Miracle-dz/UniFAR-FTUs huggingface dataset:
Kazi Chapter IV Information Technology in Construction : How to Realise the Benefits ?

Socio-technical and human cognition elements of information systems / Steve Clarke . [et al.]. p. cm. Includes bibliographical references and index. ISBN 1-59140-104-6 (cloth) 1. Information resources management. 2. Information technology-Social aspects. 3. ABSTRACT Advancement in the utilisation of computers has, in recent years, become a major, even dominating research and development target in the architecture, engineering and construction industry. However, in empirical investigations, no major benefits accruing from construction IT have been found. Why do the many IT applications, which when separately analysed seem so well justified, fail to produce positive impacts when the totality of the construction project is analysed? The objective of this chapter is to find the explanation for this paradox and to provide initial guidelines as to what should be done to correct the situation.
SVM-Based Detection of SAR Images in Partially Developed Speckle Noise

learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of SAR (synthetic aperture radar) images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to real SAR images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected SAR images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (the detection hypotheses) in the original images.
Fundamental studies of co-base superalloy biomedical prototypes fabricated by electron beam melting: microstructures and microstructural architecture

VI TABLE OF CONTENTS . VII LIST OF TABLES . IX LIST OF FIGURES . X STATEMENT OF THE PROBLEM. 1 JUSTIFICATION . 1 PROPOSAL . 2 CHAPTER 1 . 4 INTRODUCTION . 4 CHAPTER 2 . 6 BACKGROUND . 6 2.1 RAPID PROTOTYPING . 6 2.2 GAS ATOMIZATION PROCESS . 7 2.3 ELECTRON BEAM MELTING (EBM) . 7 2.3.1 EBM ADVANTAGES . 8 2.3.1.1 DIRECTIONAL SOLIDIFICATION. 8 2.4 CO-BASE ALLOYS. 9 2.4. 1 Co-Cr-Mo . 10 2.4. 1.1 METALLIC IMPLANTS AND AEROSPACE APPLICATIONS . 12 2.4. 1.2 SAMPLE ACQUISITION . 13 CHAPTER 3 . 14 METHODOLOGY . 14 3.1 EBM. 14 3.2 OPTICAL METALLOGRAPHY . 16
Self-calibration of colormetric parameters in vision systems for autonomous mobile robots

— Vision is an extremely important sense for both humans and robots, providing detailed information about the environment. In the past few years, the use of digital cameras in robotic applications has been increasing sig-niﬁcantly. The use of digital cameras as the main sensor allows the robot to take the relevant information from the surrounding environment and then take decisions. A robust vision system should be able to detect objects reliably and present an accurate representation of the world to higher-level processes, not only under ideal light conditions, but also under changing light intensity and color balance. In this paper, we propose an algorithm for the self-calibration of the most important parameters of digital cameras for robotic applications. The algorithm extracts statistical information from the acquired images, namely the intensity histogram, saturation histogram and information from a black and a white area of the image, to then estimate the colormetric parameters of the camera. We present experimental results obtained with several autonomous robotic platforms: two wheeled platforms, with different architectures of the vision system, a humanoid robots and an autonomous driving agent. The images acquired after calibration show good properties for further processing, independently of the initial conﬁguration of the camera and the type and amount of light of the environment, both indoor and outdoor.
Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms

Several different control methods are used in practice or have been proposed to cost-effectively provision IT resources. Due to the dependency of many control methods on having accurate predictions of the future to make good provisioning decisions, there has been a great deal of literature on prediction workload demand. However, even with all of this literature on workload predictions and their utilization in control algorithms, the understanding of prediction error and how to handle it remains an important open issue and research challenge. In this paper we aim to mend this gap by making the following contributions: (i) Prediction error is modeled to aid in proving worst-case dynamic regret bounds for control algorithms. (ii) Upper bounds on dynamic regret are proven for a variety of algorithms in terms of the prediction error model. In order to choose which algorithm to run without prediction error knowledge, a simple online meta-algorithm is designed. (iii) A detailed analysis of prediction accuracy is done for cloud computing by fitting real-world CPU utilization traces of Azure virtual machines to popular prediction models. (iv) Using real-world trace based simulations of CPU allocation for virtual machines, the proposed meta-algorithm is shown to outperform a popular algorithm selection policy and perform very closely to that of the best algorithm chosen in hindsight.
Local Regression and Likelihood

