Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementasi Pengolahan Citra dan Klasifikasi K-Nearest Neighbor untuk Mendeteksi Kualitas Telur Ayam

Telur ayam tidak hanya mengandung protein namun dilengkapi dengan Omega-3. Omega-3 inilah yang membuat telur akan kaya manfaat dan tidak hanya protein yang didapat. Telur biasa dan telur omega tidak bisa dibedakan secara kasat mata atau berdasarkan penglihatan manusia saja. Memecahkan telur dan melihat embrio pada telur tersebut merupakan alternatif untuk mengetahui telur tersebut merupakan telur biasa atau telur omega. Kesulitan terjadi jika jumlah telur tersebut puluhan hingga ratusan. Masalah tersebut akan diselesaikan dengan metode klasifikasi menggunakan algoritma K-Nearest Neighbor (KNN). K-Nearest Neighbor dapat mengatasi masalah dari K-Means , Otsu , Region Props dan Labelling yaitu, kurang akuratnya hasil atau nilai yang diperoleh dan juga merupakan salah satu metode klasifikasi yang mudah dan efektif. Penelitian ini menggunakan olah citra dan menambahkan metode K-Nearest Neighbor guna mencari klasifikasi data uji, membedakan telur ayam beromega dengan telur ayam biasa dengan analisa tekstur menggunakan statistik orde pertama yaitu mean, standart deviation, skewness, dan kurtosis. Dan untuk uji akurasi menggunakan confusion matrix. Citra telur yang didapat akan dianalisa dengan statistik orde pertama terlebih dahulu. Hasil penelitian menunjukkan bahwa metode yang dipilih berhasil digunakan, dengan hasil K tertinggi yaitu K=7 dan akurasinya 86%. Kata kunci — Telur Ayam, K-Nearest Neighbor, Statistik Orde Pertama Chicken eggs do not only contain protein but are equipped with Omega-3. Omega-3 is what makes eggs will be rich in benefits and not only the protein obtained. Ordinary eggs and omega eggs cannot be distinguished by naked eye or by human vision alone. Breaking the egg and seeing the embryo on the egg is an alternative to knowing that the egg is an ordinary egg or an omega egg. Difficulties occur if the number of eggs is tens to hundreds. The problem will be solved by the classification method using the K-Nearest Neighbor (KNN) algorithm. K-Nearest Neighbor can overcome problems from K-Means, Otsu, Props Region and Labeling, namely, the lack of accurate results or values obtained and also one of the easy and effective classification methods. This study uses image processing and adds the K-Nearest Neighbor method to search for classification of test data, differentiating between Omega 3 chicken eggs and ordinary chicken eggs with texture analysis using first-order statistics, namely the mean, standard deviation, skewness, and kurtosis. And to test the accuracy using confusion matrix. The image of the eggs obtained will be analyzed with first-order statistics first. The results showed that the method chosen was successfully used, with the highest K result, namely K = 7 and its accuracy was 86%. Keywords — Chicken Eggs, K-Nearest Neighbor, First Order Statistics
Investigating the limitations of video stream scheduling in the Internet

Acknowledgments I would like to thank my supervisors Carsten Griwodz and Pål Halvorsen, for their guidance , valuable feedback and helpfull attitude. I would also like to thank the guys at the lab, for giving me a great and motivating work environment. Special thanks to Chris Carlmar for help with setting up the test network. I would also like to thank my family, girlfriend and friends, for their moral support along the way.
Inside the mind of an IT champion

Abstract IT champions have considerable influence on the adoption of a new technology in organisations. A champion engages in a new technology and uses influential tactics to sell and implement it in the organisation. While the importance of IT champions is well documented, very little is understood about how a champion makes sense of a new technology and what sources of beliefs influence the technology commitment of an IT champion. Using an organisation vision lens at the individual level of analysis, this study investigates individuals who championed the adoption of mobile business intelligence (m-BI) in their organisations. Our qualitative analysis suggests that the m- BI organising vision influences IT champions through their m-BI perception and organisational needs perception as they engage in the process of identifying and making sense of the potential of m-BI. Furthermore, an IT champion actively participates and contributes in the organising vision discourse.
THE EFFECT OF COMMUNICATION STYLE ON TASK PERFORMANCE AND MENTAL WORKLOAD USING WEARABLE COMPUTERS

