Miracle-dz/UniFAR-FTUs huggingface dataset:
Faster fully polynomial approximation schemes for Knapsack problems

A fully polynomial time approximation scheme (FPTAS) is an algorithm that returns (1− )-optimal solution to a maximization problem of size n, which runs in polynomial time in both n and 1/ . We develop faster FPTASs for several classes of knapsack problems. In this thesis, we will first survey the relevant literature in FPTASs for knapsack problems. We propose the use of floating point arithmetic rather than the use of geometric rounding in order to simplify analysis. Given a knapsack problem that yield an (1− )optimal solution for disjoint subsets S and T of decision variables, we show how to attain (1 − 1.5 )-optimal solution for the knapsack problem for the set S ∪ T in O( −2). We use this procedure to speed up the run-time of FPTASs for 1. The Integer Knapsack Problem 2. The Unbounded Integer Knapsack Problem 3. The Multiple-Choice Knapsack Problem, and 4. The Nonlinear Integer Knapsack Problem Using addition ideas, we develop a fast fully polynomial time randomized approximation scheme (FPRAS) for the 0-1 Knapsack Problem, which has the run-time of O ( nmin(log n, log 1 ) + −2.5 log 1 ) . Thesis Supervisor: James B. Orlin Title: E. Pennell Brooks (1917) Professor in Management
MSc THESIS On-line Testing of Routers in Networks-on-Chips

Faculty of Electrical Engineering, Mathematics and Computer Science CE-MS-2009-18 Due to recent progress in semiconductor technology, communication is becoming the major source of expense in today’s chip design. Network-on-Chip (NoC) is a new paradigm for solving the problem of complex communication on the chips. However, in order to NoC to be efficient in providing complex on-chip communication, the designers should be assured about its correct functionality. For this reason, an efficient test method should be developed for testing the NoC. In order to test the NoC in an efficient way, the test method should be in a way that does not degrade the performance of the NoC (with online test). Moreover it should test the NoC with a high fault coverage (structural test). In this thesis, a new platform is proposed to do online-structural test on NoC. Although NoC’s elements has been tested after manufacturing and before being used in a SoC, but when NoC is being used, after some time there is possibility that some errors occur in the elements (e.g. router) of NoC and ruin their functionality. The proposed platform, helps the system developers with online detection and localization of the errors that may occur in routers. Therefore, it increases NoC’s reliability. In this platform, the idle routers of NoC are being tested while the rest of the routers are providing on-chip communication. For testing the router the standard test architecture for testing embedded cores (Test-Access-Mechanism (TAM), wrapper, source and sink)is used. In this architecture it is assumed that source and sink are given. The main contribution of this project is to design the wrapper element in such a way that it can be used in the proposed onlinestructural test platform and reuse NoC as TAM. The limitation of the proposed method is that the number of routers that can be tested at the same time without interrupting NoC’s normal functionality depends on topology and size of the NoC. The major cost of this test methods, is related to the wrapper architecture. On-line Testing of Routers in Networks-on-Chips
Interaction and visual performance in stereoscopic displays: A review

In this paper, authors systematically selected and reviewed articles related to stereoscopic displays and their advances, with a special focus on perception, interaction, and corresponding challenges. The aim was to understand interaction‐related problems, provide possible explanations, and identify factors that limit their applications. Despite promising advancements, there are still issues that researchers in the field fail to explain precisely. The two major problems in stereoscopic viewing are, compared with the real world, objects are perceived to be smaller than they actually are and there are discomfort and visual syndromes. Furthermore, there is general agreement that humans underestimate their egocentric distance in a virtual environment (VE). Our analysis revealed that in the real world, distance estimation is about 94% accurate, but in VE, it is only about 80% accurate. This problem could reduce the efficacy of different sensory motor‐based applications where interaction is important. Experts from human factors, computing, psychology, and others have studied contributing factors such as types of perception/response method, quality of graphics, associated stereoscopic conditions, experience in virtual reality (VR), and distance signals. This paper discusses the factors requiring further investigation if the VR interaction is to be seamlessly realized. In addition, engineering research directions aiming at improving current interaction performances are recommended.
Performance Analysis of SRF-PLL and DDSRF-PLL Methods for Grid Interactive Inverters

In grid interactive power converter applications, phase locked loop (PLL) methods are very important to realize grid synchronization. The performance of PLL which is used should not be affected by adverse conditions such as voltage unbalance, harmonics, frequency and phase changes. Otherwise, synchronization errors occur between the grid interactive inverter and the grid. In this paper, two different PLL methods are simulated by modeling under MATLAB/Simulink. The performances of the PLL methods are comparatively presented under four different conditions such as balanced grid condition, unbalanced grid condition, in grid voltage with harmonic component condition harmonics and variable frequency grid condition. In this study, SRF-PLL and DDSRF-PLL which are mostly used, state-of-arts and effective PLL methods are analyzed by modeling in grid synchronization applications. It has also put forward the positive and negative aspects of the PLL methods based on the obtained results.
Research on Electronic Government oriented geographic information service system

We study the construction of an Electronic Government oriented geographic information service system. Firstly we describe the objective to construct the system that is to improve the efficiency, benefit and scientificity of government management and decision-making. Secondly we state the system architecture in detail, which is mainly comprised of three layers, i.e. the SDI (Spatial Data Infrastructure) layer, the geographic information share layer, and the application layer. Each layer can be divided into two modules, i.e. "hardware module" and "software module". The "hardware module" includes geographic data, software system, network infrastructure, etc. The "software module" includes policies, laws, data standards, technology specifications about the service content, etc. Thirdly we discuss the system realization and application in "Digital Fujian" of China. As a practical application example in third layer, we take the "Forest Resource Management System" to illustrate the application of geographic information service system for the Electronic Government in Fujian province of China. The system includes three levels, i.e. the county level, the regional level and the province level, which are connected by network. The three levels implement different government actions with the support of geographic information services.
Verification and Validation of D2FD Method

—D2FD (Data to Fuzzy-DEVS) method provides a solution for the problem of system inference. This method is well designed and implemented as an available and dedicated plug-in within the process mining framework (ProM). This plug-in is also integrated with the simulation tool SimStudio. However, the last step of the process of inferring models and simulations, which is verification and validation, is missing. This paper proposes a new paradigm of verification and validation in system inference. The case study uses the method of comparing with other models as the main validation technique. Based on the same data source from the Dutch Employee Insurance Agency, it attempts to compare the previous results with other studies.
Application of Stochastic Differential System in Chemical Reactions via Simulation

Stochastic process models play a prominent role in a range of application areas, including biology, chemistry, epidemiology, mechanics, microelectronics, economics, and finance. In mathematical modeling, if we use stochastic systems then we will assume that the system follows a probabilistic rule and the future behavior of the system will not be known for sure. Idea of modeling chemical reactions in terms of ordinary and stochastic differential equations can be exposed to a range of modern ideas in applied and computational mathematics. In this paper, we will introduce some fundamental concepts of stochastic processes and simulate them with R saftware. Also, we peresent a numerical solution of chemical Langevin equation as a stochastic differential system with applications in Chemistry and Physics.
Creating a Structured AOP Knowledgebase via Ontology-Based Annotations.

Introduction The Adverse Outcome Pathway framework is increasingly used to integrate data generated based on traditional and emerging toxicity testing paradigms. As the number of AOP descriptions has increased, so has the need to define the AOP in computable terms. Materials and Methods Herein, we present a comprehensive annotation of 172 AOPs housed in the AOP-Wiki as of December 4, 2016 using terms from existing biological ontologies. Results AOP Key Events (KEs) were assigned ontology terms using a concept called the Event Component, which consists of a Process, an Object, and an Action term, with each term originating from ontologies and other controlled vocabularies. Annotation of KEs with ontology classes from fourteen ontologies and controlled vocabularies resulted in a total of 685 KEs being annotated with a total of 809 Event Components. A set of seven conventions resulted, defining the annotation of KEs via Event Components. Discussion This expanded annotation of AOPs allows computational reasoners to aid in both AOP development and applications. In addition, the incorporation of explicit biological objects will reduce the time required for converting a qualitative AOP description into a conceptual model that can support computational modeling. As high throughput genomics becomes a more important part of the high throughput toxicity testing landscape, the new approaches described here for annotating key events will also promote the visualization and analysis of genomics data in an AOP context.
Dividing the grids of compressed sensing for channel estimation and investigating Markov codes

: Bajwa et al. proposed a channel estimation method based on compressed sensing. This method is markedly superior to the conventional methods. However, there is a problem in the method that multi-path delays may not be resolved if they span between the grids. We study to overcome the drawback of the method. Firstly, we investigate upsampled codes so that we could more accurately estimate the channel. Secondly, we investigate Markov codes. It was shown that Spread Spectrum (SS) codes with negative autocorrelation reduces the Multiple Access Interference (MAI) as well as Bit Error Rate (BER) in chip-asynchronous Spread Spectrum Multiple Access (SSMA) systems. Such SS codes are generated from a Markov chain whose transition probability matrix has a negative eigenvalue λ = − 2 + √ 3. The mean square error (MSE) of channel estimation using upsampled Markov code is shown to be better than the MSE of channel estimation using upsampled independent and identically distributed code at certain conditions.
ADHOC: a Tool for Performi

This paper introduces ADHOC, a tool that integrates statistical methods and machine learning techniques to perform effective feature selection. Feature selection plays a central role in the data analysis process since redundant and irrelevant features often degrade the perjormance of induction algorithms, both in speed and predictive accuracy. ADHOC combines the advantages of both Jilter and feedback approaches to feature selection to enhance the understanding of the given data and increase the efficiency of the feature selection process. We report results of extensive experiments on realworld data which demonstrate the effectiveness of ADHOC as data reduction technique as well as feature selection method. ADHOC has been employed in the analysis of several corporate databases. In particular, it is currently used to support the diffcult task of early estimating the cost of software projects.
Reliability Prediction of High Complexity Synthesis Systems

An utility and high efficient ITEM ToolKit reliability flat roof is used to predict the reliability for a complicated instrument, which aims at the focus problem of reliability work in equipment in China and according as the stress analytical method of GJB/Z 299C Electron Equipment Reliability Prediction Handbook and MIL-HDBK-217Electron Equipment Reliability Prediction Handbook. The reliability request of each model task collectivity is the frame in work. At the same time, an utility and high efficient reliability prediction method is explored in working.
Dynamic Analysis for Security Testing of WEB Based Applications Using Agent Technology

—This is the second part of two papers covering the topic of security testing of WEB based applications using agent technology that covers dynamic analysis. This research covers an integrated dynamic analysis technique and tool for detecting and preventing such security vulnerabilities in web applications. It is based on agent technology and written in Java. The dynamic analysis starts for tracking the propagation of user input in the program which helps to detect the vulnerabilities in the source code. This technique is extendable to the vulnerabilities in the similar class and source codes written in other object oriented languages. At the end, this paper presents a Java Web Application Security Tester (JWAST) which is an implementation of the proposed technique. Also a comparison of JWAST with other tools is presented.
Developing a problem-based learning simulation: An economics unit on trade

This article argues that the merger of simulations and problem-based learning (PBL) can enhance both active-learning strategies. Simulations benefit by using a PBL framework to promote student-directed learning and problem-solving skills to explain a simulated dilemma with multiple solutions. PBL benefits because simulations structure the information students receive to focus learning on the intended curriculum and increase the strategy’s effectiveness in a wider variety of venues. A combined strategy—a PBL simulation— places its simulation at the forefront of learning and helps students and teachers sift through the overwhelming complexity that can arise in a more pure PBL. The authors illustrate the strength of a PBL simulation with an economics unit on trade, titled THE GREAT AWAKENING.
Stress Magnitudes From Logs: Effects of Tectonic Strains and Temperature

An improved method of calibrating in-situ stress logs has been validated with data from two wells. Horizontal stress profiles are useful for hydraulic fracture design, wellbore stability analysis, and sand production prediction. The industry-standard method of estimating stresses from logs is based on overburden Poisson's ratio, and pore pressure effects and outputs an estimate of minimum horizontal stress. The model proposed here adds effects of temperature and tectonics and outputs minimum and maximum horizontal stress magnitudes, which are particularly important to the successful completion of horizontal and deviated wells. This method was validated using data collected from a GRI research well and a Mobil well. Seven microfrac stress tests in GRI's Canyon Gas Sands well of Sutton County, TX, provided a means of comparing the predictive capability of different methods. First, one of the seven stress tests was selected as a calibration standard for the stress log. Then the results obtained from the two calibration methods were compared to stress magnitudes from the other six stress tests. This process was repeated using each of the seven stress tests as a calibration standard and comparing predictions to the other six. In every case, the method incorporating tectonic and temperature effects produced significantly more accurate values. In the Mobil well, pre-frac treatment breakdown tests were used to calibrate a log-derived stress profile. While reservoir pressure dominated stress variations, a significant deviation of the log value from the stress-test value in one layer was corrected when the method with tectonics and temperature was used.
IEEE Transactions on Control Systems Technology publication information