The Origins of Local Regression. - Local Regression Methods. - Fitting with LOCFIT. - Local Likelihood Estimation. - Density Estimation. - Flexible Local Regression. - Survival and Failure Time Analysis. - Discrimination and Classification. - Variance Estimation and Goodness of Fit. - Bandwidth Selection. - Adaptive Parameter Choice. - Computational Methods. - Optimizing Local Regression.
Dublin City University Video Track Experiments for TREC 2003

In this paper, we describe our experiments for both the News Story Segmentation task and Interactive Search task for TRECVID 2003. Our News Story Segmentation task involved the use of a Support Vector Machine (SVM) to combine evidence from audio-visual analysis tools in order to generate a listing of news stories from a given news programme. Our Search task experiment compared a video retrieval system based on text, image and relevance feedback with a text-only video retrieval system in order to identify which was more effective. In order to do so we developed two variations of our Fischlar video retrieval system and conducted user testing in a controlled lab environment. In this paper we outline our work on both of these two tasks.
Advanced Research in Computer Science and Software Engineering

—Due to continuously increase of fraud in whole world, affects the daily life of people’s and also economic conditions of each country. Different modern fraud detection techniques have been applied to prevent frauds in various fields. In today’s world, education is one of the famous fields where cases of fraud admissions, fraud degree certificates are occurring continuously. Fraudsters are continuously playing with life of students. However many techniques have been applied but still it needs to be improve. In this paper, the survey of different fraud detection techniques has been presented. The goal of this paper is to review different existing techniques used to detect frauds.
Active inference: demystified and compared

Active inference is a first principle account of how autonomous agents operate in dynamic, non-stationary environments. This problem is also considered in reinforcement learning, but limited work exists on comparing the two approaches on the same discrete-state environments. In this paper, we provide: 1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in reinforcement learning; 2) to our knowledge, the first explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline. We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of reinforcement learning. We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration - and account for uncertainty about their environment - in a Bayes optimal fashion. We make these properties explicit by showing that the active inference agent's ability to carry out online planning, in a pure-belief setting, enables it to act optimally, given the non-stationary dynamics of an environment when compared to both Q-learning and Bayesian model-based reinforcement learning agents. We conclude by noting that this formalism can be applied to more complex settings; e.g., robotic arm movement, Atari games, etc., if appropriate generative models can be formulated. In short, we aim to demystify the behavior of active inference agents by presenting an accessible discrete state-space and time formulation, and demonstrate these behaviors in a OpenAI gym environment, alongside reinforcement learning agents.
Revealing the economic value of managed aquifer recharge: Evidence from a contingent valuation study in Italy

Managed aquifer recharge [MAR) is a promising water management tool toward restoring groundwater balance and securing groundwater ecosystem services (i.e., water for drinking, industrial or irrigation use, control of land subsidence, maintenance of environmental flows to groundwater dependent ecosystems, etc.). Obviously, MAR projects can improve the quality of lives of the people by several ways. Thus, from a social perspective, the benefits of MAR cannot and should not be based only on market revenues or costs. Although the value of groundwater, from a social perspective, has been a subject of socio‐economic research, literature on the value of MAR per se is very limited. This paper, focusing on Italy which is a country with extensive utilization of MAR, aims to estimate the economic value of MAR and makes a first step toward filling this gap in the literature. For this purpose, the Contingent Valuation method was implemented to provide a monetary estimate and to explore the factors influencing people's attitude and willingness to pay for MAR. The results show that society holds not only use but also significant nonuse values, which are a part of the total economic value (TEV) of groundwater according to related research efforts. To this end, MAR valuation highlights its social importance for groundwater conservation and provides a solid basis for incorporating its nonmarket benefits into groundwater management policies and assessments.
Seismogenic Zone Structures Revealed by Improved 3‐D Seismic Images in the Nankai Trough off Kumano

