ClAOC1 and ClAOC2 have specific and complex expression patterns in multiple organs and under hormone treatments. Both ClAOC1 and ClAOC2 displayed the highest transcriptional levels in stem apex and fruit and exhibited relatively lower expression in stem. JA, salicylic acid (SA), and ethylene (ET) could enhance the expression of ClAOC1 and ClAOC2, particularly that of ClAOC2. Red light could induce the expression of ClAOC2 in root-knot nematode infected leaf and root of watermelon, indicating that ClAOC2 might play a primary role in red light-induced resistance against root-knot nematodes through JA signal pathway. These findings provide important information for further research on AOC genes in watermelon.
Applying Multi-Armed Bandit on Game Development

Multi-armed bandit is an increasingly popular statistical learning paradigm which can be used to make optimization decisions. The video game industry is rapidly growing and one of the fastest growing sectors in the U.S economy. The competitive game industry requires complex algorithms and critical choices to ensure that the latest games capture the attention of demanding consumers. This thesis applies multi-armed bandits to game design and evaluation. After describing multi-armed bandits and game analytics, a sample study evaluating multi-armed bandits on a dynamically changing game is presented. The results demonstrate that the multiarmed bandit algorithm can indeed be applied to game algorithm and used to find optimal design.
THE IMPACT OF A LOCAL COMMUNITY OF PRACTICE ON NEW VENTURE INTERNATIONALIZATION : A CASE STUDY OF THE FINNISH HEAVY METAL MUSIC SCENE

Little is known about how communities of practice influence new venture internationalization. In this paper I address this question by adopting a qualitative theory-building approach, given the limited amount of theory combining communities of practice, social capital and new venture internationalization. Using inductive multiple-case study approach I study the empirical setting of the emergence of Finnish heavy metal ”scene”, in the field of popular music. I find that the community of practice influences new venture internationalization on three levels. On individual level it provides role models, standards and identity. On company level it provides networks and resources. On industry level it provides legitimacy, which in turn provides wider access and a reputation.