DEDICATION I would like to dedicate this thesis to my parents for instilling in me the conviction that there can be both a right way and wrong way to do things. 3 ACKNOWLEDGEMENTS I would like to thank my chairman, Dr. Woodrow Barfield for the opportunity to work in this field. He has been the most benevolent, generous, and most importantly, accessible professor with whom I have ever had the good fortune of working. While I relied heavily on his professional expertise, I learned the most from casual afternoon conversations, during which I was allowed an occasional peek into his tremendous vision and creativity. These simple and elegant exchanges were seminal to the orchestration of my own thoughts on not only the topics of this thesis but many other ideas and concepts. I would also like to say a special thank you to the other members of my committee, Nussbaum for their guidance through the course of this thesis as well as my studies at Virginia Tech. Finally, I would like to thank the many graduate students within and without the Abstract This thesis measured the mental workload associated with operating a voice activated software application run on wearable computer under five different communication styles (buttons, command line, icon buttons, icon text menus, and text menus). The goal of this thesis was to determine which communication style would be best allow wearable computer users to simultaneously perform other non-computer tasks. Thirty subjects were randomly assigned to using one of five software versions (n = 6), each of which utilized a unique communication style. The mental workload associated with operating each version was assessed by monitoring the performance of secondary tasks. Secondary tasks consisted of completing a block assembly, digit subtraction, and walking along a marked pathway. Each secondary task was performed twice by itself and once while operating one of the software versions, creating a total of nine trials per subject. Block assembly task performance measures included average assembly time, percentage correct blocks, and percentage correct blocks attempted. Digit subtraction measures included percentage of correct digits. And path walking measures included average walking speed. Subjective estimates of mental workload were also collected for those trials in which subjects operated the wearable computer and performed physical tasks using the NASA Task Load Index (TLX). Finally, usability information was collected for each software version via a questionnaire form. Each of the five …
Confidence-aware motion prediction for real-time collision avoidance1

One of the most difficult challenges in robot motion planning is to account for the behavior of other moving agents, such as humans. Commonly, practitioners employ predictive models to reason about where other agents are going to move. Though there has been much recent work in building predictive models, no model is ever perfect: an agent can always move unexpectedly, in a way that is not predicted or not assigned sufficient probability. In such cases, the robot may plan trajectories that appear safe but, in fact, lead to collision. Rather than trust a model’s predictions blindly, we propose that the robot should use the model’s current predictive accuracy to inform the degree of confidence in its future predictions. This model confidence inference allows us to generate probabilistic motion predictions that exploit modeled structure when the structure successfully explains human motion, and degrade gracefully whenever the human moves unexpectedly. We accomplish this by maintaining a Bayesian belief over a single parameter that governs the variance of our human motion model. We couple this prediction algorithm with a recently proposed robust motion planner and controller to guide the construction of robot trajectories that are, to a good approximation, collision-free with a high, user-specified probability. We provide extensive analysis of the combined approach and its overall safety properties by establishing a connection to reachability analysis, and conclude with a hardware demonstration in which a small quadcopter operates safely in the same space as a human pedestrian.
Turnover Intention of Technology Professionals: A Social Exchange Theory Perspective

ABSTRACT Retaining skilled professionals is a critical concern for organizations because employee turnover can affect the quality of service provided by the organization and create considerable expense. Using a framework of social exchange theory, this study develops a model to investigate the interrelationships between turnover intentions, organizational commitment, and constructs of particular importance to information technology (IT) professionals. Field survey data from a large US federal agency empirically test these associations. The results confirm that IT professionals’ perceptions of their skill obsolescence, work overload, and the fairness of the rewards they receive directly influence their organizational commitment. Furthermore, their organizational commitment, perceived work overload, and fairness of rewards significantly affect turnover intention. Employees’ commitment toward the organization is an essential mediator between the perception that their skills are becoming obsolete and intention to leave the organization. Implications of these results for literature and practice are discussed.
Techniques for determining equality of the maximum nullity and the zero forcing number of a graph

