Miracle-dz/UniFAR-FTUs huggingface dataset:
Using topological information in opportunistic network coding

Recent advances in methods to increase network utilization have lead to the introduction of a relatively new method called Network Coding. Network Coding is a method that can reduce local congestion in a network by combining information sent over the network. It is commonly researched in the information theory field after it was first introduced by Ahlswede et al in 2000. Network Coding was proven in 2003, by Koetter & Medard to be the only way to achieve the throughput capacity defined by the Min cut Max flow theorem of Shannon. It was applied deterministically in wired networks and randomly in wireless networks. Random Network Coding however requires a lot of overhead and may cause possible delays in the network. We found that there is an open question as to determine where in a wireless network, Network Coding can be implemented. In this thesis we propose to find opportunities for the implementation of Network Coding, by searching for known deterministic Network Coding topologies in larger Networks. Because a known topology is used, we will then also know how Network Coding should be implemented. This method of finding opportunities for the implementation of Network Coding using topology can be combined with a routing algorithm to improve the utilization of a wireless network. We implemented our method on three different topologies and searched 1000 random networks for the presence of these topologies. We found that these topologies occurred frequently enough to make our method a viable method of finding opportunities for the implementation of Network Coding.
EVALUATION OF HIERARCHICAL PATH FINDING TECHNIQUES FOR ITS ROUTE GUIDANCE

Efficient path finding necessary for route guidance has been identified as one of the key requirements for Intelligent Transportation System (ITS) applications. Precomputing all-pair shortest paths for an ITS network would make retrieval of paths extremely efficient allowing a high throughput of path finding requests, though at the cost of precomputed path maintenance and storage. To overcome these costs, which grow quadratically with the size of the ITS networks, we propose the utilization of a hierarchical structure for encoding paths. The main contributions of this paper are two fold. First, an extensive experimental evaluation of our proposed hierarchical path structure, determining both its benefits and limitations as well as contrasting it with alternative techniques currently employed for ITS route guidance is conducted and analyzed. Secondly, we apply our hierarchical path structure model to the Detroit city and vicinity road maps that are very large in size (30,000 nodes) for which the application of path precomputation techniques was previously considered impractical. Our experiments demonstrate that the maintenance costs of the path structure are dramatically reduced in our hierarchical model. We also demonstrate that 3-level hierarchical path structures have superior maintenance costs over 2-level path structures for large maps, with only a negligible performance penalty for path retrieval. Our hierarchial path structure is more efficient in path retrieval than alternative path finding algorithms, such as A and Hierarchical A. Therefore, our approach offers a feasible solution to path finding for centralized ITS route guidance.
A critical examination of deep learningapproaches to automated speech recognition

Recently, deep learning techniques have been successfully applied to automatic speech recognition (ASR) tasks. Most current speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) are exploited to model the emission probability of the HMM. Deep Neural Networks (DNNs) and Deep Belief Networks(DBNs) have recently proven though to outperform GMMs in modeling the probability of emission in HMMs. Deep architectures such as DBNs with many hidden layers are useful for multilevel feature representation thus building a distributed representation at different levels of a certain input. These networks are first pre-trained as a multi-layer generative model of a window of feature vector without making use of any discriminative information in unsupervised mode. Once the generative pre-training is complete, discriminative fine-tuning is performed to adjust the model parameters to make them better at predicting. Our aim is to study different levels of representation for speech acoustic features that are produced by the hidden layers of DBNs. To this end, we estimate phoneme recognition error and use classification accuracy evaluated with Support Vector Machines (SVMs) as a measure of separability between the DBN representations of 61 phoneme classes. In addition, we investigate the relation between different subgroups/categories of phonemes at various representation levels using correlation analysis. The tests have been performed on TIMIT database and simulations have been developed to run on a graphics processing unit (GPU) cluster at PDC/KTH.
Glass Garden: Chihuly Blown Glass Translated as Wearable Art

Dale Chihuly’s sculptures rely on overlapping transparent glass shapes in a wide variety of colors, and the effect of light in the sculptures is a crucial component of the design. The purpose of this dress is to translate a Chihuly glass sculpture into a unified composition using the principle of the circular shapes of Chihuly’s sculptures and capturing the colors and transparency of the original art, incorporating 1950s historic inspiration reminiscent of the shapes present in the glass. This work explores the visual similarity between Chihuly’s bowl-shaped sculptures and the full skirts of 1950s dresses. Iridescent silk organza yoyos applied to the garment mimic the layered, transparent effects of Chihuly’s sculptures, adding a three-dimensional element to the two-dimensional fabric design. In this garment, digital technology is combined with traditional dressmaking techniques to integrate glass art, textile design, and historic inspiration into a contemporary design.
Using JRuby: Bringing Ruby to Java

