Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovering Statistics Using Ibm Spss Statistics

Unrivalled in the way it makes the teaching of statistics compelling and accessible to even the most anxious of students, the only statistics textbook you and your students will ever need just got better! Andy Field's comprehensive and bestselling Discovering Statistics Using SPSS 4th Edition takes students from introductory statistical concepts through very advanced concepts, incorporating SPSS throughout. The Fourth Edition focuses on providing essential content updates, better accessibility to key features, more instructor resources, and more content specific to select disciplines. It also incorporates powerful new digital developments on the textbook's companion website(visit sagepub.com for more information). WebAssign The Fourth Edition will be available on WebAssign, allowing instructors to produce and manage assignments with their studnets online using a grade book that allows them to track and monitor students' progress. Students receive unlimited practice using a combination of approximately 2000 multiple choice and algorithmic questions. WebAssign provided students with instant feedback and links directly to the accompanying eBook section where the concept was covered, allowing students to find the correct solution. SAGE MobileStudy SAGE MobileStudy allows students equipped with smartphones and tablets to access select material, such as Cramming Sam's Study Tips, anywhere they receive mobile service. With QR codes included throughout the text, it's easy for students to get right to the section they need to study, allowing them to continue their study from virtually anywhere, even when they are away from thier printed copy of the text. Click here to preview the MobileStudy site (available late spring 2013). Education and Sport Sciences instructor support materials with enhanced ones for Psychology, Business and Management and the Health sciences make the book even more relevant to a wider range of subjects across the social sciences and where statistics is taught to a cross-disciplinary audience. Major Updates to the 4th Edition Fully compatible with recent SPSS releases up to and including version 20.0 Exciting new characters, including statistical cult leader Oditi, who provides students access to interesting and helpful video clips to illustrate statistical and SPSS concepts, and Confusious, who helps students clarify confusing quantitative terminology New discipline specific support matierlas have been added for Education, Sports Sciences, Psychology, Business & Management, and Health Sciences, making the book even more relevant to a wider range of subjects across the Social, Behavioral, and Health Sciences is taught to an interdisciplinary audience. An enhanced Companion Website (available late spring 2013) offers a wealth of material that can be used in conjunction with the textbook, including: PowerPoints Testbanks Answers to the Smart Alex tasks at the end of each chapter Datafiles for testing problems in SPSS Flashcards of key concepts Self-assessment multiple-choice questions Online videos of key statistical and SPSS procedures
ARCHITECTING MODELS OF TECHNICAL SYSTEMS FOR NON-ROUTINE SIMULATIONS

Complex physical behavior and contradictory requirements, such as performance and reliability, frequently have to be addressed by engineering design in a competitive environment where speed and agility are essential. Models are very important tools in performing such complex cognitive activities. In engineering of high performance artifacts, numerical modeling and simulation (i.e., experimenting with computer-based models) are increasingly important problem solving activities. The complex nature of engineering design and the time and cost constraints on the process require highly efficient and flexible procedures to configure models of technical systems for non-routine simulations. To support efficient configuration of complex models and to enable navigation in systems models, an architecting tool based on the model structure matrix (MSM) has been developed. The MSM is based on the design structure matrix concept. The advantages of a modular model architecture and the usability of the developed tool are illustrated in relation to an engineering modeling challenge.
Syntax and Semantics of the Clock Constraint Specification Language (CCSL)

The UML Profile for Modeling and Analysis of Real-Time and Embedded (MARTE) systems has recently been adopted by the OMG. Its Time Model extends the informal and simplistic Simple Time package proposed by UML2 and offers a broad range of capabilities required to model real-time systems. The MARTE OMG specification introduces a Time Structure inspired from Time models of the concurrency theory and proposes a new clock constraint specification language (CCSL) to specify, within the context of UML, logical and chronometric time constraints. This report specifies the syntax and a formal semantics of a subset of CCSL, called kernel CCSL. This semantics is to be the reference semantics of CCSL.
On the optimality of the shortest line discipline

We consider a many server queueing system with identical exponential servers. Customers arrive according to an arbitrary arrival process and upon arrival the customer must be assigned to some server's queue. We show that the shortest line discipline is the best decision rule when assigning the customers to the servers in the sense that the corresponding departure process is stochastically fastest.
Efficient Implementation of Bi-directional Path Tracer on GPU

Most of the implementations solving photo-realistic image rendering use standard unidirectional path tracing, having fast and accurate results for scenes without caustics or hard cases. These hard cases are usually solved by a bidirectional path tracing algorithm. However, due to the complexity of the bidirectional path tracing algorithms, its implementations almost exclusively target sequential CPUs. The following paper proposes a new parallel implementation of the bi-directional path tracing algorithm for the GPUs. Our approach is compared with existing solutions in terms of both performance and image quality. As the references we use the standard unidirectional GPU path tracer and commercial off-line bidirectional path tracers. We achieve interactive rendering rates for scenes of medium complexity.
De arbeidsmarkt naar opleiding en beroep tot 2006

