Miracle-dz/UniFAR-FTUs huggingface dataset:
DEVELOPMENT OF PROBABILISTIC RESRAD 6.0 AND RESRAD-BUILD 3.0 COMPUTER CODES

The RESRAD and RESRAD-BUILD codes are part of the RESRAD family of codes developed by the U.S. Department of Energy. For many years, these deterministic codes have been used as dose assessment tools for cleanup of sites contaminated with radioactive materials. The RESRAD code applies to the cleanup of soils, and the RESRAD-BUILD code applies to the cleanup of buildings and structures. This report is the third in a series documenting the procedures used to enhance the deterministic RESRAD and RESRAD-BUILD codes for probabilistic dose analysis. A six-step procedure was used in developing default parameter distributions and the probabilistic analysis modules. These six steps include (1) listing and categorizing parameters, (2) ranking parameters, (3) developing parameter distributions, (4) testing parameter distributions for probabilistic analysis, (5) developing probabilistic modules, and (6) testing probabilistic modules and integrated codes. These six steps are discussed and summarized in this report. Steps 4 and 5 are documented in separate NUREG/CR reports (NUREG/CR-6676 [Kamboj et al., 2000] and NUREG/CR-6692 [LePoire et al., 2000]). The reports for steps 1, 2, 3, and 6 are included in this report as attachments. The probabilistic versions of RESRAD and RESRAD-BUILD codes provide tools for studying the uncertainty in dose assessment caused by uncertain input parameters. The codes are designed to be user-friendly, but they can be misused. Therefore, it is important that potential users be trained in the proper use of the codes consistent with the guidance in NRC's Standard Review Plan (SRP) for Decommissioning (NRC, 2000) for dose modeling and analysis. Furthermore, it is important that the code users follow the guidance in the Multi-Agency Radiation Survey and Site Investigation Manual (NRC, 1997) on collecting site-specific data for developing probabilistic distributions of parameter values to be used in the RESRAD and RESRAD-BUILD codes. They need to collect enough data to develop values that are as close to real-world distributions of these values as possible to produce meaningful and technically defensible results.
Semi-Parametric Estimation in Network Data and Tools for Conducting Complex Simulation Studies in Causal Inference

Semi-Parametric Estimation in Network Data and Tools for Conducting Complex Simulation Studies in Causal Inference by Oleg A Sofrygin Doctor of Philosophy in Biostatistics University of California, Berkeley Professor Mark Van Der Laan, Chair This dissertation is concerned with application of robust semi-parametric methods to problems of estimation in network-dependent data and the conduct of large-scale simulation studies for causal inference research in epidemiological and medical data. Specifically, Chapter 1 presents a modern semi-parametric approach to estimation of causal effects in a population connected by a single social network. The connectivity of the population units will typically imply that the observed data on these units is no longer independent and identically distributed. Moreover, such social settings typically result in highly dimensional data. This chapter contributes to current statistical methodology by presenting an approach that allows valid estimation and inference and addresses the statistical issues specific to such networked population datasets. The framework of semi-parametric estimation, called the targeted maximum likelihood estimation (TMLE), is presented. This framework improves upon the existing methods by offering robustness, weakened sensitivity to near positivity violations, as well as the ability to deal with high-dimensionality issues of social network data. In particular, this approach relies on the accurate reflection of the background knowledge available for a given scientific problem, allowing estimation and inference without having to make unrealistic assumptions about the structure of the data. In addition, this chapter generalizes previous work describing estimation of complex causal parameters, such as the direct treatment effects under interference and the causal effects of interventions on social network structure. Although the past decade has produced many contributions towards estimation of causal effects in social network settings, there has been considerably less research on the topic of variance estimation for such highly-dependent data. This chapter presents an approach to constructing valid inference, providing a variance estimator that is scalable to very large datasets with highly-connected observations. The efficient open-source software implementation of these methods also accompanies this chapter. Chapter 2 presents opensource software tools for conduct of reproducible simulation studies for complex parameters that emerge from application of causal inference methods in epidemiological and medical research. This simulation software is build on the framework of non-parametric structural
Reduced-Rank Adaptive Least Bit-Error-Rate Detection in Hybrid Direct-Sequence Time-Hopping Ultrawide Bandwidth Systems

