Miracle-dz/UniFAR-FTUs huggingface dataset:
A benchmark study on intelligent sampling techniques in Monte Carlo simulation

In recent years, new, intelligent and efficient sampling techniques for Monte Carlo simulation have been developed. However, when such new techniques are introduced, they are compared to one or two existing techniques, and their performance is evaluated over two or three problems. A literature survey shows that benchmark studies, comparing the performance of several techniques over several problems, are rarely found. This article presents a benchmark study, comparing Simple or Crude Monte Carlo with four modern sampling techniques: Importance Sampling Monte Carlo, Asymptotic Sampling, Enhanced Sampling and Subset Simulation; which are studied over six problems. Moreover, these techniques are combined with three schemes for generating the underlying samples: Simple Sampling, Latin Hypercube Sampling and Antithetic Variates Sampling. Hence, a total of fifteen sampling strategy combinations are explored herein. Due to space constrains, results are presented for only three of the six problems studied; conclusions, however, cover all problems studied. Results show that Importance Sampling using design points is extremely efficient for evaluating small failure probabilities; however, finding the design point can be an issue for some problems. Subset Simulation presented very good performance for all problems studied herein. Although similar, Enhanced Sampling performed better than Asymptotic Sampling for the problems considered: this is explained by the fact that in Enhanced Sampling the same set of samples is used for all support points; hence a larger number of support points can be employed without increasing the computational cost. Finally, the performance of all the above techniques was improved when combined with Latin Hypercube Sampling, in comparison to Simple or Antithetic Variates sampling.
Composing The Performance: An exploration of musical composition as a dramaturgical strategy in contemporary intermedial theatre

This thesis explores musical composition in the context of contemporary theatre. It suggests methods where compositional thinking – broadly understood – can be used as a dramaturgical device in the creation of theatre performances. Such musicalization of theatre provides new methods for an artistic practice and questions traditional artistic hierarchies and working structures. The investigation departs from a process of transference of radiophonic art to stage performances. From this a series of interdisciplinary works in the intersection between concert, theatre performance, radio play and music theatre has been created, each with different approaches to the roles and functions of musical composition. Through these works the creative processes and methods are examined and analysed. The notion of vertical dramaturgy and polyphony in theatre contexts is discussed and demonstrated, which pinpoints the significance of interaction between the elements of a performance. The interplay between dramatic writing, acting methods and musical composition is under scrutiny, as is the way in which visual elements as video, light and scenography are integrated into polyphonic theatre experiences. Transformation is an essential compositional and dramaturgical tool as it allows a performance to move seamlessly between different art forms, continuously creating new relationships. Through the research a model for interdisciplinary, collaborative work in performing arts emerges. The concepts of Shared space and Shared time are introduced and enable a collective ‘thinking-through-practice’ and expansion of the artistic roles. This joint arena of space and time opens up new connections, interactions and mutual exchanges between artists and art forms. The outcome provides a range of artistic methods in the field of contemporary intermedial theatre where musical composition is one of the dramaturgical devices for composing the performances.
Improving The Prediction Accuracy Of Financial Time Series By Using Multi-Neural Network Systems And

The objective of this thesis is the evaluation of the performance improvement that can be achieved by utilizing a master neural network design and enhanced data preprocessing methods for the prediction of currency exchange rates. The prediction of nancial time series, in general, has been identi ed as a challenging task since nancial time series have a nonlinear and nonstationary behavior. The master neural network design is a hierarchical neural network system where the master network estimates an optimal weight combination of the corresponding output of several individual trained neural networks. Using nancial statistical features, we train master neural networks to predict the behavior of nancial time series. The performance of the system is evaluated by using two metrics, accuracy and pro tability. Modeling accuracy is measured by the metric of predicting the direction of the next return. Pro tability is measured by performing a real market simulation with and without consideration of trading costs of 0.05% per round-trip (one buy and one sell) transaction. Finally, the performance of our neural network systems is compared with a simple buy-and-hold model and a linear regression based trading model. Our experiments demonstrate signi cant improvements over the previous two models in accuracy and pro tability on real market data by using the master networks design. Trading based on the predictions of our neural network systems always produced a higher positive return, even in cases where the regression based trading model or the buy-and-hold strategy generated a loss. Our preliminary investigations nd that there is a de nite bene t in using neural network models for the prediction of nancial time series and the trading of currency exchange rates. v
A Weakness in the 4.2BSD Unix† TCP/IP Software