In Using JRuby you'll venture into the wide world of open-source Ruby and Java libraries. Write Ruby on Rails web applications that run on Java servers like Tomcat. Use Java's JDBC or Hibernate to easily connect Ruby to industry-standard databases. Test your Java program using Ruby's elegant Cucumber and RSpec frameworks. Create dazzling desktop user interfaces with frameworks like Limelight and Monkeybars. Package a Rails or plain Ruby project for easy deployment to any Java environment. JRuby lets you merge the best of several possible worlds, so you can create unique software using the best tools available. This book is your definitive guide.
An integrated support system for design of distributed databases

A methodology for designing distributed data bases is presented. The design process, from the data base administrator's point of view, consists of forming and placing data fragments across the geographically dispersed locations. It is recognized that the number of unique ways in which data can be partitioned and allocated across sites is innumerable. Also, related issued such as query processing and concurrency overheads have a bearing on the evaluation of a candidate partitioning and allocation schema and hence have to be made part of the design process. Since the complexity of the integrated problem makes the problem ill-structured, a tool in the form of a Decision Support System is needed to solve the problem. The methodology presented for the design process incorporates such a tool. The design process is viewed as a decision-making process on the part of the designer who is supported by an interactive design support system to find an acceptable partitioning and allocation choice. The support system uses the semantic knowledge about the data base and the human judgement of the designer to generate a restricted number of meaningful candidate alternatives which are evaluated by a query processing algorithm. A few, more promising, alternatives are evaluated in detail using a simulation model. A new query processing algorithm which maximizes parallelism in processing is presented. A simulation model incorporating a 2-phase locking concurrency control mechanism is developed. The design system is implemented and tested using a real-life case study.
Highly Optimized Toleranceによるセンサ集団の最適配置構造の設計

A new design algorithm based on highly optimized tolerance, recently introduced by Carlson and Doyle, is applied to a toy problem of determining the optimal arrangement of hypothetical biosensors in an airport terminal for risk aversion to epidemic outbreak. The hallmark of robust design for the arrangement of sensors is obtained, providing a power law in the probability of occurrence of epidemic outbreak as a function of the number of persons infected with pathogen.
Microsoft Robotics Developer Studio入門

イントロダクション システムの準備 ＲＤＳとロボットの接続 ＶＰＬのプログラミング作法 センサー・ブロック モーター・ブロック 拡張ブロック シミュレータ サービス メッセージとハンドラ 通知 フォーム ロボットの制御 コントローラによる遠隔制御 音声認識によるロボットの制御
Dynamic Gesture Recognition Based on Fuzzy Neural Network Classifier

This paper presents a dynamic gesture recognition method based on the combination of the fuzzy features of the dynamic gesture track changes and the fuzzy neural network inference system. This method first classified the dynamic gestures roughly into circular gestures and linear gestures. Further, gestures were classified narrowly into up, down, left, right, clockwise, and counter-clockwise gestures. These six dynamic gestures, which are commonly used in IP-TV controlling, were introduced as the recognition goal in our dynamic gesture recognition system. The results show that this method has a good recognition performance and fault tolerance, and more applicable to real gesture-controlled human-computer interactive environment.
Nonlinear Control of Dynamic Networks

Significant progress has been made on nonlinear control systems in the past two decades. However, many of the existing nonlinear control methods cannot be readily used to cope with communication and networking issues without nontrivial modifications. For example, small quantization errors may cause the performance of a "well-designed" nonlinear control system to deteriorate. Motivated by the need for new tools to solve complex problems resulting from smart power grids, biological processes, distributed computing networks, transportation networks, robotic systems, and other cutting-edge control applications, Nonlinear Control of Dynamic Networks tackles newly arising theoretical and real-world challenges for stability analysis and control design, including nonlinearity, dimensionality, uncertainty, and information constraints as well as behaviors stemming from quantization, data-sampling, and impulses. Delivering a systematic review of the nonlinear small-gain theorems, the text: Supplies novel cyclic-small-gain theorems for large-scale nonlinear dynamic networks Offers a cyclic-small-gain framework for nonlinear control with static or dynamic quantization Contains a combination of cyclic-small-gain and set-valued map designs for robust control of nonlinear uncertain systems subject to sensor noise Presents a cyclic-small-gain result in directed graphs and distributed control of nonlinear multi-agent systems with fixed or dynamically changing topology Based on the authors recent research, Nonlinear Control of Dynamic Networks provides a unified framework for robust, quantized, and distributed control under information constraints. Suggesting avenues for further exploration, the book encourages readers to take into consideration more communication and networking issues in control designs to better handle the arising challenges.
The Official GameSalad Guide to Game Development

