Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Decomposition-Based Algorithms by Estimation of Distribution for Constrained Optimal Software Product Selection

This paper integrates an estimation of distribution (EoD)-based update operator into decomposition-based multiobjective evolutionary algorithms for binary optimization. The probabilistic model in the update operator is a probability vector, which is adaptively learned from historical information of each subproblem. We show that this update operator can significantly enhance decomposition-based algorithms on a number of benchmark problems. Moreover, we apply the enhanced algorithms to the constrained optimal software product selection (OSPS) problem in the field of search-based software engineering. For this real-world problem, we give its formal definition and then develop a new repair operator based on satisfiability solvers. It is demonstrated by the experimental results that the algorithms equipped with the EoD operator are effective in dealing with this practical problem, particularly for large-scale instances. The interdisciplinary studies in this paper provide a new real-world application scenario for constrained multiobjective binary optimizers and also offer valuable techniques for software engineers in handling the OSPS problem.
Two Correlated Interferometric Optical Fiber Systems Applied to the Mining Activity Recordings

In order to meet new directions of research in the field of rotational seismology, a registration of rotation phenomena from mining seismic shocks was carried out by two interferometric optical systems. Applied instruments are based on the Sagnac effect and measure the rotation rate in a direct way. Systems have a wide measuring range both in the case of signal amplitude as well as frequency range. Allan variance analysis has indicated that they have proper parameters for the rotational seismology investigation (angle random walk equals 3.20 · 10−8 rad/√s, as well as bias instability equals 2.41 · 10−8 rad/s). Moreover, regarding applied special electronic processing, they provide rotation rate as an output instead of a change of an angle like in commercially available fiber optic gyroscopes. The optical head of the constructed systems uses depolarized light in order to avoid polarization effect. Systems are remotely controlled by the Internet and adapted to an independent power supply which makes them fully autonomous. Correlation between data gathered by two systems applied in a geophysical observatory in Książ, Poland, have been calculated according to the person's correlation coefficient.
Energy maximising robust control for spectral and pseudospectral methods with application to wave energy systems

Spectral and Pseudospectral methods have been widely considered in diverse optimal control applications, usually where energy optimisation is required. Although such methods are a good way to ensure a good balance between performance and computational effort, in most of the literature, nominal mathematical models are considered without taking into account possible dynamic deviations from the nominal case. The main aim of this study is to propose a novel framework where spectral and pseudospectral problems include some structured uncertainty, achieving robust optimal control designs guaranteeing the ‘best worst-case performance’. In this paper, the objective function used for optimisation is inspired by wave energy converters. Two solution methodologies are developed. Firstly, an analytical solution, for circular and convex polytopic uncertainty boundaries, is proposed. Then, a numerical formulation is introduced to consider uncertainty sets of arbitrary shape, adding the ability to consider physical system constraints. Finally, an application example shows the benefit of this new control formulation.
Ethics Discovering Right And Wrong

Thank you for reading ethics discovering right and wrong. Maybe you have knowledge that, people have search numerous times for their favorite books like this ethics discovering right and wrong, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some harmful bugs inside their desktop computer. ethics discovering right and wrong is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the ethics discovering right and wrong is universally compatible with any devices to read.
Enhancing Security in user authentication through honeyword

Honeyword system used to detect password file disclosure. For each user set of honeyword is generated. When adversary have a password file, then it get confused which one is real password in honeyword set. Adversary enters all honeywords in the set. When honeywords are entered notification will be send to the admin. Author gives hybrid method for generation of honeyword. Hybrid method provides strong DoS resistance and flatness.
Improving student model for individualized learning

Computer-based educational environments, like Intelligent Tutoring Systems (ITSs), have been used to enhance human learning. These environments aim at increasing student achievement by providing individualized instructions. It has been recognized that individualized learning is more effective than the conventional learning. Student models which are used to capture student knowledge underlie the individualized learning. In recent decades, various competing student models have been proposed. However, some diagnostic information in student behaviors is usually ignored by these models. Furthermore, to individualize learning paths, student models should capture prerequisite structures of fine-grained skills. However, acquiring skill structures requires much knowledge engineering effort. We improve student models for individualized learning with respect to the two aspects. On one hand, in order to improve the diagnostic ability of a student model, we introduce the diagnostic feature—student error patterns. To deal with the noise in student performance data, we extend a sound probabilistic model to incorporate erroneous responses. The results show that the diagnostic feature improves the prediction accuracy of student models. On the other hand, we target on discovering prerequisite structures of skills from student performance data. It is a challenging task, since student knowledge of a skill is a latent variable. We propose a two-phase method to discover skill structure from noisy observations. Our method is validated on simulated data and real data. In addition, we verify that prerequisite structures of skills can improve the accuracy of a student model.
Interacting with Design Alternatives