The 4.2 Berkeley Software Distribution of the Unix operating system (4.2BSD for short) features an extensive body of software based on the "TCP/IP" family of protocols. In particular, each 4.2BSD system "trusts" some set of other systems, allowing users logged into trusted systems to execute commands via a TCP/IP network without supplying a password. These notes describe how the design of TCP/IP and the 4.2BSD implementation allow users on untrusted and possibly very distant hosts to masquerade as users on trusted hosts. Bell Labs has a growing TCP/IP network connecting machines with varying security needs; perhaps steps should be taken to reduce their vulnerability to each other.
Machine Learning Approach to Search Query Classification

Search query classification is a necessary step for a number of information retrieval tasks. This chapter presents an approach to non-hierarchical classification of search queries that focuses on two specific areas of machine learning: short text classification and limited manual labeling. Typically, search queries are short, display little class specific information per single query and are therefore a weak source for traditional machine learning. To improve the effectiveness of the classification process the chapter introduces background knowledge discovery by using information retrieval techniques. The proposed approach is applied to a task of age classification of a corpus of queries from a commercial search engine. In the process, various classification scenarios are generated and executed, providing insight into choice, significance and range of tuning parameters.
Local Tableaux for Reasoning in Distributed Description Logics

The last decade of basic research in the area of Description Logics (DL) has created a stable theory, efficient inference procedures, and has demonstrated a wide applicability of DL to knowledge representation and reasoning. The success of DL in the semantic web and the distributed nature of the last one inspired recently a proposal of Distributed DL framework (DDL). DDL is composed of a set of stand alone DLs pairwise interrelated with each other via collection of bridge rules. In this paper, we investigate the reasoning mechanisms in DDL and introduce a tableau-based reasoning algorithm for DDL, built on the top of the state of the art tableaux reasoners for DL. We also describe a first prototype implementation of the proposed algorithm.
A New Musical Interface Using Acoustic Tap Tracking

Input interface Output interface Computing THE ARCHITECTURE OF HYPER-INSTRUMENTS (HI) • HI’s usually separate the control interface and the sound generation ⇒ risk of developing poor mapping strategies • Break down of the perceptual linkage between the physical action and the musical reaction • Expressive information in music relies on details that are usually difficult to perceive at the level of the performer's gestures
The statistical reasoning assessment: development and validation of a research tool

A flow control valve for use in a hydraulic system including a variable orifice for creating a pressure drop to control the rate of fluid flow to a fluid pressure operated device. A pressure compensating device incorporated in the valve is responsive to deviations in pressure drop across the orifice to modulate the flow thereto and thereby maintain a preselected pressure drop across the orifice. Opening of the variable orifice is by inlet pressure against the force of a light spring to an adjustable stop. Retarding opening of the orifice is an expansible chamber having variable volume and connected through an adjustable flow restriction valve to the control valve outlet. Adjustment of the flow restriction valve controls the rate of opening of the variable orifice to reduce pressure shock to the hydraulic system upon initiation of flow.
Keeping the Kids in School: What the drama class tells us

This study used questionnaires and interviews to discover senior secondary students’ perceptions of their drama class experiences in three different schools from an Ontario public board of education. Questionnaire results from entire classes supported interview results from four students in each class. No notable differences in student perceptions emerged, either between boys and girls, or among the three schools. Findings uncovered student attitudes about scholastic motivation, retention, and success that might be applicable across subject areas. For example, they enjoyed opportunities for physical mobility, peer interaction, and self-expression. They praised authentic, challenging, and relevant learning tasks that culminated in displays for audiences beyond their own classrooms. More than subject content, students valued drama class as a vehicle for enduring personal and social growth.
On the Evolving Transformation System Representation of Fairy Tales

This thesis presents an initial application of the Evolving Transformation System (ETS) formalism to the structural representation of Russian fairy tales. This work was motivated by the needs of information retrieval (IR) in general, but specifically concentrates on proposing a new type of document representation. The new representation is based on the temporal or constructive process of events that occur in the mind of the “generic” fairy tale listener/reader. Representing documents in this way yields several advantages to using ETS over traditional IR techniques, such as being able to retrieve partial information from a document (i.e. a single sentence or paragraph), fast searching by traversing the multilevel hierarchial class structure generated by the ETS intelligent process, and locating similar documents based on structural similarity rather than key words. Moreover, the ETS formalism suggests an explanation for the mechanism behind plot anticipation by an experienced reader as well as various levels of fairy tale conceptualization. Using the ETS formalism and in-depth analysis of many fairy tales, we propose approximately 40 ETS primitives that make up the underlying structure of events for
The 'aperture problem' in complex moving scenes