PART I. 1. Gameplay. 2. Interface Design. 3. Prototyping. PART II. 4. Windows(R) Game Development. 5. Mac(R) Game Development. 6. Mobile Game Development (iOS/Android(TM)). 7. Online Game Development (HTML 5). PART III. 8. Social Game Development. 9. Serious Game Development.
KTHFS – A HIGHLY AVAILABLE ANDSCALABLE FILE SYSTEM

KTHFS is a highly available and scalable file system built from the version 0.24 of the Hadoop Distributed File system. It provides a platform to overcome the limitations of existing distributed file systems. These limitations include scalability of metadata server in terms of memory usage, throughput and its availability.This document describes KTHFS architecture and how it addresses these problems by providing a well coordinated distributed stateless metadata server (or in our case, Namenode) architecture. This is backed with the help of a persistence layer such as NDB cluster. Its primary focus is towards High Availability of the Namenode.It achieves scalability and recovery by persisting the metadata to an NDB cluster. All namenodes are connected to this NDB cluster and hence are aware of the state of the file system at any point in time.In terms of High Availability, KTHFS provides Multi-Namenode architecture. Since these namenodes are stateless and have a consistent view of the metadata, clients can issue requests on any of the namenodes. Hence, if one of these servers goes down, clients can retry its operation on the next available namenode.We next discuss the evaluation of KTHFS in terms of its metadata capacity for medium and large size clusters, throughput and high availability of the Namenode and an analysis of the underlying NDBcluster.Finally, we conclude this document with a few words on the ongoing and future work in KTHFS.
Modelling and analysis of wireless MAC protocols with applications to vehicular networks

The popularity of the wireless networks is so great that we will soon reach the point where most of the devices work based on that, but new challenges in wireless channel access will be created with these increasingly widespread wireless communications. Multi-channel CSMA protocols have been designed to enhance the throughput of the next generation wireless networks compared to single-channel protocols. However, their performance analysis still needs careful considerations. In this thesis, a set of techniques are proposed to model and analyse the CSMA protocols in terms of channel sensing and channel access. In that respect, the performance analysis of un-slotted multi-channel CSMA protocols is studied through considering the hidden terminals. In the modelling phase, important parameters such as shadowing and path loss impairments are being considered. Following that, due to the high importance of spectrum sensing in CSMA protocols, the Double-Threshold Energy Detector (DTED) is thoroughly investigated in this thesis. An iterative algorithm is also proposed to determine optimum values of detection parameters in a sensing-throughput problem formulation. Vehicle-to-Roadside (V2R) communication, as a part of Intelligent Transportation System (ITS), over multi-channel wireless networks is also modelled and analysed in this thesis. In this respect, through proposing a novel mathematical model, the connectivity level which an arbitrary vehicle experiences during its packet transmission with a RSU is also investigated.
EFFECT OF HEAVY VEHICLES AT AUSTRALIAN TRAFFIC CIRCLES AND UNSIGNALIZED INTERSECTIONS

The technique for estimating the capacity of traffic circles in Australia has employed a gap-acceptance approach. For this approach the influence of heavy vehicles was essentially ignored because the sites were typical and had about 5% heavy vehicles. The parameters used to quantify the gap-acceptance behavior of drivers had included the behavior of the heavy-vehicle drivers. The use of this technique has now been extended beyond a simple analytical process and is incorporated into the SIDRA analysis program. The consequences of including a high proportion of heavy vehicles needs to be estimated. The passenger car unit (pcu) technique for estimating the influence of heavy vehicles and another technique using three factors to modify the gap-acceptance parameters for drivers of heavy vehicles and drivers accepting a gap just ahead of a heavy vehicle are discussed. In both cases an analytical assessment was done. Values for these terms, which modify the gap-acceptance parameters, are evaluated from field studies and are reported. It was concluded that the pcu method is reasonable but that the modified gap acceptance approach provides greater understanding and flexibility. The values found will assist others in evaluating the influence of heavy vehicles at traffic circles and unsignalized intersections.
Information, Activity and Social Order in Distributed Work: The Case of Distributed Software Problem Management