The design of high-efficiency low-complexity detection schemes for ultrawide bandwidth (UWB) systems is highly challenging. This paper proposes a reduced-rank adaptive multiuser detection (MUD) scheme that is operated in least bit-error-rate (LBER) principles for hybrid direct-sequence time-hopping UWB (DS-TH UWB) systems. The principal component analysis (PCA)-assisted rank-reduction technique is employed to obtain a detection subspace, where the reduced-rank adaptive LBER-MUD is carried out. The reduced-rank adaptive LBER-MUD is free from channel estimation and does not require knowledge about the number of resolvable multipaths and the multipaths' strength. In this paper, the BER performance of the hybrid DS-TH UWB systems using the proposed detection scheme is investigated, assuming communications over UWB channels modeled by the Saleh-Valenzuela channel model. Our studies and performance results show that, given a reasonable rank of the detection subspace, the reduced-rank adaptive LBER-MUD can efficiently mitigate both multiuser and intersymbol interference (ISI) and achieve the diversity gain promised by the UWB systems.
Embedded Applications of MS-PSO-BP on Wind/Storage Power Forecasting

Higher proportion wind power penetration has great impact on grid operation and dispatching, intelligent hybrid algorithm is proposed to cope with inaccurate schedule forecast. Firstly, hybrid algorithm of MS-PSO-BP (Mathematical Statistics, Particle Swarm Optimization, Back Propagation neural network) is proposed to improve the wind power system prediction accuracy. MS is used to optimize artificial neural network training sample, PSO-BP (particle swarm combined with back propagation neural network) is employed on prediction error dynamic revision. From the angle of root mean square error (RMSE), the mean absolute error (MAE) and convergence rate, analysis and comparison of several intelligent algorithms (BP, RBP, PSO-BP, MS-BP, MS-RBP, MS-PSO-BP) are done to verify the availability of the proposed prediction method. Further, due to the physical function of energy storage in improving accuracy of schedule pre-fabrication, a mathematical statistical method is proposed to determine the optimal capacity of the storage batteries in power forecasting based on the historical statistical data of wind farm. Algorithm feasibility is validated by application of experiment simulation and comparative analysis.
When does abstraction occur in semantic memory: insights from distributional models

ABSTRACT Abstraction is a core principle of Distributional Semantic Models (DSMs) that learn semantic representations for words by applying dimensional reduction to statistical redundancies in language. Although the posited learning mechanisms vary widely, virtually all DSMs are prototype models in that they create a single abstract representation of a word’s meaning. This stands in stark contrast to accounts of categorisation that have very much converged on the superiority of exemplar models. However, there is a small but growing group of accounts in psychology, linguistics, and information retrieval that are exemplar-based semantic models. These models borrow many of the ideas that have led to the prominence of exemplar models in fields such as categorisation. Exemplar-based DSMs posit only an episodic store, not a semantic one. Rather than applying abstraction mechanisms at learning, these DSMs posit that semantic abstraction is an emergent artifact of retrieval from episodic memory.
Probabilistic models of natural language semantics

Fakultät für Mathematik und Informatik Institut für Informatik Probabilistic models of natural language semantics by Ingmar Schuster This thesis tackles the problem of modeling the semantics of natural language. Neural Network models are reviewed and a new Bayesian approach is developed and evaluated. As the performance of standard Monte Carlo algorithms proofed to be unsatisfactory for the developed models, the main focus lies on a new adaptive algorithm from the Sequential Monte Carlo (SMC) family. The Gradient Importance Sampling (GRIS) algorithm developed in the thesis is shown to give very good performance as compared to many adaptive Markov Chain Monte Carlo (MCMC) algorithms on a range of complex target distributions. Another advantage as compared to MCMC is that GRIS provides a straight forward estimate of model evidence. Finally, Sample Inflation is introduced as a means to reduce variance and speed up mode finding in Importance Sampling and SMC algorithms. Sample Inflation provides provably consistent estimates and is empirically found to improve convergence of integral estimates.
Block turbo codes for multicarrier local loop transmission

We investigate the performance of different block turbo codes in the context of Enhanced VDSL. We first describe the VDSL system used in our simulation link. We then propose an iterative decoding algorithm based on Pyndiah's (1998) SISO decoder for product turbo block codes that can be implemented with reasonable complexity. We also illustrate several results for various QAM modulation schemes. The coding gains obtained for the different codes and modulations then allow us to predict the attainable bit rates as a function of the line length. We show that such a turbo block decoding outperforms the classical Reed Solomon hard decoding used in the ADSL system.
Self-Adjusting Monitor for Measuring Aging Rate and Advancement