To reveal the detailed deformation structures related to plate subduction in the Nankai Trough, we applied up‐to‐date technologies to improve our 3‐D seismic images. This region is dominated by a megasplay fault system that consists of a coseismic out‐of‐sequence thrust branching from the plate interface and separating the inner and outer accretionary prism. The 3‐D seismic volume was acquired off Kumano in 2006 as a preliminary site survey as part of the Nankai Trough Seismogenic Zone Experiment. The preprocessed data quality was improved by recovering the broadband responses and by better attenuating multiple reflections and noise. New reflection images were then produced using prestack time migration and prestack depth migration with updated velocity models. The new velocity model suggests the possible existence of a high‐velocity zone just above the megasplay fault, which might indicate petrophysical alteration in the seismogenic zone. The fault geometry with spatial dip angle variation and the overburden velocities are important factors for further estimating the force distribution along the coseismic fault. Deformation structures newly imaged beneath the Kumano Basin and dipping layers above the megasplay fault imply complex thrusting and possible fluid flow paths within the inner prism. Fine‐scale deformation features are clarified in the shallow areas from the outer prism to the transition zone, which are useful for reconstructing the accretionary prism development. A low‐reflectivity zone, including an isolated layered block, may be originally underthrusted sediments that have been remobilized during later strike‐slip faults along the edge of Kumano Basin.
Talking through design: requirements and resistance in cooperative prototyping

Some analyses are presented of talk between designers and a potential user in a participatory design session where a prototype application was worked with to determine future requirements. We explore the ways in which design suggestions are formulated and argued for, and how requirements emerge as a negotiated product of interaction. On this basis, we re-examine user participation in design and the relationship between protot yping and user requirements. We conclude by offering a notion (gradients of resistance in design space) to help understanding the interplay of the social and the technical in design.
Elrond A Highly Scalable Public Blockchain via Adaptive State Sharding and Secure Proof of Stake

The advent of secure public blockchains through Bitcoin and later Ethereum, has brought forth a notable degree of interest and capital influx, providing the premise for a global wave of permissionless innovation. Despite lofty promises, creating a decentralized, secure and scalable public blockchain has proved to be a strenuous task. This paper proposes Elrond, a novel architecture which goes beyond state of the art by introducing a genuine state sharding scheme for practical scalability, eliminating energy and computational waste while ensuring distributed fairness through a Secure Proof of Stake (SPoS) consensus. Having a strong focus on security, Elrond’s network is built to ensure resistance to known security problems like Sybil attack, Nothing at Stake attack and others. In an ecosystem that strives for interconnectivity, our solution for smart contracts offers an EVM compliant engine to ensure interoperability by design. Preliminary simulations reflect that Elrond exceeds Visa’s average throughput and achieves an improvement beyond three orders of magnitude or 1000x compared to the existing viable approaches, while drastically reducing the costs of bootstrapping and storage to ensure long term sustainability.
Parallel Data Mining for Association Rules on Shared-memory Systems

In this paper we present a new parallel algorithm for data mining of association rules on shared-memory multiprocessors. We study the degree of parallelism, synchronization, and data locality issues, and present optimizations for fast frequency computation. Experiments show that a significant improvement of performance is achieved using our proposed optimizations. We also achieved good speed-up for the parallel algorithm. A lot of data mining tasks (e.g. Association Rules, Sequential Patterns, etc.) use complex pointer-based data structures (e.g. hash trees) that typically suuer from sub-optimal data locality. In the multiprocessor case shared access to these data structures may also result in false sharing. For these tasks it is commonly observed that the recursive data structure is built once and accessed multiple times during each iteration. Furthermore, the access patterns after the build phase are highly ordered. In such cases locality and false sharing sensitive memory placement of these structures can enhance performance signiicantly. We evaluate a set of placement policies for parallel association discovery, and show that simple placement schemes can improve execution time by more than a factor of two. More complex schemes yield additional gains.
Manufacturing & Service Operations Management

Economic growth in many countries is increasingly driven by successful startups that operate as online platforms. These success stories have motivated us to de ﬁ ne and classify various online platforms according to their business models. This study discusses strategic and operational issues arising from ﬁ ve types of online platforms (resource sharing, matching, crowdsourcing, review, and crowdfunding) and presents some research opportunities for operations management scholars to explore. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2018. 0757.
CS229 Lecture notes

We will present a model-free algorithm called REINFORCE that does not require the notion of value functions and Q functions. It turns out to be more convenient to introduce REINFORCE in the finite horizon case, which will be assumed throughout this note: we use τ = (s0, a0, . . . , sT−1, aT−1, sT ) to denote a trajectory, where T < ∞ is the length of the trajectory. Moreover, REINFORCE only applies to learning a randomized policy. We use πθ(a|s) to denote the probability of the policy πθ outputting the action a at state s. The other notations will be the same as in previous lecture notes. The advantage of applying REINFORCE is that we only need to assume that we can sample from the transition probabilities {Psa} and can query the reward function R(s, a) at state s and action a,1 but we do not need to know the analytical form of the transition probabilities or the reward function. We do not explicitly learn the transition probabilities or the reward function either. Let s0 be sampled from some distribution μ. We consider optimizing the expected total payoff of the policy πθ over the parameter θ defined as.
Loyola eCommons Loyola