The initial encoding of direction by mammals occurs in striate cortex by neurons with small receptive fields that are tuned to narrow bands of the spatiotemporal frequency spectrum. Individual neurons are unable to signal the global direction of 2D motion and are instead sensitive to the 1D component of motion perpendicular to a moving edge. To compute 2D velocity, it is necessary to integrate over a range of 1D velocity sensors. In this work I probe the ability of the visual system to compute 2D velocity from a range of stimulus classes, including naturally contoured scenes, natural scenes and a global-Gabor array. My research shows that the motion stream is highly sensitive to the distribution of local orientations present in a moving image, but is largely insensitive to their spatial second-order statistics. I present a computational model of two-dimensional motion processing that is able to derive precise estimates of 2D motion directly from complex natural scenes. The model produces errors when confronted with stimuli composed of anisotropic orientation configurations and is able to capture many of the biases and errors experienced by human observers. Finally, I argue that observers’ misperceptions of 2D motion does not reflect a sub-optimal 2D motion strategy, but reflects a compromise between the competing requirements of defining motions in a spatially discrete manner across space, and the ability to accurately estimate 1D motions, on which the computation of 2D velocity must rely.
Learning-BEB: Avoiding Collisions in WLAN

Abstract —Random access protocols have been the mechanismof choice for most WLANs, thanks to their simplicity anddistributed nature. Nevertheless, these advantages come at theprice of sub-optimal channel utilization because of empty slotsand collisions. In previous random access protocols, the stationstransmit on the channel without any clue of other stations’intentions to transmit. In this article we provide a framework tostudy the efﬁciency of channel access protocols. This frameworkis used to analyze the efﬁciency of the Binary Exponential Backoffmechanism and the maximum achievable efﬁciency that can beobtained from any completely random access protocol. Then wepropose Learning-BEB (L-BEB). L-BEB is exactly the same as legacy BEB, with one exception:L-BEB chooses a deterministic backoff value after a successfultransmission. We call this value the virtual frame size ( V ). Thissubtle modiﬁcation signiﬁcantly reduces the number of collisions.It can be observed that, as the system runs, the number of colli-sions is progressively reduced. Thus we conclude that the systemlearns. Further, if the number of contending stations is equal orlower than
A Research On Retrieving And Parsing Of Multiple Web Pages For Storing Them In Large Databases

This paper intends to present one of the studies we jointly done during the research for our Ph.D. theses. Cristian Bucur`s thesis aim is to study how the knowledge stored in web pages from various sources can be retrieved and classified. Bogdan Tudorica`s thesis aim is to study the ways to manage large quantities of data for various purposes (especially through use of new technologies, such as NoSQL databases. As such, the application we are describing in this paper is a mixed one, containing both web page crawling and parsing and data storage in a commonly used NoSQL database.
Transfer of Knowledge in Reinforcement Learning through Policy-based Proto-Value Functions

Transfer learning is a process that occurs when learning in one context affects the performances of learning in another contexts. On the other hand, reinforcement learning is a very general learning paradigm, which potentially can be used to solve any learning and control problem. Nevertheless it suffers from high complexity problems. Transferring knowledge across di erent tasks can be important, since it makes possible to re-use existing experience in order to reduce the complexity both in terms of required experience and time. There have already been several proposals of transfer learning techniques inside reinforcement learning. However, these consist of separate e orts, which on a rst sight appear to have few points of contacts. In this thesis we give two main contributions. The rst contribution is to provide a sketch for a uni ed framework of transfer learning in reinforcement learning, identifying three di erent sub-objectives for transfer. Among these three objectives, we propose solution strategies for what concerns one of them: representation transfer, i.e. learning and transfer of representation across tasks. Our approach is based on proto-value functions, a set of techniques that enables learning of the domain representation via learning of basis functions for function approximation.
CHANNEL ESTIMATION FOR MIMO OFDM USING PILOT CARRIER

A multiple-input multiple-output (MIMO) communication system combined with the orthogonal frequency division multiplexing (OFDM) modulation technique can achieve reliable high data rate transmission over broadband wireless channels. Channel state information for both single-input single-output (SISO) and MIMO systems based on pilot aided arrangement is investigated in this paper. The estimation of channel at pilot frequencies with conventional Least Square (LS) and Minimum Mean Square (MMSE) estimation algorithms is carried out through Matlab simulation. The performance of MIMO OFDM and SISO OFDM are evaluated on the basis of Bit Error Rate (BER) and Mean Square Error (MSE) level. Further enhancement of performance can be achieved through maximum diversity Space Time Block Coding (STBC) and Maximum Likelihood Detection at transmission and reception ends respectively. MMSE estimation has been shown to perform much better than LS but is more complex than LS for the MIMO system using pilot carriers.
New metrics for space-time decoding with imperfect synchronization