Many designers tend to work with multiple, simultaneously-available design alternatives. The evidence for this fact can be found in many different domains of design, such as architectural, product, industrial and mechanical design, but also in drawing, painting, sculpting, and fine arts in general. Numerous empirical studies, likewise, report instances of designers developing, and then simultaneously working with multiple design solutions in parallel. The need for alternatives-enabled work can be further confirmed by instances of expert opinion, as well as explained by theoretical accounts based on first perceptual principles, such as those governing the human visual system, or human perception in general. Yet, despite the available evidence confirming the importance of multi-state work, most contemporary computational tools, including computer-aided design (CAD) tools, their important subvariant (parametric CAD, or pCAD tools), as well as other types of computational tools, are steeply immersed in the single-state paradigm, whereby the user can work with, and modify, just a single computational model at a time. Consequently, since the overwhelming majority of users nowadays still employ single-state tools, they necessarily have to recur to various ad hoc methods when engaging in multi-state work, in order to circumvent the limitations inherent to single-state tools. However, such workaround methods are inefficient, and error-prone. The aim of this work is to address the aforementioned shortcoming of single-state computational tools, while focusing on pCAD tools. The thesis of this work, accordingly, is that “alternativesenabled (i.e. multi-state) pCAD tools, designed in concordance with the interaction design guidelines and principles presented in this dissertation, support designing”. I break down my thesis statement further into the following three main research questions, each addressing one separate aspect of the statement: (1) What are the essential system features of novel multi-state pCAD tools that can support design work? (2) What are the design guidelines and principles for building and evaluating multi-state pCAD tools? and (3) What methodological approach can be followed that can help system designers build multi-state pCAD tools? In order to support my thesis, I first conceptualize (or design, develop) the class of multi-state pCAD tools, by first employing both (1) various pre-design methods (such as literature review, and probing of the “design space” of feasible interaction designs), as well as (2) by employing various
City recommender system based on a latent topic model

Travel recommendation is a very challenging for the application of recommender systems. A travel recommender system helps travelers in decision making processes when they are planning a trip, including the choice of destinations. In this research, our goal is to build algorithm for recommending venues for a user when he visits a city. One of the challenges is the recommendation of venues in a city that the user has never been (no history activities in that city the cold-start problem). We cope with this challenge by exploiting the location information of users ’history activities as well as users ’ behavior when they visit a city. Our experiment is conducted on the dataset of FourSquare, a location-based social network.
Methodology and Applications of Visual Stance Analysis : An Interactive Demo

Analysis of stance in textual data can reveal the attitudes of speakers, ranging from general agreement/disagreement with other speakers to fine-grained indications of wishes and emotions. The implementation of an automatic stance classifier and corresponding visualization techniques facilitates the analysis of human communication and social media texts. Furthermore, scholars in Digital Humanities could also benefit from such an approach by applying it for literature studies. For example, a researcher could explore the usage of such stance categories as certainty or prediction in a novel. Analysis of such abstract categories in longer texts would be complicated or even impossible with simpler tools such as regular expression search.Our research on automatic and visual stance analysis is concerned with multiple theoretical and practical challenges in linguistics, computational linguistics, and information visualization. In this interactive demo, we demonstrate our web-based visual analytics system called ALVA, which is designed to support the text data annotation and stance classifier training stages. (Less)
Effect of SW defect on structural and transport properties of silicene nanoribbons

Using density functional theory and nonequilibrium Green's function technique, we performed theoretical investigations on the structural and transport properties of zigzag silicene nanoribbons (SiNRs) with Stone–Wales (SW) defect. The calculated formation energy is significantly lower than that of graphene and silicene, which implies the high stability of such defect in SiNRs. Negative differential resistance (NDR) can be observed within certain bias voltage range in both perfect and SW-defected SiNRs. In order to elucidate the mechanism, the NDR behavior, the transmission spectra and molecular projected self-consistent Hamiltonian (MPSH) states are discussed in details.
Noncommutative Field Theories and (Super)String Field Theories

In this lecture notes we explain and discuss some ideas concerning noncommutative geometry in general, as well as noncommutative field theories and string field theories. We consider noncommutative quantum field theories emphasizing an issue of their renormalizability and the UV/IR mixing. Sen's conjectures on open string tachyon condensation and their application to the D-brane physics have led to wide investigations of the covariant string field theory proposed by Witten about 15 years ago. We review main ingredients of cubic (super)string field theories using various formulations: functional, operator, conformal and the half string formalisms. The main technical tools that are used to study conjectured D-brane decay into closed string vacuum through the tachyon condensation are presented. We describe also methods which are used to study the cubic open string field theory around the tachyon vacuum: construction of the sliver state, ``comma'' and matrix representations of vertices.
$H^{\infty}$ interpolation and embedding theorems for rational functions

We consider a Nevanlinna-Pick interpolation problem on finite sequences of the unit disc D constrained by Hardy and radial-weighted Bergman norms. We find sharp asymptotics on the corresponding interpolation constants. As another application of our techniques we prove embedding theorems for rational functions. We find that the embedding of H $\infty$ into Hardy or radial-weighted Bergman spaces in D is invertible on the subset of rational functions of a given degree n whose poles are separated from the unit circle and obtain asymptotically sharp estimates of the corresponding embedding constants. Mathematics Subject Classification (2010). Primary 15A60, 32A36, 26A33; Secondary 30D55, 26C15, 41A10.
Study of dynamically reconfigurable algorithm for low-power cache

Pynamically reconfigurable the algorithm used the instruction-cycle to detect phase changes.In each phase,the state machine used the average access time to predict the cache access and obtained optimal configuration.The experimental results show that the algorithm can reduce cache power dissipation by 61%,but increasing CPI by 2% on average compared with four-way conventional cache,and further saves the energy consumption and reduces the performance loss compared to the previous works.
PID CONTROLLER TUNING OF 3-PHASE SEPARATOR IN OIL & GAS INDUSTRY USING BACTERIA FORAGING OPTIMIZATION ALGORITHM

In oil and gas industry, one of the most important stages in processing petroleum is separation. It can be classified by operating configuration such as vertical, horizontal and spherical or by its function which is 2-phase or 3-phase. In this paper, vertical 3-phase separator will be chosen and researched. 3-phase separator is used to separate water, oil and gas. Gas will be at the top, oil will be the middle layer and water will be at the bottom due to gravitational force and the density of the substance. The objective is to tune the PID controller controlling the level of the water in the separator. Outflow rate of the water from the bottom of the separator will be used to control the water level. Currently there are controlling methods namely PI control using trial and error method, PI control using Butterworth filter design method and IMC method. These methods were having quite high % overshoot and long settling time. So, this paper will introduce Bacterial Foraging Optimization Algorithm (BFOA) in optimizing the parameters for PI control. BFOA mimics the behaviour of the bacteria in searching for highest food concentration which then modified to search the best parameters for the PID controller. BFOA will be able to find the best parameters compared with the conventional methods and show better performance than PI control using trial and error method, PI control using Butterworth filter design method or IMC method. BFOA will be studied and other existing conventional methods as well. Simulation will be done based on the mathematical model of the 3-phase separator.
Realistic Rain Model for the Estimation of the Rainfall Rate From Radar Measurements

This contribution describes a realistic rain model that considers a cluster of axis-symmetric but flattened raindrops with a specified shape–size relation, a raindrop size distribution, and a distribution of orientation angle of the symmetry axis. The backscattering cross section and specific attenuation due to the rain medium are determined by the extended boundary condition method. The model is applied to data from two X-band radars to estimate reflectivity and rainfall rate, considering attenuation effects during the two-way propagation between the radar and each sampled rain volume. The results are then compared with corresponding ones from well-established attenuation-correction methods. One of these methods, which relies on unattenuated reflectivities measured by an S-band radar, has been carefully validated by a previous study. The reasonable agreement between the two methods indicates that the proposed model may be a suitable option to estimate reflectivity and rainfall rate fields based on X-band radar data, particularly when no such references are available.
The University of New South Wales Extrasolar Planet Search: methods and first results from a field centred on NGC 6633

We report on the current status of the University of New South Wales Extrasolar Planet Search project, giving details of the methods we use to obtain millimagnitude precision photometry using the 0.5-m Automated Patrol Telescope. We use a novel observing technique to optimally broaden the point spread function and thus largely eliminate photometric noise due to intrapixel sensitivity variations on the CCD. We have observed eight crowded Galactic fields using this technique during 2003 and 2004. Our analysis of the first of these fields (centred on the open cluster NGC 6633) has yielded 49 variable stars and four shallow transit candidates. Follow-up observations of these candidates have identified them as eclipsing binary systems. We use a detailed simulation of our observations to estimate our sensitivity to short-period planets, and to select a new observing strategy to maximize the number of planets detected. Ke yw ords: methods: data analysis ‐ methods: observational ‐ binaries: eclipsing ‐ planetary systems ‐ open clusters and associations: individual: NGC 6633.
A Binary-Tree Hierarchical Multiple-Chip Architecture for Real-Time Large-Scale Learning Processor Systems