Time-variant age information of different parts of a system can be used for system-level performance improvement through high-level task scheduling, thus extending the life-time of the system. Progressive age information should provide the age state that the system is in, and the rate that it is being aged at. In this paper, we propose a structure that monitors certain paths of a circuit and detects its gradual age growth, and provides the aging rate and aging state of the circuit. The proposed monitors are placed on a selected set of nodes that represent a timing bottleneck of the system. These monitors sample expected data on these nodes, and compare them with the expected values. The timing of sampling changes as the circuit ages and its delay increases. The timing of sampling will provide a measure of aging advancement of a circuit. To assess the efficacy of the proposed method and compare it with other state-of-the-art aging monitors, we use them on selected nodes of the execution unit of different processors, as well as some circuits from ITC99 benchmarks. The results reveal that the precision of our proposed method is between 0.12 (ns) to 0.401 (ns). Its Area and power overhead are negligible and are about 2.13 and 0.69 percent respectively.
The Network Robot System : Enabling Social Robots in the Real World

The field of social human-robot interaction has grown over the past decade to become a mainstream research topic in robotics, and great advances have been made in many of the essential technologies required for the realization of robots able to interact with people as social peers. Yet, such robots are seldom seen outside of the laboratory, indicating that there are still limitations preventing the real-world deployment of social robots to provide services in society. In this thesis, I will address several key limitations and present a software framework designed to enable social robots to be deployed in real environments using today’s technology. What I propose is a “Network Robot System” approach, in which environmental sensor networks and ambient intelligence systems are used to augment a robot’s recognition capabilities for navigational interactions; a human operator is employed to assist a fleet of robots in difficult recognition or judgment tasks for conversational interactions; and centralized servers and knowledge resources are utilized to coordinate robots and deliver personalized services. Each of the topics addressed in this thesis is grounded in practical experience, and all of the concepts and elements presented here have been tested not only in the laboratory, but in experiments and demonstrations of social robots in real public and commercial spaces. Based on this experience, I present design requirements and working prototypes of each element in the framework and show several examples of their use in field experiments and demonstrations of multiple social robots in real public and commercial spaces.
Combining MMSE enhancement with LA model adaptation for robust automatic speech recognition

A new scheme for robust automatic speech recognition is presented. The scheme combines minimum mean square error (MMSE)-based enhancement with Log-Add (LA) model adaptation. Experimental evaluations show that the proposed MMSE-LA scheme can achieve significant improvement in recognition across a wide range of signal-to-noise ratios, especially in very noisy environments.
Influence of social networking sites on scholarly communication: A study using literature in Artificial Intelligence

With the advent of Web 2.0 tools and especially social media, researchers are increasingly active on the Web. This has resulted in a transformation in the scholarly communication process through which researchers share and bookmark research works in online platforms. In the present study, the influence of social networking sites on the field of Artificial Intelligence research was studied. The study analysed the influence of social networking sites on both conference papers and journal articles, as in a field like Artificial Intelligence both channels of research dissemination play important roles. The top 100 cited journal articles and conference papers in Artificial Intelligence published in 2009 and 2013 were analysed for their presence on social networking sites and online reference managers. It was found that amongst social networking sites, Mendeley had the greatest influence on research in Artificial Intelligence. Mendeley played the most remarkable role in transforming scholarly communication with the highest coverage of both journal articles and conference papers for both the years 2009 and 2013. It was found that the influence of social networking sites was greater for journal articles than conference papers, the latter still having a lower average Mendeley readership. The highest correlation between citation counts and Mendeley readership was found for journal articles published in 2009, followed by journal articles published in 2013, conference papers published in 2009 and conference papers published in 2013. The average Mendeley readership was also higher for journal articles than for conference papers. Mendeley readership was also found to be higher for journal articles and conference papers published earlier in time, indicating that research works published earlier in time were more popular in social networking sites and online reference managers.
Academic researchers’ behavioural intention to use academic social networking sites: A case of Malaysian research universities

This study seeks to validate a comprehensive model of academic researchers’ intention in the context of academic social networking sites (ASNSs). It uses the Unified Theory of Acceptance and Use of Technology (UTAUT) model with constructs of perceived security, perceived privacy, trust, attitude towards technology, and communication benefits as well as age, gender and experience as moderator variables. The survey results supported the positive relationships between the factors in the model, except effort expectancy, which had no significant effect on behavioural intention. However, the results of three moderator variables demonstrated that they had no significant effect on the adoption of ASNSs. The finding of this research will provide directions for ASNSs providers to support them in developing effective ASNSs for academic researchers.
Exploring Non-Native English Speaker Teachers' Classroom Language Use in South Korean Elementary Schools.

