Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Six Sigma to Enhance the Process Maturity of a Supply Chain

This paper explains the influence of process-oriented methodology on the performance of a supply chain, applies the concept of process maturity to the SCM domain, analyses the rationale of integrating SCM and Six Sigma, and presents a basic Six Sigma-based framework that could effectively enhance the process maturity of a supply chain, further improving the performance of the supply chain. This framework can be applied to the performance improvement either of an entire supply chain process or of any sub-process.
Anchorage System to Prestress FRP Laminates for Flexural Strengthening of Steel-Concrete Composite Girders

Using externally bonded (EB) fiber-reinforced polymer (FRP) laminates for strengthening steel-concrete composite girders has recently received more attention from researchers. By prestressing the EB FRP laminates, the material is used more efficiently because a greater portion of its tensile capacity is employed and it contributes to the load-bearing capacity under both service and ultimate conditions. This is an ideal technique because it combines the advantage of using noncorrosive and lightweight advanced composite materials in the form of bonded FRP laminates with the high efficiency offered by external prestressing. An innovative mechanical anchorage system was developed to prestress the FRP laminates directly by jacking and reacting against the steel girder itself. The efficiency of the system was investigated using two types of FRP laminates for flexural strengthening of large-scale steel-concrete composite girders. The used FRP composite materials included carbon-fiber-reinforced polymer (CFRP) plate and steel-fiber-reinforced polymer (SFRP) sheets. The developed anchorage/prestressing system was easy to use/apply and proved to be a feasible and practical system for prestressing both CFRP plate and SFRP sheet. The prestressing levels in the FRP laminates were sufficiently maintained. The prestressing losses were insignificant.
A Fully Annotated Corpus for Studying the Effect of Cognitive Ageing on Users’ Interactions with Spoken Dialogue Systems

In this paper we present a corpus of interactions of older and younger users with nine different dialogue systems. The corpus has been fully transcribed and annotated with dialogue acts and Information State Update (ISU) representations of dialogue context. Users not only underwent a comprehensive battery of cognitive assessments, but they also rated the usability of each dialogue system on a standardised questionnaire. In this paper, we discuss the corpus collection and outline the semi-automatic methods we used for discourse-level annotations. We expect that the corpus will provide a key resource for modelling older peoples interaction with spoken dialogue systems.
Detection of Microaneurysms in Color Fundus Images

Diabetic Retinopathy is an eye disease which occurs due to damage in the retina as a result of long term illness of diabetic mellitus. Microaneurysm detection is the first step in automated screening of diabetic retinopathy. To perform this detection, several methods have been proposed in the diagnosis of diabetic retinopathy. In this paper, initially the pre-processing techniques like gamma correction, green channel extraction, location based contrast enhancement process such as Adaptive Histogram Equalization (AHE) are applied to improve the visibility of microaneurysms in the color fundus images. Microaneurysms are segmented using fuzzy c-means clustering algorithm on the pre-processed image. Then features are extracted using Gray Level Co- occurrence Matrix (GLCM), wavelet and first order statistics. All these features constitute the feature set which is used in knn classifier to remove spurious candidates. The remaining candidates can be thresholded further for a binary output which gives the final candidates.
Multiple feature hashing for real-time large scale near-duplicate video retrieval

Near-duplicate video retrieval (NDVR) has recently attracted lots of research attention due to the exponential growth of online videos. It helps in many areas, such as copyright protection, video tagging, online video usage monitoring, etc. Most of existing approaches use only a single feature to represent a video for NDVR. However, a single feature is often insufficient to characterize the video content. Besides, while the accuracy is the main concern in previous literatures, the scalability of NDVR algorithms for large scale video datasets has been rarely addressed. In this paper, we present a novel approach - Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR. MFH preserves the local structure information of each individual feature and also globally consider the local structures for all the features to learn a group of hash functions which map the video keyframes into the Hamming space and generate a series of binary codes to represent the video dataset. We evaluate our approach on a public video dataset and a large scale video dataset consisting of 132,647 videos, which was collected from YouTube by ourselves. The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.
Decentralized Controller Design for String of Moving Vehicles Based on Inclusion Principle

A decomposition methodology to design decentralized feedback controllers for a linear system is proposed in this paper. The controller regulates the position and velocity of every vehicle in a densely packed string of high-speed moving vehicles. The linear model of the vehicle-string velocity and distance bias system can be of overlapping interconnected subsystem models constructed by pair-wise vehicles. Centralized controllers can provide better results but are not suitable for the system due to vehicle autonomies. By using the inclusion principle conditions, the overlapping interconnected structure of the system appears as decoupled. The general theoretical formulation and solution of the decentralized LQG control problem is then presented. Simulation studies are also considered for the case of a string of four vehicles based on the decentralized algorithm. The application results of the decentralized controller illustrate the effectiveness of the proposed methodology.
Interfaces, attributes, and use cases: A compass for SDN

The term Software Defined Networking (SDN) is prevalent in today's discussion about future communication networks. As with any new term or paradigm, however, no consistent definition regarding this technology has formed. The fragmented view on SDN results in legacy products being passed off by equipment vendors as SDN, academics mixing up the attributes of SDN with those of network virtualization, and users not fully understanding the benefits. Therefore, establishing SDN as a widely adopted technology beyond laboratories and insular deployments requires a compass to navigate the multitude of ideas and concepts that make up SDN today. The contribution of this article represents an important step toward such an instrument. It gives a thorough definition of SDN and its interfaces as well as a list of its key attributes. Furthermore, a mapping of interfaces and attributes to SDN use cases is provided, highlighting the relevance of the interfaces and attributes for each scenario. This compass gives guidance to a potential adopter of SDN on whether SDN is in fact the right technology for a specific use case.
Valuing Time and Reliability: Commuters' Mode Choice from a Real Time Congestion Pricing Experiment

The valuation of travel time savings has been an important theme in transportation research because it is the single largest contributor to the benefits of many transportation projects. It also plays a central role in deciding about the size and scope of public investment and has important welfare implications. It can shed important light as to whether any congestion pricing scheme will have increase social welfare or not. And help us understand how commuters make their travel decisions. The San Diego I-15 Congestion Pricing Project (SDCPP) is a demonstration project where an existing High Occupancy Vehicle lane has converted to HOT (High Occupancy/Toll) lane. Beginning in 1996 these lanes were made available to solo drivers who pay a toll. The toll adjusts every six minutes to maintain free flowing traffic on the HOT lane. Carpoolers get to use the lane for free as before. This presents us with a unique opportunity to study commutersâ€™ choice between a tolled and a free alternative based on not only what the commuters say they would do (SP), but also on what they actually did (RP). The general result is that this tolled facility is used by high income, middle aged, homeowners, female commuters. An interesting result that comes out of this analysis is the dual effects of toll. If the actual toll rises above the mean toll then the commuter is more likely to take the FasTrak lane. Another interesting implication that the effect of toll is conditional on the level of uncertainty of travel time and conversely uncertainty in travel time encourages use of FasTrak lane only if toll rises above a threshold value. Commuters are more sensitive to variations in travel time in the morning, specially during the peak, than in the afternoon. Another salient result is that the Value of Time estimates from Stated Preference models are significantly lower than the Revealed Preference estimates. The difference is consistent and persistent across the different models which lead to the conclusion that these differences are real. Probably it reflects the difference in responses of individuals to actual and hypothetical situations.
Learning-Based Procedural Content Generation

Procedural content generation (PCG) has recently become one of the hottest topics in computational intelligence and AI game research. While some substantial progress has been made in this area, there are still several challenges ranging from content evaluation to personalized content generation. In this paper, we present a novel PCG framework based on machine learning, named learning-based procedure content generation (LBPCG), to tackle a number of challenging problems. By exploring and exploiting information gained in game development and public player test, our framework can generate robust content adaptable to end-user or target players on-line with minimal interruption to their gameplay experience. As the data-driven methodology is emphasized in our framework, we develop learning-based enabling techniques to implement the various models required in our framework. For a proof of concept, we have developed a prototype based on the classic open source first-person shooter game, Quake. Simulation results suggest that our framework is promising in generating quality content.
Unifying the Stochastic Spectral Descent for Restricted Boltzmann Machines with Bernoulli or Gaussian Inputs

Stochastic gradient descent based algorithms are typically used as the general optimization tools for most deep learning models. A Restricted Boltzmann Machine (RBM) is a probabilistic generative model that can be stacked to construct deep architectures. For RBM with Bernoulli inputs, non-Euclidean algorithm such as stochastic spectral descent (SSD) has been specifically designed to speed up the convergence with improved use of the gradient estimation by sampling methods. However, the existing algorithm and corresponding theoretical justification depend on the assumption that the possible configurations of inputs are finite, like binary variables. The purpose of this paper is to generalize SSD for Gaussian RBM being capable of mod- eling continuous data, regardless of the previous assumption. We propose the gradient descent methods in non-Euclidean space of parameters, via de- riving the upper bounds of logarithmic partition function for RBMs based on Schatten-infinity norm. We empirically show that the advantage and improvement of SSD over stochastic gradient descent (SGD).
Barleria prionitis Leaf Mediated Synthesis of Silver and Gold Nanocatalysts

Development of rapid, efficient, eco-friendly processes for synthesis of metal nanoparticles is one of the most significant thrust areas of nanobiotechnology. Medicinal plants are rich source of bioactive principles of diverse therapeutic importance. However, this unique phytochemistry can play a critical role not only in reduction of metal ions to their corresponding nanoparticles but also help to stabilize the same. Herein we report for the first time the synthesis of silver and gold nanoparticles using a medicinal plant, Barleria prionitis leaf extract. The synthesis was complete within 5 h and UV-vis spectra exhibited signature peaks at 420 nm and 540 nm for silver and gold nanoparticles, respectively. 3 mM AgNO3 and 2 mM HAuCl4 showed maximum synthesis of silver nanoparticles and gold nanoparticles, respectively at 50°C. High resolution transmission electron microscopy revealed that majority of the silver nanoparticles was spherical in a size range between 10 to 20 nm while gold, between 10 to 25 nm. Various exotic shapes were seen in case of gold nanoparticles like spheres, triangles and pentagons. The bioreduced nanoparticles were further characterized and confirmed using energy dispersive spectra, dynamic light scattering, X-ray diffraction spectrometry. Reducing sugars, ascorbic acid, starch, citric acid along with phenolics were found to be present in the extract that might play a major role in bioreduction and stabilization. GCMS/MS analysis further confirmed the diverse phytochemistry confirming tetradecane, 1-dodecanol, 3,7,11-trimethyl- and tert-hexadecanethiol as most predominant. Both silver and gold nanoparticles exhibited efficient catalytic reduction of o-nitrophenol.
Post-Event Volunteering Legacy: Did the London 2012 Games Induce a Sustainable Volunteer Engagement?

The hosting of the London 2012 Olympic Games was seen as an opportunity to harness the enthusiasm of the 70,000 volunteers involved and to provide a post-event volunteer legacy. A total of 77 individuals who had acted as volunteers in London 2012 were contacted approximately four years after the Games and agreed to complete a web-based open-ended survey. The participants were asked to indicate their level of current volunteering engagement and whether volunteering at the Games had an impact on their current volunteering levels. The study found that the London Olympics were the first volunteer experience for most of the volunteers who completed the survey, with the main motivation to volunteer being anything related to the Olympic Games. Just over half of the respondents are currently volunteering. Lack of time is shown to be the main barrier towards further volunteering commitment. Only half of respondents had been contacted by a volunteering scheme after London 2012. The implications of the findings for a potential volunteering legacy are then explored.
Investigating the Model Building Process with HOMER

An experimental study is presented, which investigates the process of building qualitative simulation models using HOMER, a tool for building qualitative models of systems. HOMER consists of a number of dedicated editors aiming at decomposing the complexity of the model building process into more manageable subtasks. The aims of the present study are a) validating the original task analysis underlying the implemented system and the resulting task decomposition, b) identifying problems and/or misconceptions users encounter when building simulation models, c) assessing the tool’s usability in supporting the model building process, and (d) defining possible improvements and faults of the present version.
Ju l 2 00 1 Nonparametric Volatility Density Estimation

We consider two kinds of stochastic volatility models. Both kinds of models contain a stationary volatility process, the density of which, at a fixed instant in time, we aim to estimate. We discuss discrete time models where for instance a log price process is modeled as the product of a volatility process and i.i. d. noise. We also consider samples of certain continuous time diffusion processes. The sampled time instants will be be equidistant with vanishing distance. A Fourier type deconvolution kernel density estimator based on the logarithm of the squared processes is proposed to estimate the volatility density. Expansions of the bias and bounds on the variances are derived.
On Conditions for Modular Verification in Systems of Synchronising Components

Property preservation is investigated as an approach to modular verification, leading to reduction of the property verification time for formal models. For modelling purposes, formalisms with multi-way synchronisations are considered. For the modular verification technique to work, a specific type of synchronisation is required for which a sufficient and necessary condition is identified. It is a requirement on the semantics of the formalism, which is restricted to permit simultaneous execution only of component moves that make reference to each other. Implications for modular verification of several well-known formalisms for concurrent systems are investigated.
Benefit-Cost Analysis with Local Residents' Stated Preference Information: A Study of Non-Motorized Transport Investments in Pune, India

One of the major difficulties in doing benefit-cost analyses of a development project is to estimate a total economic value of the project benefits, which are usually multi-dimensional and include goods and services that are not traded in the market, and challenges also arise in aggregating the values of different benefits, which may not be mutually exclusive. This paper presents an analysis of a non-motorized transport project in Pune, India, which uses the contingent valuation method to estimate the total value of the project benefits across beneficiaries. A sample of the project beneficiaries are presented with a detailed description of the project and then are asked to vote on whether such a project should be undertaken given different specifications of costs to their households. A function of willingness-to-pay for the project is then derived from the survey answers and the key determinants are found to include household income, distance to the project streets, current use of the transportation modes, future use of the project streets, predicted impacts of the project, and level of trust in the government. The total willingness-to-pay of the local residents is found to be smaller than the total cost of an initial design of the project. Heteroskedasticity is also found to present in the willingness-to-pay models.
A New Two-Stage Ultra-Wide-Band 1-10-GHz Low Noise Amplifier

A new lower complexity ultra-wide-band 1-10-GHz low-noise amplifier is presented in this paper. This LNA only consists of two simple amplifiers with an inter-stage inductor connected. The first stage is a common-gate amplifier that is used to attain the wideband input matching and low noise. A common-source amplifier with shunt-peaked load as the second stage achieves high gain and wideband output matching simultaneously that does not need the source follower. Designed in the TSMC 0.18 mum RFCMOS technology, this proposed UWB LNA achieves the maximum power gain (S21) of 12.1 dB with the 3-dB band from 1 to 10 GHz and input/output reflection coefficient (S11 ,S22) of less than -10 dB from 1 to 10 GHz.The minimum noise figure is 3.8 dB.An IIP3 is -4.1 dBm at 5 GHz.The power dissipation is 12.6 mW from a 1.5-V supply voltage.
Automatic data allocation and buffer management for multi-GPU machines

Multi-GPU machines are being increasingly used in high-performance computing. Each GPU in such a machine has its own memory and does not share the address space either with the host CPU or other GPUs. Hence, applications utilizing multiple GPUs have to manually allocate and manage data on each GPU. Existing works that propose to automate data allocations for GPUs have limitations and inefficiencies in terms of allocation sizes, exploiting reuse, transfer costs, and scalability. We propose a scalable and fully automatic data allocation and buffer management scheme for affine loop nests on multi-GPU machines. We call it the Bounding-Box-based Memory Manager (BBMM). BBMM can perform at runtime, during standard set operations like union, intersection, and difference, finding subset and superset relations on hyperrectangular regions of array data (bounding boxes). It uses these operations along with some compiler assistance to identify, allocate, and manage data required by applications in terms of disjoint bounding boxes. This allows it to (1) allocate exactly or nearly as much data as is required by computations running on each GPU, (2) efficiently track buffer allocations and hence maximize data reuse across tiles and minimize data transfer overhead, and (3) and as a result, maximize utilization of the combined memory on multi-GPU machines. BBMM can work with any choice of parallelizing transformations, computation placement, and scheduling schemes, whether static or dynamic. Experiments run on a four-GPU machine with various scientific programs showed that BBMM reduces data allocations on each GPU by up to 75% compared to current allocation schemes, yields performance of at least 88% of manually written code, and allows excellent weak scaling.
Unsolicited Commercial Email (Spam): An Exploratory Understanding Using Stakeholder Analysis

The growth in the use of email marketing has been accompanied by an enormous increase in the amount of Unsolicited Commercial Email (UCE), popularly known as spam. The unprecedented amount of unsolicited messages is now recognized as a serious problem, costing the society billions of dollars very year. In this paper, we provide an exploratory understanding and conceptualization of unsolicited commercial email. Based on critical characteristics of UCE, we propose a conceptual typology of spam. Further, we identify the key stakeholders in the UCE process and enunciate the roles played by them. Using the stakeholder analysis, we highlight some key mechanisms for addressing the problem of UCE.
La teoría no monetaria de la inflación

Los fenómenos de inflación qiic se vienen desarrollando en eicrtos países de América Latina han sido atribuidos, por algunas opiniones autorizadas, en gran parte a la existencia de desajustes económicos de carácter no monetario/ Este tipo de interpretación, en so estado actual, constitnve más bien una hipótesis para casos especiales que una teoría. Nos proponemos examinar a continuación la posibilidad de una teoría de esta clase, sus condiciones lógicas, y su vinculación con la doctrina general del análisis económico. Fijemos, ante todo, los límites del tema. Se considera a veces como explicación no monetaria de la inflación la que consiste en reducir las causas nronetarias que actúan directamente sobre el valor del dinero a los factores que a su vez las determinan: la alteración de las condiciones monetarias refleja cambios no monetarios o "reales". En rigor, desde luego, sólo es verdaderamente no monetaria una explicación del proceso si acentúa la importancia de los factores reales como causas directas; esto es, si se basa en la existencia de mecanismos no monetarios de causación que operan con fuerza propia sobre el nivel de precios. El presente trabajo se refiere a este tipo de teoría.
Detection of cell nuclei in breast biopsies using receptive fields

A biopsy analysis support system, called BASS, is introduced. The system uses an unsupervised object detection loop involving center-surround receptive fields, and a nonlinearity to mark the location of cell nuclei in an image. Preliminary results of applying BASS to breast cancer biopsies are presented. <<ETX>>