In deze aanhoudend roerige tijden op de arbeidsmarkt brengt het Researchcentrumvoor Onderwijs en Arbeidsmarkt ROA de twaalfde uitgave van het rapport De arbeidsmarktnaar opleiding en beroep uit. Hierin wordt een beeld geschetst van de huidige enop de middellange termijn te verwachten arbeidsmarktontwikkelingen. Wij schetsende arbeidsmarktperspectieven voor de komende vijf jaar tot 2018 onder andere omde jongeren die nu voor hun studiekeuze staan inzicht te bieden in de verwachtearbeidsmarktsituatie na afstuderen. Deze informatie is van belang voor zowel hetgeven van voorlichting aan degenen die aan een vervolgopleiding willen beginnenals voor werkgevers bij het nemen van strategische beslissingen over hun personeelsbeleid.Het rapport is in het bijzonder bedoeld voor de beleidsontwikkeling van deoverheid, de arbeidsbemiddelingssorganisaties, de sociale partners en het onderwijsveld.Het rapport is gebaseerd op gedetailleerde arbeidsmarktinformatie naar sector,beroep en opleiding, zoals deze is opgenomen in het ArbeidsmarktinformatiesysteemAIS van het ROA.
Instruction-based self-testing of performance oriented faults in modern processors

Aggressive microprocessor design methodologies using gigahertz clock and very deep sub-micron technology are necessitating the use of at-speed testing of small and distributed timing defects caused by process variations during manufacturing. A primary objective of such tests is to test for the faults that can lead to performance degradation, such as delay faults. However, at-speed testing using external tester is not an economically viable scheme and hardware BIST leads to unacceptable performance loss and area overhead. A new paradigm, instruction-based self-testing, can alleviate these problems as it uses processor instructions to deliver the test patterns and collect the test responses. Also, it has the ability to link to low level fault models and it is well-suited methodology for testing processors, embedded processor cores, IP cores, and SoCs. Moreover, the same test can also be used for online periodic testing of processors to improve the reliability. This thesis proposes an instruction-based self-testing methodology for delay fault testing of modern processors in a chronological way by first dealing with non-pipelined processors, and then pipelined processors and superscalar processor architectures. In order to test a non-pipelined processor a graph theoretic model, called instruction execution graph based on the register transfer level description of the processor is developed. This model, in conjunction with the structural and ∗Doctoral Dissertation, Department of Information Processing, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-DD0261204, July 25, 2005.
An Embedded Interval Type-2 Neuro-Fuzzy Controller for Intelligent Navigation of Mobile Robot.

This paper describes intelligent navigation using an embedded interval type-2 neuro–fuzzy controller. Weightless neural network (WNNs) strategy is used because fast learning, easy hardware implementation and well suited to microcontroller-based-real-time systems. The WNNs utilizes previous sensor data and analyzes the situation of the current environment and classifies geometric feature such as U-shape, corridor and left or right corner. The behavior of mobile robot is implemented by means of interval type-2 fuzzy control rules can be generated directly from the WNNs classifier. This functionality is demonstrated on a mobile robot using modular platform and containing several microcontrollers implies the implementation of a robust architecture. The proposed architecture implemented using low cost range sensor and low cost microprocessor. The experiment results show, using that technique the source code is efficient. The mobile robot can recognize the current environment and to be able successfully avoid obstacle in real time and achieve smother motion compare than logic function and fuzzy type-1 controller.
Supplemental Instruction and the Enhancement of Student Performance in Economics Principles

This paper presents an empirical evaluation of Supplemental Instruction, a formal review/lab session program developed at the University of Missouri, designed to improve student learning in courses that have typically exhibited poor student performance. Using a two-equation model and student transcript data readily available to instructors and academic researchers, we evaluate the effectiveness of the program in economics principles. The analysis explicitly considers the confounding factor of self-selection in program participation. We find that ordinary least squares significantly underestimates the positive impact of Supplemental Instruction. The results suggest that formal programs designed to increase the intensity of instruction can have a demonstrable payoff in the form of increased student learning.
Improving file system consistency and durability with patches and bpfs

This dissertation improves the consistency and durability guarantees that file systems can efficiently provide, both by allowing each application to choose appropriate trade-offs between consistency and performance and by dramatically lowering the overheads of durability and consistency using new hardware and careful file system design. We first describe a new abstraction, the patch, which represents a write to persistent storage and its ordering requirements. This abstraction allows file system modules to specify ordering guarantees without simultaneously requesting more expensive, immediate durability. Algorithmic and data structure optimizations make this abstraction practical, and our patch-based file system implementation is performance-competitive with similarly-reliable Linux ext2 and ext3 configurations. To illustrate the benefits of patchgroups, an application-accessible version of patches, we apply them to improve the performance of the UW IMAP server by over 13 times and to make file handling in gzip robust to crashes. In the second part of this work we investigate using upcoming byte-addressable, persistent memory technologies—in particular, phase change memory—in place of disks and flash to reduce the costs of enforcing ordering constraints and providing durability and atomicity. We describe a new file system, BPFS, that commits each file system operation synchronously and atomically. BPFS exploits byte-addressability, improved throughput and latency, and our new atomic write primitive to eliminate copy-on-writes that have until now been required to implement shadow paging. Our evaluation shows that, because of our optimizations, BPFS provides its exceptionally stronger guarantees on phase change memory without lowering the throughput that today's file systems achieve on disks.
Motion Graphs

In this paper we present a novel method for creating realistic, controllable motion. Given a corpus of motion capture data, we automatically construct a directed graph called a motion graph that encapsulates connections among the database. The motion graph consists both of pieces of original motion and automatically generated transitions. Motion can be generated simply by building walks on the graph. We present a general framework for extracting particular graph walks that meet a user's specifications. We then show how this framework can be applied to the specific problem of generating different styles of locomotion along arbitrary paths.
Performance enhancement of Dissolved Gas Analysis using ANFIS

This paper presents a method for performance enhancement of Dissolved Gas Analysis through Adaptive Neuro - Fuzzy Inference System for fault detection and classification of incipient faults in power transformers. The proposed method is evaluated and results indicate that it is an effective tool for classification of incipient faults. The performance of the proposed method is compared with existing methods namely Rogers ratio method, artificial neural network method.
An Optimal Cache Placement Strategy Based on Content Popularity in Content Centric Network

In-network caching is one of the key aspects of Content Centric Network (CCN), while optimized content dissemination and efficient caching of information is one the most important aspects of cache strategy. In this paper, we proposed an Optimal Cache Placement strategy based on Content Popularity (OCPCP), in which developed a content popularity computing model by exploiting request records, to improve the caching gain and reduce the redundancy of the network caching. In addition, we propose an optimal content placement model and performance metrics to evaluate OCPCP performance. The experimental results indicate that the proposed algorithm achieve higher hit rate, lower delay and less load on the data servers.
The transactional HW/SW stack for fault tolerant embedded computing

Fault tolerance implementation in embedded systems is challenging because the physical constraints of area occupation, power dissipation, and energy consumption of these systems. The need for optimizing these three physical constraints while doing computation within the available performance goals and real-time deadlines creates a conundrum that is hard to solve. Classical fault tolerance solutions such as triple and dual modular redundancy are not feasible due to their high power overhead or lack of efficient and deterministic error recovery. Existing techniques, although some of them reduce the power and area overhead, incur heavy performance penalties and most of the time do not assume a feasible fault model. This dissertation introduces the Transactional HW/SW Stack, or simply Stack, to efficiently manage the area, power, fault coverage, and performance conundrum. The Stack introduces a new compilation strategy that assembles programs into Transactional Basic Blocks, together with a novel microprocessor, the TransactiOnal Basic Block Architecture (ToBBA), which provides fine-grained error detection and deterministic error rollback and elimination using the Transactional Basic Blocks (TBBs) both as a container for errors and as a small unit of data checkpointing. Two solutions to sustain the TBB semantics in hardware are introduced: softwareand hardware-based. Stack’s area, power, performance, and coverage were evaluated using ToBBA’s hardware implementation model. The Stack attains an error correction coverage of 99.35% with 2.05 power overhead within an area overhead of 2.65. The Stack also presents a performance overhead of 1.33 or 1.54, depending on the hardware model adopted to support the TBB.
Sparse Approximation and Dictionary Learning with Applications to Audio Signals

Over-complete transforms have recently become the focus of a wide wealth of research in signal processing, machine learning, statistics and related fields. Their great modelling flexibility allows to find sparse representations and approximations of data that in turn prove to be very efficient in a wide range of applications. Sparse models express signals as linear combinations of a few basis functions called atoms taken from a so-called dictionary. Finding the optimal dictionary from a set of training signals of a given class is the objective of dictionary learning and the main focus of this thesis. The experimental evidence presented here focuses on the processing of audio signals, and the role of sparse algorithms in audio applications is accordingly highlighted. The first main contribution of this thesis is the development of a pitch-synchronous transform where the frame-by-frame analysis of audio data is adapted so that each frame analysing periodic signals contains an integer number of periods. This algorithm presents a technique for adapting transform parameters to the audio signal to be analysed, it is shown to improve the sparsity of the representation if compared to a non pitchsynchronous approach and further evaluated in the context of source separation by binary masking. A second main contribution is the development of a novel model and relative algorithm for dictionary learning of convolved signals, where the observed variables are sparsely approximated by the atoms contained in a convolved dictionary. An algorithm is devised to learn the impulse response applied to the dictionary and experimental results on synthetic data show the superior approximation performance of the proposed method compared to a state-of-the-art dictionary learning algorithm. Finally, a third main contribution is the development of methods for learning dictionaries that are both well adapted to a training set of data and mutually incoherent. Two novel algorithms namely the incoherent k-svd and the iterative projections and rotations (ipr) algorithm are introduced and compared to different techniques published in the literature in a sparse approximation context. The ipr algorithm in particular is shown to outperform the benchmark techniques in learning very incoherent dictionaries while maintaining a good signal-to-noise ratio of the representation. Submitted for the degree of Doctor of Philosophy Queen Mary, University of London 2013
Energy Consumption in Cloud Computing Data Centers

The implementation of cloud computing has attracted computing as a utility and enables penetrative applications from scientific, consumer and business domains. However, this implementation faces tremendous energy consumption, carbon dioxide emission and associated costs concerns. With energy consumption becoming key issue for the operation and maintenance of cloud datacenters, cloud computing providers are becoming profoundly concerned. In this paper, we present formulations and solutions for Green Cloud Environments (GCE) to minimize its environmental impact and energy consumption under new models by considering static and dynamic portions of cloud components. Our proposed methodology captures cloud computing data centers and presents a generic model for them. To implement this objective, an in-depth knowledge of energy consumption patterns in cloud environment is necessary. We investigate energy consumption patterns and show that by applying suitable optimization policies directed through our energy consumption models, it is possible to save 20% of energy consumption in cloud data centers. Our research results can be integrated into cloud computing systems to monitor energy consumption and support static and dynamic system level-optimization.
Model and performance evaluation of a piconet for point-to-multipoint communications in Bluetooth

We consider model and performance evaluation of the Bluetooth system supporting point-to-multipoint communications in a piconet. In order to support more than seven slave devices in a piconet, a park mode is considered and modeled. We evaluate the performance, i.e., throughput and delay, using simulations when multi-connections (both A CL and SCO connections) are present in a piconet. We show that the data rate may be less than 10 kbps when SCO connection(s) and more than six ACL connections are jointly supported in a piconet. In addition, if up to four ACL connections are supported, the average delay is shown to be maintained less than 10 msec. Our results serve as a guideline to the design of Bluetooth systems with throughput and delay requirements.
Robust feature selection for large scale image retrieval

This paper addresses the problem of recognizing specific objects in ver y large datasets. A common approach has been based on the bag-of-words ( BOW) method, in which local image features are clustered into visual words, providing memo ry savings through feature quantization. In this paper we take an additional s tep to reducing memory requirements by selecting only a small subset of the training features to use for recognition. This approach, which we name Robust F eature Selection ( RFS), is based on the observation that many local features are unreliable or represent irrelevant clutter. We are able to select “maximally robust” fe a ur s by an unsupervised preprocessing step that identifies correctly matching features among the training images. We demonstrate that this selection approach allows an average of 4% of the original features per image to provide matching perfo rmance that is as accurate as the full set in the Oxford Buildings dataset. In addition , we employ a graph to represent the matching relationships between images. Doing so enables us to effectively augment the feature set for each image by mer ging them with maximally robust features from neighbouring images. We demonstrate adjacent and 2-adjacent augmentation, both of which give a substantial b oost in recognition performance.
Wikipedia based Query Expansion for Searching in Norwegian

With the rapid increase of information available on the Internet, searching is becoming a daily operation. This thesis address the issue that arises when there is a difference between what the users are typing in the search engine, and what they actually are interested in. The method presented in this thesis is a Query expansion method using Wikipedia as a knowledge-base, with the goal of reducing the gap between what the users are formulating, and the actual information need behind the search. The method is based on analysis of hyperlinks between articles in Wikipedia. The research contribution of this thesis is a new method for query expansion designed for Norwegian language. The proposed method uses the knowledge-base to suggest words for the users that are relevant to the original query. The evaluation of the new method shows a marginal increase in quality, but highlights the need for a search engine designed for Norwegian language. The thesis concludes that a search option for Norwegian language will produce better results, and the system for suggestion of words might help users move from a broad search strategy to a more precise search strategy.