This thesis emphasizes the unexploited challenges in an unsynchronized space-time block coded (STBC) system. This research involves the unknown Rayleigh fading channel parameters and the carrier frequency offset (CFO) at the receiver. Plenty of research has been done on the optimal estimation of these parameters over the period of time. Considering a practical receiver system with a non-ideal estimator leads to the necessity of introducing the erroneous estimates of these unknown parameters into the detection algorithm. It is therefore necessary to augment the existing conventional detectors, such as maximum likelihood (ML), maximal ratio combiner (MRC) and minimum mean square error (MMSE). This work is an extension of these conventional detection schemes, which will make the proposed detectors capable of overcoming the challenge of mismatch scenario. The proposed detectors are designed to optimize the output at the receiver, for various estimation error variances of the uncertainties. Extensive simulation results demonstrate that the proposed schemes outperform conventional detectors. The performance of these proposed detectors is analyzed by plotting the bit error rate (BER) over signal to noise ratio (SNR) curves for various modulation schemes, such as M-PSK and M-QAM. At the end, the computational complexities of the proposed detectors from the implementation point of view is discussed briefly.
Geometry and Information Retrieval

The Vector Space Model (VSM) is one of the most successful models in IR. It was firstly proposed by G. Salton, A. Wong and C. S. Yang [13] in the 70s, and it is used until nowadays. It represents each document as an n-tuple of nonnegative real numbers. Each coordinate corresponds to one of the n different terms present in the collection. The value of the coordinate describes the importance of the term in the document, i. e. , a very important term in the document has a higher value of the coordinate than less important ones. The value (weight) of the importance of the term i on the document j (the i-th component of the j-th document vector) is denoted by wij, and it is often defined as:
Building Search Applications: Lucene, Lingpipe, and Gate

Lucene, LingPipe, and Gate are popular open source tools to build powerful search applications. Building Search Applications describes functions from Lucene that include indexing, searching, ranking, and spelling correction to build search engines. Use LingPipe and Gate to find the meaning of text to make search applications more useful. With this book you will learn to: - Extract tokens from text using custom tokenizers and analyzers from Lucene, LingPipe, and Gate. - Construct a search engine index with an optional backend database to manage large document collections. - Explore the wide range of Lucene queries to search an index, understand the ranking algorithm for a query, and suggest spelling corrections. - Find the names of people, places, and other entities in text using LingPipe and Gate. - Categorize documents by topic using classifiers and build groups of self-organized documents using clustering algorithms from LingPipe. - Create a Web crawler to scan the We b, Intranet, or desktop using Nutch. - Track the sentiment of articles published on the Web with LingPipe - Detect plagiarism of documents using a registered document collection.
New techniques for efficiently discovering frequent patterns

Because of its theoretical and practical importance, the field of frequent pattern mining has been and remain to be one of the most active research area in KDD. In this dissertation, we study three different problems in frequent pattern mining, mining multiple datasets, mining streaming data, and mining large-scale structures from graph datasets. Our study has not only extended the breadth of frequent pattern mining, but also brought new techniques and algorithms into this field. Specifically, our contributions are as follows. (1) Mining multiple datasets. We develop a systematic approach to generate efficient query plans for a single mining query across multiple datasets. We also propose methods to simultaneously optimize multiple such queries and utilize the past mining results in a query-intensive KDD environment. Our experimental results have shown a speedup up to two-order of magnitude comparing with the naive methods without these optimizations. (2) Mining frequent itemsets over streaming data. We propose a new algorithm StreamMining to discover the frequent itemsets over streaming data. In a single pass, StreamMining will guarantee to find a superset of frequent itemsets, but false positive may occur. If the second pass is allowed, StreamMining will be able to remove the false positive and find the exact frequent itemsets. Our detailed evaluation using both synthetic and real datasets has shown our one-pass algorithm is very accurate in practice, and is also very memory efficient. (3) Mining frequent large-scale structures from graph datasets. We develop a new framework to discover the frequent large-scale structures from graph datasets. This framework is derived from a mathematical concept, topological minor. In this framework, we propose a new algorithm TSMiner, which efficiently enumerates all the frequent large-scale structures in a graph dataset, and a new approach called relabeling function to perform constraint mining. We apply our framework to protein structure data and discover meaningful topological structures. Finally, we demonstrate the viability and scalability of the proposed algorithms on both real and synthetic datasets.