The teaching of English as a foreign language in South Korean public schools has seen the implementation of a number of new innovations. One such innovation, the teaching of English through English, dubbed TETE, is a government-initiated policy that requires public schools to teach English by only using English. Nevertheless, studies reveal that teachers are not implementing the policy. The current study, through a series of observations and interviews, ascertained that teachers were not implementing the government policy at the elementary-school level due to a conflict in government decrees, making it difficult for them to teach English by only using English while maintaining student motivation to learn English. The study reveals the importance that teachers place on the belief that motivation needs to be maintained at all costs, even superseding the need to maximize target language exposure. The paper calls for further studies of teachers who have established techniques to maintain student motivations for learning the target language while teaching exclusively in the target language, as well as touching upon the idea of the need for an alternative to the TETE policy.
Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

Artificial Neural Networks are a computational paradigm formed based on the neural structure of intelligent organisms to gain better knowledge. Artificial neural networks are often used for various computing purposes. One of them is for prediction (forecasting) data. The type of artificial neural network that is often used for prediction is the artificial neural network backpropagation because the backpropagation algorithm is able to learn from previous data and recognize the data pattern. So from this pattern backpropagation able to analyze and predict what will happen in the future. In this study, the data to be predicted is Human Development Index data from 2011 to 2015. Data sourced from the Central Bureau of Statistics of North Sumatra. This research uses 5 architectural models: 3-8-1, 3-18-1, 3-28-1, 3-16-1 and 3-48-1. From the 5 models of this architecture, the best accuracy is obtained from the architectural model 3-48-1 with 100% accuracy rate, with the epoch of 5480 iterations and MSE 0.0006386600 with error level 0.001 to 0.05. Thus, backpropagation algorithm using 3-48-1 model is good enough when used for data prediction.
Strategy, Spatial Visualization Ability, and Performance on Website Navigation

Spatial Visualization Ability is a determinant of performance efficiency on website navigation tasks. It is unclear, however, why this is the case; though the spatial metaphor for web “navigation” is popular, it is not accurate. We put forth a hypothesis that strategy use is another important component of web navigation efficiency that relates to Spatial Visualization Ability. To investigate this hypothesis, we designed a series of experiments using numeric indicators instead of verbal labels in model websites. We then characterized the strategies that participants used on each website into categories based on their resemblance to prototype strategies. We found that, though strategy choice does not seem to depend on Spatial Visualization Ability, precise implementation does seem to depend to some degree on Spatial Visualization Ability.
Digital technology for conviviality : making the most of students' energy and imagination in learning environments

This thesis contributes to the body of research on constructionist philosophy. It expands the conceptual framework to a broader scale by linking constructionism to Ivan Illich’s notion of conviviality. An emphasis on developing convivial learning environments has been made. The learning activities were developed with a special highlight on the idea of emergent design. The emphasis on conviviality and emergent design allowed a systematic and theorized framework to identify and discuss the pattern in the developmental process of learning activities, which is an area in the constructionist framework that needs more study. I gave special emphasis on learning activities that involve tool construction. I show how the making of tools could strengthen conviviality. I present a concept of dynamic equilibrium that allows different methods of learning and teaching to intertwine. I present a case study based on a five-week fieldwork conducted at a rural school of northern Thailand.
A 360◦ comprehensive study of quantitative pricing of options, ranging from theory and computation to model calibration and empirical studies

Classified by different purposes and contributions, this thesis is divided into three parts. In specific, Part 1 focuses on different option pricing models with analytical pricing formula for European options, and three topics covered by the first part are 1) a new closed-form pricing formula is obtained under a skew Brownian motion; 2) an analytical approximation formula is derived when regime-switching is introduced into the Heston model; and 3) a modified Black-Scholes option pricing formula is presented when the underlying price is bounded. Part 2 is devoted to solving option pricing problems with different solution techniques, including analytical approximation, series solution techniques, integral equation approaches and numerical methods. Part 3 considers calibration problems of the Heston model, the Stein-Stein model and the local regime-Switching model with real market data.
Assessing the Translation of Google and Microsoft Bing in Translating Political Texts from Arabic into English

Online machine translation (OMT) systems are widely used throughout the world freely or at low cost. Most of these systems use statistical machine translation (SMT) that is based on a corpus full with translation examples to learn from them how to translate correctly. Online automatic machine translation systems differ widely in their effectiveness and accuracy. Therefore, the wide spread of such translation platforms make it necessary to evaluate the output in order to shed light on the capacity and usability of each system. The present study have selected the most prominent translation systems, Google and Microsoft to test which system is better and more reliable in rendering English<>Arabic translation. To conduct the study, the researcher has chosen automatic evaluation of the two system outputs by using the most popular automatic evacuation metric BLEU. The study’s corpus consists of 25 Arabic sentences extracted from Petra News Agency of Jordan with its human reference translation from the English version of Petra. The result of the research showed that Google translate achieves better results than Microsoft Bing in comparison to human referenced translation. However, Machine Translation (MT) is still far from reaching fully automatic translation of a quality obtained by human translators.