This study investigated patterns in the development of computational thinking and programming expertise in the context of the Exploring Computer Science (ECS) program, a high school introductory CS course and professional development program designed to foster deep engagement through equitable inquiry around CS concepts. Prior research on programming expertise has identified three general areas of development Ð program comprehension, program planning, and program generation. The pedagogical practices in ECS are consistent with problem solving approaches that support the development of programming expertise. The study took place in a large urban district during the 2016ś17 school year with 28 ECS teachers and 1,931 students. A validated external assessment was used to measure the development of programming expertise. The re-sults indicate that there were medium-sized, statistically significant increases from pretest to posttest, and there were no statistically significant differences by gender or race/ethnicity. After controlling for prior academic achievement, performance in the ECS course correlated with performance on the posttest. With respect to specific programming concepts, the results also provide evidence on the progression of the development of programming expertise. Students seem to develop comprehension and planning expertise prior to expertise in program generation. In addition, students seem to develop expertise with concrete tasks prior to abstract tasks.
Scale Invariant Feature Transform-Scholarpedia

Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and recognition developed by David Lowe (1999, 2004). This descriptor as well as related image descriptors are used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for image matching and object recognition under real-world conditions.
NeuroImage: Clinical

Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes in ﬂ uence cognitive function and could result in improved individual risk strati ﬁ cation. Brain connectome studies in adults and older children have shown that abnormal network properties may be useful as discriminative features and have exploited machine learning models for early diagnosis in a variety of neurological conditions. However, analogous studies in neonates are rare and with limited signi ﬁ cant ﬁ ndings. In this paper, we propose an arti ﬁ cial neural network (ANN) framework for early prediction of cognitive de ﬁ cits in very preterm infants based on functional connectome data from resting state fMRI. Speci ﬁ cally, we conducted feature selection via stacked sparse autoencoder and outcome prediction via support vector machine (SVM). The proposed ANN model was unsupervised learned using brain connectome data from 884 subjects in autism brain imaging data exchange database and SVM was cross-validated on 28 very preterm infants (born at 23 – 31weeks of gestation and without brain injury; scanned at term-equivalent postmenstrual age). Using 90 regions of interests, we found that the ANN model applied to functional connectome data from very premature infants can predict cognitive outcome at 2years of corrected age with an accuracy of 70.6% and area under receiver operating characteristic curve of 0.76. We also noted that several frontal lobe and somatosensory regions, signi ﬁ cantly contributed to prediction of cognitive de ﬁ cits 2years later. Our work can be considered as a proof of concept for utilizing ANN models on functional connectome data to capture the individual variability inherent in the developing brains of preterm infants. The full potential of ANN will be realized and more robust conclusions drawn when applied to much larger neuroimaging datasets, as we plan to do.
WSBen: A Web Services Discovery and Composition Benchmark

A novel benchmark, WSBen, for testing Web services discovery and composition is presented. WSBen includes: (1) a collection of synthetic Web services (WSDL) files with diverse characteristics and sizes; (2) test discovery and composition queries and solutions; and (3) external files for statistical analysis and AI planners. Users can fine-tune the generated WSDL files using various parameters such as skewness or matching type. It is our hope that WSBen provides useful insights for researchers evaluating the performance of Web services discovery and composition algorithms and software
WSBen : A Web Services Discovery and Composition Benchmark Toolkit 1

In this article, a novel benchmark toolkit, WSBen, for testing web services discovery and composition algorithms is presented. The WSBen includes: (1) a collection of synthetically generated web services files in WSDL format with diverse data and model characteristics; (2) queries for testing discovery and composition algorithms; (3) auxiliary files to do statistical analysis on the WSDL test sets; (4) converted WSDL test sets that conventional AI planners can read; and (5) a graphical interface to control all these behaviors. Users can fine-tune the generated WSDL test files by varying underlying network models. To illustrate the application of the WSBen, in addition, we present case studies from three domains: (1) web service composition; (2) AI planning; and (3) the laws of networks in Physics community. It is our hope that WSBen will provide useful insights in evaluating the performance of web services discovery and composition algorithms. The WSBen toolkit is available at: