Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Three Meta-Heuristic Algorithms for Maximizing the Net Present Value of a Resource-Constrained Project Scheduling Problem with Respect to Delay Penalties

The Resource Constrained Project Scheduling Problem (RCPSP) is been studied under different kinds of constraints and limitations. In this paper, we are going to consider the discounted cash flows for project activities, and delay penalties which occur when the project make span exceeds its deadline as the objective function of the RCPSP. To solve the model, we will take advantage of three different meta-heuristic algorithms Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), and Shuffled Frog Leaping Algorithm (SFLA) to achieve the optimal solution of the problem. The evaluation of the algorithms performance reveals that, in comparison with ICA and SFLA, GA performs better, especially in large-scale problems. KEywoRDS Delay Penalty, Net Present Value, Project Scheduling
Ellipsis Resolution in Comparatives and the Right Periphery of DP

Since Bresnan (1973), a consensus has emerged in the generative literature thatthe rules of grammar contributing to comparative formation minimally have toinclude the process of Comparative Deletion (‘CD’). CD is defined as anobligatory operation which removes the gradable property expression fromwithin the comparative clause:(1) Mary knows younger authors than Peter knows -(-
Combination of two programming by demonstration techniques to improve the quality of a grasp

In almost all tasks performed by a human being implicit handling of objects, specifically grasp objects, is paramount in most tasks and is an area of robotic research. This paper presents the combination of two programming by demonstration techniques to improve the quality of grasp objects. The two techniques are the Gaussian mixture model and task parameterized Gaussian mixture model. One simulated experiment allows us to obtain a database of different grasps to subsequently to apply the proposed algorithm. The obtained results by using a grasp quality metric shows an improvement to the ones generated only using the task parameterized Gaussian mixture model.
SILVERRUSH. IV. Ly$\alpha$ Luminosity Functions at $z = 5.7$ and $6.6$ Studied with $\sim$ 2,000 LAEs on the $14-21$ deg$^2$ Sky

We present the Ly$\alpha$ luminosity functions (LFs) at $z=$5.7 and 6.6 derived from a new large sample of 2,354 Ly$\alpha$ emitters (LAEs) identified in total areas of 14 and 21 deg$^2$, respectively, based on the early narrowband data of the Subaru/Hyper Suprime-Cam (HSC) survey. Together with careful Monte-Carlo simulations that account for the incompleteness of the LAE selection and the flux estimate systematics in the narrowband imaging, we have determined the Ly$\alpha$ LFs with the unprecedentedly small statistical and systematic uncertainties in a wide Ly$\alpha$ luminosity range of $10^{42.8-43.8}$ erg s$^{-1}$. We obtain the best-fit Schechter parameters of $L^{*}_{{\rm Lya}}=1.1^{+0.7}_{-0.4}\times10^{43}\ (8.2^{+8.6}_{-3.0}\times10^{42})$ erg s$^{-1}$, $\phi^{*}_{{\rm Lya}}=2.5^{+3.4}_{-1.9}\ (2.8^{+3.6}_{-2.3})\times10^{-4}$ Mpc$^{-3}$, and $\alpha=-2.3^{+0.8}_{-0.4}\ (-1.9^{+0.8}_{-0.6})$ at $z=5.7$ ($6.6$). We confirm that our best-estimate Ly$\alpha$ LFs are consistent with the majority of the previous studies, but find that our Ly$\alpha$ LFs do not agree with the large Ly$\alpha$ LFs recently claimed by Matthee/Santos et al.'s studies that may overcorrect the incompleteness and the flux systematics. Our Ly$\alpha$ LFs at $z=$5.7 and 6.6 show a very steep slope ($\alpha\simeq-2$) or a significant ($\simeq3\sigma$) excess beyond the best-fit Schechter function at the bright end ($\gtrsim10^{43.5}$ erg s$^{-1}$) that is claimed by previous studies. This bright-end LF excess at $z\sim6-7$ may be explained by the contribution from AGNs, blended merging galaxies, and/or large ionized bubbles around bright LAEs. Comparing our Ly$\alpha$ LF measurements with four independent reionization models, we estimate the neutral hydrogen fraction of the IGM to be $x_{\rm HI}=0.3\pm0.2$ at $z=$6.6 that is consistent with the small Thomson scattering optical depth obtained by Planck 2016.
Topic Evolution Based on LDA and Topic Association

Topic evolution will help people to learn information quickly.In this paper,a method was proposed to discover topic's evolution over time by topic detection and relating topics in different time periods.The method applies LDA model on temporal documents to extract topics.The number of topics in different time periods is different.Relating topics in consecutive time periods is based on Jensen-Shannon divergence and features similarity.Experiments show that the method can detect new topics and describe topic's evolution over time effectively.It not only shows that the topics evolve with time,but also that the content of topics change with time.
Design of Review Based Product AspectRanking With Transaction Id Verification

A wide range of consumer reviews are available on different e-commerce websites for different products. Consumer reviews contain a rich set of knowledge which can be used for finding both product aspects or features as well as sentiment of consumer on different aspects of the product. The important features of any product can be extracted from the consumer review by finding the frequent noun terms in the reviews which are commented by large number of consumer, since the aspects are usually noun or noun phrases (Liu, 2009). Then we find the aspect - level sentiment word from the consumer review by using sentiment lexicon which consist a list of sentiment words. Then we develop a review based product aspect ranking framework which will use aspect frequency and the influence of consumer openions given to each and every product aspect for giving ranking to the product. Since we are giving ranking to the product based on the consumer review so we provide a unique transaction-id to every consumer on their successful transaction, Then we use Transaction-Id verification system to verify the customer and to allow them to write review.
Terminal Controller Feedback on an Automated Sequencing and Spacing Tool

As part of the technology transfer of the Terminal Sequencing and Spacing (TSAS) prototype from NASA to the FAA, a demonstration of TSAS was conducted in May of 2015. Arrival traffic to the Phoenix Sky Harbor International Airport (PHX) was simulated and was worked by four terminal controllers who had no prior exposure to the TSAS tools. Participants were asked for their opinions on the usability and impact of the TSAS tool elements. They reported that the tool elements were usable and that they used some elements more than others. Scales measuring trust and workload recorded that participants’ trust in TSAS increased over the course of the demonstration and that their perceived workload remained moderate to low even during disturbance events that had been scripted into scenario runs.
Two Ways for Design of Arbitrary Waveform Signal Generator Based on Matlab/DSP Builder

According to the principles of the traditional arbitrary waveform signal generator and DDS-based arbitrary waveform signal generator,the system modeling and simulation of the two principles were achieved with modeling method of Matlab/DSP Builder in DSP Builder platform.Signalcompiler tool was used to compile the models and to generate VHDL source program which QuartusⅡcan distinguish.It is realized by FPGA chip EP2C8Q208C.The system simulation and hardware testing proved the correctness of the two methods.In comparison with the traditional hardware description language modeling,this method has the advantages of simple design,convenient modification and low cost,does not involve any programming,has not high requirement to hardware theory knowledge,and is easy to realize.
A NOVEL APPROACH OSA DETECTION USING SINGLE-LEAD ECG SCALOGRAM BASED ON DEEP NEURAL NETWORK

Obstructive sleep apnea (OSA) is the most common and severe breathing dysfunction which frequently freezes the breathing for longer than 10[Formula: see text]s while sleeping. Polysomnography (PSG) is the conventional approach concerning the treatment of OSA detection. But, this approach is a costly and cumbersome process. To overcome the above complication, a satisfactory and novel technique for interpretation of sleep apnea using ECG were recording is under development. The methods for OSA analysis based on ECG were analyzed for numerous years. Early work concentrated on extracting features, which depend entirely on the experience of human specialists. A novel approach for the prediction of sleep apnea disorder based on the convolutional neural network (CNN) using a pre-trained (AlexNet) model is analyzed in this study. After filtering per-minute segment of the single-lead ECG recording accompanied by continuous wavelet transform (CWT), the 2D scalogram images are generated. Finally, CNN based on deep learning algorithm is adopted to enhance the classification performance. The efficiency of the proposed model is compared with the previous methods that used the same datasets. Proposed method based on CNN is able to achieve the accuracy of 86.22% with 90% sensitivity in per-minute segment OSA classification. Based on per-recording OSA diagnosis, our works correctly classify all the abnormal apneic recording with 100% accuracy. Our OSA analysis model using time-frequency scalogram generates excellent independent validation performance with different state-of-the-art OSA classification systems. Experimental results proved that the proposed method produces excellent performance outcomes with low cost and less complexity.
Towards DIVE : A platform for automatic visualization and analysis of structured datasets

Our world is filled with data describing complex systems like international trade, personal mobility, particle interactions, and genomes. To make sense of these systems, analysts often use visualization and statistical analysis to identify trends, patterns, or anomalies in their data. However, currently available software tools for visualizing and analyzing data have major limitations because they have prohibitively steep learning curves, often need domain-specific customization, and require users to know a priori what they want to see before they see it. Here, I present a new platform for exploratory data visualization and analysis that automatically presents users with inferred visualizations and analyses. By turning data visualization and analysis into an act of curation and selection, this platform aspires to democratize the techniques needed to understand and communicate data. Conceptually, for any dataset, there are a finite number of combinations of its elements. Therefore there are a finite number of common visualizations or analyses of a dataset. In other words, it should be possible to enumerate the whole space of possible visualizations and analyses for a given dataset. Analysts can then explore this space and select interesting results. To capture this intuition, we developed a conceptual framework inspired by set theory and relational algebra, and drawing upon existing work in visualization and database architecture. With these analytical abstractions, we rigorously characterize datasets, infer data models, enumerate visualizable or analyzable data structures, and score these data structures. We implement this framework in the Data Integration and Visualization Engine (DIVE), a web-based platform for anyone to efficiently analyze or visualize arbitrary structured datasets, and then to export and share their results. DIVE has been under development since March 2014 and will continue being development in order to have a alpha version available by September 2015 and a beta version by the end of 2015. Thesis Supervisor: César A. Hidalgo Title: Associate Professor of Media Arts and Sciences Program in Media Arts and Sciences
Personal Credit Scoring Models on Naive Bayesian Classifier

Personal credit scoring plays an important role in financial and banking industry.This paper investigates the credit scoring accuracy of three naive Bayesian classifier models.They are tested using 10-fold cross validation with two real world data sets,and compared with five neural network models of David West's.Results demonstrate that the naive Bayesian classifiers are competitive with neural network classifiers and predominant in credit scoring domain.
Degree of $L^2$-Alexander torsion for 3-manifolds

For an irreducible orientable compact $3$-manifold $N$ with empty or incompressible toral boundary, the full $L^2$--Alexander torsion $\tau^{(2)}(N,\phi)(t)$ associated to any real first cohomology class $\phi$ of $N$ is represented by a function of a positive real variable $t$. The paper shows that $\tau^{(2)}(N,\phi)$ is continuous, everywhere positive, and asymptotically monomial in both ends. Moreover, the degree of $\tau^{(2)}(N,\phi)$ equals the Thurston norm of $\phi$. The result confirms a conjecture of J.~Dubois, S. ~Friedl, and W. ~L\"uck and addresses a question of W. ~Li and W. ~Zhang. Associated to any admissible homomorphism $\gamma:\pi_1(N)\to G$, the $L^2$--Alexander torsion $\tau^{(2)}(N,\gamma,\phi)$ is shown to be continuous and everywhere positive provided that $G$ is residually finite and $(N,\gamma)$ is weakly acyclic. In this case, a generalized degree can be assigned to $\tau^{(2)}(N,\gamma,\phi)$. Moreover, the generalized degree is bounded by the Thurston norm of $\phi$.
Pengaruh Penggunaan Kompensator Dispersi Fiber Bragg Grating (fbg) Pada Sistem Komunikasi Optik Long Haul

ABSTRAK Dispersi merupakan salah satu faktor yang dapat menurunkan performansi dari komunikasi optik khususnya di komunikasi yang jaraknya jauh (long haul). Hal tersebut mengakibatkan kualitas komunikasi yang buruk dan dapat menyebabkan kegagalan komunikasi. maka dari itu dibutuhkan tindakan untuk mengatasi hal tersebut. Di dalam ilmu teknik telekomunikasi ada beberapa tekhnik kompensasi untuk menaggulangi dispersi, dan salah satu yang bisa digunakan adalah teknik penggunaan kompensator dispersi Fiber Bragg Grating (FBG). Penggunaan metode Fiber Bragg Grating (FBG) pada penelitian ini menjadi salah satu solusi untuk menanggulangi dispersi di komunikasi optik. Penelitian ini dilakukan pada komunikasi long haul optik dengan jarak komunikasi sejauh 100 km – 300 km dengan bit rate 10 Gbps. Hasil yang didapatkan pada penelitian ini adalah perbandingan jaringan sebelum dan sesudah ditambahkan kompensator dispersi Fiber Bragg Grating (FBG). Dengan penelitian pada 3 jarak yang berbeda yaitu 100 km, 200 km, dan 300 km dengan bit rate 10 Gbps. Hasil dari jarak terjauh yaitu 300 km, sebelum nilai performasnsi-nya adalah BER= 0,486, Q-factor= 1,64, dan setelah ditambahkan kompensator dispersi Fiber Bragg Grating (FBG) hasil nilai performansi-nya adalah BER = 1,62x10 -91, dan Q-factor-nya = 20,26. Kata kunci: Komunikasi Longhaul, Dispersi, Kompensator, FBG, BER, Q-factor. ABSTRACT Dispersion is one of the factors that reduce the performance of fiber optic communication, especially in long haul communication which results in poor communication quality and can lead to communication failure. In the field of telecommunications engineering, there are several compensation techniques for preventing dispersion. The use of the Fiber Bragg Grating (FBG) method in this study is one solution to overcome dispersion in optical communication. This research was conducted on optical communication with a distance of 100 km - 300 km with a 10 Gbps bit rate. The Fiber Bragg Grating (FBG) compensator is added. By using 3 different distances, 100 km, 200 km and 300 km with 10 Gbps bit rate. The sample from the farthest distance is 300 km with the results before its performance is BER = 0,486, Q-factor = 1,64, and after adding the compensator of the Fiber Brag Grating dispersion (FBG) the performance result is BER = 1,62x10 -91, and the Q-factor = 20,26. Keywords: Longhaul Optic, Dispersion, FBG, BER, Q-factor
Design and Analysis of Machine Learning Experiments

This chapter contains sections titled: 19.1 Introduction, 19.2 Factors, Response, and Strategy of Experimentation, 19.3 Response Surface Design, 19.4 Randomization, Replication, and Blocking, 19.5 Guidelines for Machine Learning Experiments, 19.6 Cross-Validation and Resampling Methods, 19.7 Measuring Classifier Performance, 19.8 Interval Estimation, 19.9 Hypothesis Testing, 19.10 Assessing a Classification Algorithm's Performance, 19.11 Comparing Two Classification Algorithms, 19.12 Comparing Multiple Algorithms: Analysis of Variance, 19.13 Comparison over Multiple Datasets, 19.14 Notes, 19.15 Exercises, 19.16 References
Optimisation of underground mine decline development system using genetic algorithm

When deposit is composed of few ore bodies it is necessary to interconnect them into one integrated system. Suppose the deposit characteristics indicate that decline development system is preferred one. In such environment we treat development of an underground mine as access infrastructure composed of different decline sections. Access infrastructure designing can be treated as spanning the spatial network which will connect all main terminals (points). In our model we defined spatial network by adequate nonlinear constrained objective function representing the cost of mine development and ore haulage. To find the minimum value of the objective function we use Genetic algorithm.
Probabilistic Parsing as Intersection

We show that a well-known algorithm to compute the intersection of a context-fre language and a regular language can be extended to apply to a probabilistic context-free grammar and a probabilistic finite automaton, provided the two probabilistic models are combined through multiplication. The result is a probabilistic context-free grammar that contains joint information about the original grammar and automaton.
Scene Recognition for Mobile Robot Localization

This paper proposes a scene recognition approach for mobile robot localization. The multi-channel Gabor filters are used to extract the global texture features of the scene images which are associated with the corresponding locations, and then these texture features are fed back to support the vector machine classifier to determine the logical location of the robot. The algorithm has been tested on the autonomous mobile robot CASIA-I designed and developed by us. The experiment results indicate that the algorithm can reach up to a correct localiztion rate of 91.11%, is robust to the various illumination and contrast, and satisfies the real-time localization demand of the mobile robot.
A glass box coupling methodology for rigorously solving strongly coupled phenomena in nuclear reactors

High fidelity computational modelling of coupled multi-physics problems occurring in several scientific fields require solution to large, stiff, nonlinear systems of equations with disparate spatiotemporal variations in physical scales. While there are multiple strategies that can be pursued in resolving the heterogeneous descriptions of physical models, the needs for creating a verified multiphysics solver requires usage of existing monophysics solvers seamlessly together, with minimally intrusive interfaces. Reuse of existing codes also offers the advantage of leveraging validated and computationally efficient solvers on petascale architectures thereby preserving several man-years of previously invested efforts. Traditionally, numerical methodologies to tackle such problems have relied on partitioned or operator-split schemes but iterative splitting with fully implicit treatment of the coupled fields can restore higher order accuracy and consistency in the solution [1]. In this talk, we present the Coupled Physics Environment (CouPE) based on SIGMA [2] components, to generate/import/query/manipulate complex unstructured meshes that are used as a data-backplane to transfer and transform the dependent solutions between physics codes consistently. These components are utilized to solve several nonlinearly coupled steady-state and pseudo-transient problems involving neutron transport, thermal-hydraulics and structural deformation physics, defined on fully heterogeneous single assembly [3] and full reactor core geometries. Due to the variations in the spatial scales and discretizations relevant to each physical model, the need for a global or local conservative transfer from one physics mesh to another becomes imperative. The multiphysics solver utilizes iterative splitting (optionally staggered or simultaneous partitioning using a predictor [4]) with Picard or nonlinear Richardson scheme to converge the nonlinearities in the solution fields. The overall goal of the presented glass-box methodology is to enable researchers to perform fully resolved reactor physics analysis on explicit geometry representations, in order to reduce the overall numerical uncertainty, while utilizing available computational resources efficiently. Verifying the accuracy preservation in this setting is fundamental to effective resolution of the disparate characteristic physical scales, even if the process is nontrivial. Convergence studies are presented for several relevant demonstration problems to illustrate the preservation in accuracy via global and subset based conservative solution transfer mechanisms for different fields of interest. In this context, we also analyze the efficiency gain due to the usage of Aitken and Anderson acceleration schemes to improve the linear convergence rates of fixed-point methods. The presented methodology to integrate the three strongly coupled physics components establishes the flexibility and feasibility of the approach for solving a large array of relevant reactor problems.
Comparison of neural network and neurofuzzy identification of vehicle handling under uncertainties

In this study, two adaptive neural network and neurofuzzy identification models are proposed to identify vehicle handling under uncertainties. These models are used to identify vehicle handling in different road friction coefficients and velocities. These two identification models modify their weights to cope with uncertainties using back propagation of error as a learning algorithm. However, an adaptive model has some limitations to identify real systems. The ability of adaptation is not the same for all identification models; some models are more robust to cope with a specific uncertainty or a wider range of uncertainties. In this study, adaptiveness of two identification models are compared under two different uncertainties. First, a precise model in CARSIM software is simulated and a set of input/output data of vehicle response are collected. Then an initial three-layer neural network is trained in MATLAB software. In addition, a Neurofuzzy model is also trained in ANFIS (adaptive neurofuzzy inference system) toolbox of MATLAB software. Then this trained model is applied to the vehicle in different maneuvers, velocities and road friction coefficients. Results show that proposed neural network identifies the vehicle handling more efficiently than neurofuzzy model in conditions that are away from training condition. However, proposed neurofuzzy model is more precise and accurate than neural network in the training condition.