A binary-tree hierarchical multiple-chip processor architecture leveraging the K-means clustering algorithm has been developed for real-time learning of large amounts of sample data. To improve the computational speed, an embedded memory configuration has been used to store all sample data on the same chip for massively parallel processing. As a solution to the problem of the maximum sample data size limited by the chip area, a multiple-chip architecture has been developed, in which dedicated processor chips are connected in a binary-tree hierarchical structure. As a result, the system has been made extendible to any larger scale depending on the application needs. Furthermore, the pipeline calculation flow has been introduced to compensate for the interchip data communication delay. A proof-of-concept chip was designed using a 0.18 µm five-metal complementary metal–oxide–semiconductor (CMOS) technology. The chip operation was verified by NanoSim simulation, and pipeline calculation flow was demonstrated by test chip measurement.
A novel millimeter‐wave dual‐band circularly polarized dielectric resonator antenna

In this article, a novel dual‐band circularly polarized (CP) dielectric resonator antenna (DRA) for millimeter‐wave (MMW) band is presented. The rectangular dielectric resonator with layered truncated corners is excited by a microstrip‐coupled cross‐slot. CP radiations in the lower band are realized by utilizing two quasi‐TE111 modes operating at 21.7 GHz and 23.8 GHz, while CP radiations in the upper band are obtained by exciting a quasi‐TE113 mode at 28.2 GHz. The dual‐band DRA is fabricated and measured. Due to the higher order mode, the average gain of the DRA in the upper band is about 3 dB higher than that in the lower band. The measured impedance bandwidths (|S11| < −10 dB) are 17.0% (20.5‐24.3 GHz) and 15.2% (26.1‐30.4 GHz), while the measured axial ratio (AR) bandwidths (AR < 3 dB) are 12.8% (21.2‐24.1 GHz) and 5% (27.4‐28.8 GHz). In addition, the peak gain values are 5 and 8 dBic.
aiSelections: Computational Techniques for Matching Faculty Research Profiles to Library Acquisitions

As institutional libraries of all sizes adopt more focused acquisitions policies, subject librarians and other selectors will benefit from sophisticated computational approaches that help to identify the monographs, serials, and electronic resources that are likely to receive the most use, thereby reducing interlibrary loan requests, special orders, and unused materials. We describe a pilot study in which data-mining software tools and algorithms were used to summarize faculty biographies, publications, and departmental curricula and to match the resulting profiles to potential monograph selections. We evaluate the effectiveness of these tools by examining the circulation records, interlibrary loan requests, and purchasing receipts from the past several years, noting the computational techniques that are most likely to improve selection accuracy. Keywords: selections, computation, matching, data mining, research profiles
EFFICIENT OFFLINE ALGORITHMIC TECHNIQUES FOR SEVERAL PACKET ROUTING PROBLEMS IN DISTRIBUTED SYSTEMS

In this paper we consider several problems concerning packet routing in distributed systems. Each problem is formulated using terms from Graph Theory and for each problem we present efficient, novel,algorithmic techniques for computing optimal solutions. We address topics like: bottleneck paths (trees), optimal paths with non-linear costs, optimal paths with multiple optimization objectives, maintaining aggregate connectivity information under a sequence of network link failures, and several others. The development of distributed systems worldwide follows a steeper and steeper ascending trend, as such systems become natural solutions to many real-life problems. Communication (at a lower level) and collaboration (at a higher level) are two key elements of a distributed system. At the lowest level, packet routing techniques are employed by the intermediate nodes, switches and routers in order to transfer packets from a source to one or several des- tinations. However, we have little control over the packet routing techniques employed by the routers in the Internet and, furthermore, the Internet does not provide any kind of Quality-of-Service (QoS) guarantees. Because of this, devising new communication architectures and novel, efficient packet routing algorithms and techniques is an important step towards obtaining increased communication performance and QoS guarantees.
Data Mining Techniques for Medical Data Classification

Data mining is information extraction from database. In this paper, we use data mining techniques to get correct medical diagnosis. In this study different techniques presented to get better accuracy using data mining tools such as Bayesian Network, Multilayer Perceptron, Decision Trees and Support Vector Machines (SVM). By using SVM, we achieved 97.72 % accuracy with WDBC dataset.