The Control Systems Society is an organization, within the framework of the IEEE, of members with professional interest in automatic control. All members of the IEEE are eligible for membership in the Society and will receive this TRANSACTIONS upon payment of the annual Society membership fee of $25.00, plus an annual subscription fee of $40.00. For further information, write to the IEEE at the address below. Member copies of Transactions/Journals are for personal use only.
Vector Generalized Linear and Additive Models: With an Implementation in R

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully operable, the framework allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole.Since their advent in 1972, GLMs have unified important distributions under a single umbrella with enormous implications. However, GLMs are not flexible enough to cope with the demands of practical data analysis. And data-driven GLMs, in the form of generalized additive models (GAMs), are also largely confined to the exponential family. The methodology here and accompanying software (the extensive VGAM R package) are directed at these limitations and are described comprehensively for the first time in one volume. This book treats distributions and classical models as generalized regression models, and the result is a much broader application base for GLMs and GAMs.The book can be used in senior undergraduate or first-year postgraduate courses on GLMs or categorical data analysis and as a methodology resource for VGAM users. In the second part of the book, the R package VGAM allows readers to grasp immediately applications of the methodology. R code is integrated in the text, and datasets are used throughout. Potential applications include ecology, finance, biostatistics, and social sciences. The methodological contribution of this book stands alone and does not require use of the VGAM package.
Statistical model of W-CDMA signals over realistic multipath channel

This paper proposes a statistical model of the wideband code division multiple access (W-CDMA) signals. The performance of a W-CDMA system is evaluated by calculating the average bit error rate (BER) which is derived from the probability distribution of the W-CDMA receiver output. If a statistical model of the receiver output is available, the performance evaluation becomes much simpler and it enables diverse analyses of the system for channel coding and other purposes. In this paper the statistical model of W-CDMA signals, more specifically those of the receiver outputs, is represented as a Nakagami-m distribution, considering two cases of bandwidths and channel environments. The adequacy of a statistical model is verified by chi-square test of 1% significance level and the comparison of BER obtained from simulation data and the statistical model.
Model-Free Error Detection and Recovery for Robot Learning from Demonstration

Learning from human demonstrations can facilitate automation but is risky because the execution of the learned policy might lead to collisions and other failures. Adding explicit constraints to avoid unsafe states is generally not possible when the state representations are complex. Furthermore, enforcing these constraints during execution of the learned policy can be challenging in environments where dynamics are difficult to model such as push mechanics in grasping. In this paper, we propose a two-phase method for generating robust policies from demonstrations in robotic manipulation tasks. In the first phase, we use support estimation of supervisor demonstrations and treat the support as implicit constraints on states in addition to learning a policy directly from the observed controls. We also propose a time-variant modification to the support estimation problem allowing for accurate estimation on sequential tasks. In the second phase, we use a switching policy to steer the robot from leaving safe regions of the state space during run time using the decision function of the estimated support. The policy switches between the robot's learned policy and a novel recovery policy depending on the distance to the boundary of the support. We present additional conditions, which linearly bound the difference in state at each time step by the magnitude of control, allowing us to prove that the robot will not violate the constraints using the recovery policy. A simulated pushing task suggests that support estimation and recovery control can reduce collisions by 83%. On a physical line tracking task using a da Vinci Surgical Robot, recovery control reduced collisions by 84%.
Target Location Based on Ultrasonic Ranging and Vision Information for Picking Robot

In order to confirm the spatiallocation of the fruit target, a target location method by inosculating ultrasonic ranging and vision was developed for eggplant picking robot. The two dimension image information was achieved by means of color camera. The distance of fruit target was measured by using ultrasonic distance measurer. A three layers BP(Back Propagation) neural network was structured to locate the eggplant. The input variables of the neural network were image center coordinates and distance obtained by ultrasonic distance measurer. The output were space coordinates of picking point. The improved BP algorithm was used to train the parameter of the neural network. The effective parameter was achieved after 100 times of training. Experiments showed that the average rms error of the space coordinates of eggplant was 15.6mm when the measure distance ranges from 150 mm to 600 mm and the average time consumed was 0.27s. The target location method by inosculating ultrasonic ranging and vision information for eggplant picking robot owns good intelligence and wide adaptability and it can meet the demands of the eggplant picking robot.