It is known that the zero forcing number of a graph is an upper bound for the maximum nullity of the graph. In this paper, we search for characteristics of a graph that guarantee the maximum nullity of the graph and the zero forcing number of the graph are the same by studying a variety of graph parameters which bound the maximum nullity of a graph below. In particular, we introduce a new graph parameter which acts as a lower bound for the maximum nullity of the graph. As a result, we show that the Aztec Diamond graph's maximum nullity and zero forcing number are the same. Other graph parameters that are considered are a Colin de Verdi\'ere type parameter and the vertex connectivity. We also use matrices, such as a divisor matrix of a graph and an equitable partition of the adjacency matrix of a graph, to establish a lower bound for the nullity of the graph's adjacency matrix.
A New Method for Generating Minimal Test Cases for Regression Testing using Dynamic Approach

Testing the software is very important level in the development of the software life cycle. So to the test software automatically is the best way to test the software because it consume less time where testing software manually is consuming process. To software automatically test, test case creation is the best method. One method to produce test cases is to help of the UML figures. One of the basic regression testing objectives is to test that improved system works specification according, at the same time of duration optimizing various test cases through creating it effective and efficient. This paper shows the black-box method that uses Steiner Tree algorithm to create a minimal test set to check functionality. Test cases are produced from requirement represented applying UML. A terminal set are provide as input to the Dynamic Steiner Tree algorithm with graph G. The modification dnodes are defined as terminal nodes to guarantee presence in the test set. A minimal test cases set is created as an indicator to the efficiency of the change. Initial outcomes present that the technique is applicable for rapid testing to guarantee that basic functionality works properly.
CCF approach for asymptotic option pricing under the CEV diffusion

In the last two decades, the asymptotic expansion approach has become popular in mathematical finance because it enables us to obtain closed-form approximation formulae for many kinds of options within various kinds of financial models, such as local and stochastic volatility models. In this study, we propose an asymptotic expansion formula for the option price in a constant elasticity of variance model using the asymptotic expansion technique and Fourier analysis. This approach enables us to derive the higher order terms using only algebraic computation. Furthermore, this method enables us to derive not only the price of European options but also the price of options with an early exercise feature, such as Bermudan options and American options.
Laser-written polymeric scaffolds for micro-patterned liquid crystal alignment

ABSTRACT We demonstrate that two-photon polymerisation-based direct laser writing (TPP-DLW) is a very convenient technique for the fabrication of tunable liquid crystal (LC) based optical diffractive structures. The underlying TPP-DLW scaffold is composed of out-of-plane oriented polymeric ribbons with surface relief gratings on their side walls that induce parallel alignment of the adjacent LC medium. With the aid of such scaffolds infiltrated with the standard nematic liquid crystalline mixture E7, we prepared optical transmission gratings whose diffraction efficiency can be tuned with electric or magnetic fields. We report on an experimental and theoretical comparison between both types of tuning mechanisms. Graphical Abstract
Electron capture, excitation and ionization in He2+–H and H+–He+ collisions

The wave-packet convergent close-coupling method has been applied to model electron capture, excitation and ionization processes in He2+–H and H+–He+ collisions. These processes constitute all possible reactions taking place in a three-body system of an α particle, a proton and an electron but corresponding to two different initial states. We present systematic and convergent calculations of nl-resolved cross sections required for plasma modeling and diagnostics. The presented results are largest and most accurate to date. The integrated cross sections for electron capture, excitation and ionization show generally good agreement with experimental measurements and previous close-coupling calculations where available, however the calculated cross section for Balmer-α emission in He2+–H collisions significantly disagree with experiment. We also present the fully differential, as well as various doubly and singly differential cross sections for ionization in He2+–H collisions at an incident energy of 200 keV amu−1.
Teaching strategy for introducing beginners to Coherent States

Handling coherent states by undergraduates students may be a hard task, as they have to deal with Glauber's series $e^{-\frac {|\alpha|^2} {2}}\sum\limits_{n=0}^\infty\frac {\alpha^n} {\sqrt{n! }}\phi_n(x)$. We show here that the task can be greatly simplified by introduction of a novel compact formula for Glauber coherent states employed in [ Int. J. Mod. Phys. B {\bf 31} (2017) 175051]. This expression is obtained by solving the basic differential equation associated to coherent states $a|\alpha>=\alpha|\alpha>$.
Large-Scale Spatiotemporal Photonic Reservoir Computer for Image Classification

We propose a scalable photonic architecture for implementation of feedforward and recurrent neural networks to perform the classification of handwritten digits from the MNIST database. Our experiment exploits off-the-shelf optical and electronic components to currently achieve a network size of 16,384 nodes. Both network types are designed within the reservoir computing paradigm with randomly weighted input and hidden layers. Using various feature extraction techniques (e.g., histograms of oriented gradients, zoning, and Gabor filters) and a simple training procedure consisting of linear regression and winner-takes-all decision strategy, we demonstrate numerically and experimentally that a feedforward network allows for classification error rate of $\mathbf {1}{\%}$, which is at the state-of-the-art for experimental implementations and remains competitive with more advanced algorithmic approaches. We also investigate recurrent networks in numerical simulations by explicitly activating the temporal dynamics, and predict a performance improvement over the feedforward configuration.
Assessing innovation: Dynamics of high-rise development in an Israeli city

Urban models serve as laboratories, providing researchers with the opportunity to assess the impact of a wide range of social and economic processes on the development of a built environment. Novelty and unexpected changes play an essential role in this development, but these are difficult to formalize and imitate. Typically, urban models simulate innovation by introducing stochastic fluctuations of the pre-established development rules. This research offers a methodology for assessing innovation in a developing city and examining its impact on urban development. The methodology is implemented in the Israeli city of Netanya, where urban development is analyzed at a resolution of single buildings over a period of three decades. We recognize two types of innovation: spatial innovation, manifested by leapfrogging residential clusters that establish new development areas; and contextual innovation, manifested by residential clusters that include buildings that are substantially higher than their surroundings. We demonstrate the impact of few innovative residential clusters on urban development in the following decades and highlight the diffusion of innovation in the city.
High-performance strain sensor based on a 3D conductive structure for wearable electronics

Strain sensors have been widely used in various applications, such as wearable devices, robot arms, electronic skin, and human–machine interfaces. There is an increasing demand for flexible strain sensors as they have high sensitivity, a wide range, excellent flexibility, and good stability. Herein, we report a flexible strain sensor that utilizes reduced graphene oxide (rGO) decorated with Ag nanoparticles as the sensing material (Ag-rGO). Polydimethylsiloxane is used as the substrate and packing material to form a sandwich structure. The process of fabricating the strain sensor is simple and of low cost. The strain sensor exhibits high performance due to the unique 3D conductive structure. Ag nanoparticles function as connection points and adhere to the surface of graphene, which considerably improves the electrical conductivity of rGO, yielding a sensor with high sensitivity. The Ag-rGO strain sensor has a gauge factor (GF) up to 429 with a strain range of 200%; the minimal limit strain of the sensor is 0.2%, where the GF achieves 7.23. Additionally, we employed the strain sensor for applications involving the monitoring of human motions, such as facial expression recognition, vocal vibration tests, motion recognition, pulse, and breath monitoring. The strain sensor proposed in this study exhibits high sensitivity, wide range, and ultrashort response and recovery time. Moreover the sensor can be used to monitor human motions in real time. Therefore, the strain sensor possesses significant potential for use in wearable devices for healthcare, motion monitoring, speech recognition, and human interaction.
Quantifying the AGN-driven outflows in ULIRGs (QUADROS) IV: HST/STIS spectroscopy of the sub-kpc warm outflow in F14394+5332

Considerable uncertainties remain about the nature of warm, AGN-driven outflows and their impact on the evolution of galaxies. This is because the outflows are often unresolved in ground-based observations. As part of a project to study the AGN outflows in some of the most rapidly evolving galaxies in the local Universe, here we present Hubble Space Telescope (HST)/Space Telescope Imaging Spectrograph (STIS) observations of F14394+5332E that resolve the sub-kpc warm outflow for the first time in an ultra-luminous infrared galaxy. The observations reveal a compact, high-ionization outflow region (rmax ∼ 0.9 kpc) set in a more extensive (rmax ∼ 1.4 kpc) halo that is kinematically quiescent and has a lower ionization state. A large line width (600 < FWHM < 1500 km s−1) is measured throughout the outflow region, and the outflowing gas shows a steep velocity gradient with radius, with the magnitude of the blueshifted velocities increasing from ∼500 to 1800 km s−1 from the inner to the outer part of the outflow. We interpret the observations in terms of the local acceleration, and hydrodynamic destruction, of dense clouds as they are swept up in a hot, low-density wind driven by the AGN. We discuss the implications for measuring the mass outflow rates and kinetic powers for the AGN-driven outflows in such objects.
Highly Sensitive Refractive Index Sensor Based on an In-Fiber Droplet-Shape Air-Cavity

We present a highly sensitive refractive index (RI) sensor based on an ultra-compact in-fiber Mach–Zehnder interferometer (MZI) with a droplet-shape air-cavity. The droplet-shape air-cavity is fabricated by fusion splicing and single-side fused tapering. The fabricated MZI is with a very short interference arm of <inline-formula> <tex-math notation="LaTeX">$\sim 330\mu \text{m}$ </tex-math></inline-formula> and the taper waist diameter of <inline-formula> <tex-math notation="LaTeX">$\sim 40\mu \text{m}$ </tex-math></inline-formula>. The interference happens between the silica-cladding mode and the air-core mode. The trough wavelength of the interference with specific order in the transmission spectrum shifts with the change of surrounding refractive index. The experimental results show that this sensor offers high sensitivity (up to 2803.6 nm/RIU at RI∼1.42) for RI measurement in the range of 1.3164–1.4270, which match well with the theoretical analysis. High RI sensitivity, high-spatial resolution, temperature insensitivity, easy fabrication, and low-cost make it a competitive RI sensor in the biochemical and physical sensing fields.
Implementing Network Common Data Form (netCDF) for the 3DWF Model

Abstract : Until recently, the Three-dimensional Wind Field (3DWF) model developed by the US Army Research Laboratory (ARL) had produced geographic coordinated wind data results only in standard Grib format on the Windows platform and was not easily read by other platforms. Having recognized this limitation, recent efforts have been made to improve the model, including the capability to format the results data file in network Common Data Form (netCDF) so that the distributed results can be easily accessible to other machines and operating platforms. This improvement will also allow for easier creation of code and sharing of scientific data. This technical report describes processes, software, and written programs needed in order to produce the 3DWF models results in netCDF data format. In addition, data extraction from netCDF-formatted Weather Research and Forecasting (WRF) model results necessary for the 3DWF models wind initialization will also be documented.
Divergence-Based Risk Measures: A Discussion on Sensitivities and Extensions

This paper introduces a new family of the convex divergence-based risk measure by specifying (h,ϕ)-divergence, corresponding with the dual representation. First, the sensitivity characteristics of the modified divergence risk measure with respect to profit and loss (P&L) and the reference probability in the penalty term are discussed, in view of the certainty equivalent and robust statistics. Secondly, a similar sensitivity property of (h,ϕ)-divergence risk measure with respect to P&L is shown, and boundedness by the analytic risk measure is proved. Numerical studies designed for Rényi- and Tsallis-divergence risk measure are provided. This new family integrates a wide spectrum of divergence risk measures and relates to divergence preferences.