Results from a study of a global Free/Open Source Software (F/OSS) development organization show how distributed information practices contribute to the management of software problems. The study addressed these questions: (1) How do F/OSS development communities manage software problems? (2) What varieties of information and activity occur as part of software problem management? And (3) How are information, activity, and social order related in software problem management? A systematic random sample of 385 bug reports was drawn from a collection more than 182,000 bug reports held in this community's bug report repository, with additional bug reports selected from the repository using theoretical sampling. The bug reports were treated as texts; content analytic and grounded theory procedures were used to develop categories of phenomena and new and revised theoretical constructs. The analysis of the data was informed by previous research into human information behavior, negotiation, collaboration, social and information contexts, software engineering, and sensemaking to provide insights into how this large, distributed organization utilizes information to produce successful, high-quality software. Contributions from this project include the first detailed, empirically grounded description of the information practices used by a distributed F/OSS development organization to manage software problems, including the first description of a fundamental information object, the bug report network; clearly drawn distinctions between the information management work necessary to support the community's software problem management goals and the work that directly resolves software problems; identification of the processes and social arrangements that enable this community's distributed collective software problem management practices; explication of software problem management as a distributed, communal sensemaking enterprise; description of the layers of context and nested processes necessary to support distributed, information-intensive work; and the description of a new class of human information behavior, distributed collective information practices. Throughout, the ways in which information, activity, social order, context, and process are mutually constitutive are identified and explained. Bug report networks are used as a primary example of how activity leads to new forms of information, re-orders existing information, affects social order, and contributes to the community's efforts to bring problematic situations to closure.
Educator Perspectives on Earth System Science Literacy: Challenges and Priorities

ABSTRACT The challenges and priorities of defining and achieving Earth System Science (ESS) literacy are examined through surveys of geoscience educators attending a professional geological meeting. Two surveys with Likert-style and free-response questions were distributed to geoscientists and K–12 teachers to elicit what instructors think are important concepts, experiences, and hurdles to ESS literacy. Survey 1 asked participants open-ended questions about the challenges and priorities of ESS literacy. Survey 2 asked participants to evaluate the importance of various concepts for nonscience majors taking an ESS course. Survey 1 results indicate that the geoscience professors and K–12 teachers place emphasis on the relevance of Earth Science for public decision-making and regard formal education as having an important role in building Earth Science literacy. Respondents identified weaknesses of K–12 ESS education and the lack of respect for the geosciences as substantial hurdles for ESS literacy. Survey 2 results reveal that respondents highly value integrated Earth Systems concepts, such as, Earth systems involve complex interactions between rock, water, air, and life. Less value is placed on fact-like statements that cover a narrow range of content, such as the age of the Earth and that Earth is mostly covered by an ocean. Results from both surveys indicate that K–12 teachers value teaching the interconnectedness of humanity and the Earth more so than do professors. This study identifies geoscience educators' perspectives of Earth Systems Science Principles and reveals the need for a more cohesive movement to promote the importance of ESS and develop ESS literacy in the general public.
A Modified Error In Constitutive Equation Approach For Viscoelasticity Imaging With Interior Data

This chapter presents a method for the identification of linearly viscoelastic material parameters in the context of steady-state dynamics using interior data. In this method, the inverse problem of viscoelasticity imaging is solved by minimizing an objective which includes the modified error in constitutive equation (MECE) functional and is subject to the conservation of linear momentum without the need to introduce any knowledge of external excitations or boundary conditions. The MECE functional measures the discrepancy in the constitutive equations that connect kinematically admissible strains and dynamically admissible stresses; in addition to this, it also incorporates the measurement data in a quadratic penalty term. Regularization is achieved through a penalty parameter in combination with the discrepancy principle due to Morozov. Numerical results demonstrate the robust performance of the method in situations where the available measurement data is incomplete and corrupted by noise of varying levels.
Automatic Lung CT Classification using LogitBoost Algorithm Optimized by PSO

Automated systems based on machine learning present more accurate diagnosis and easy analysis of medical images. In real world applications especially in medical applications we are usually faced with the problem of imbalanced data sets. In lung CT nodule detection systems, nodules are a part of minority class and other non-nodule organs are a member of the majority class. So to detect nodules without ignoringthem we should improve the predictive accuracies over the minority class. Conventional machine learning classifiers have high predictive accuracy over the majority class and less predictive accuracy over the class with minor members. Ensemble of classifiers is one of the most important solutions.In this work researchers used the LogitBoost ensemble classifier to diagnosis nodules from non-nodule objects. LogitBoost optimized the AdaBoostM1 by minimizing the binomial deviance which causes incorrect classification over the minority observations. LogitBoost can give better average accuracy than AdaBoostM1 for data with poorly separable classes. One of the problems in ensemble classifiers is estimating the cost parameters. In this paper Particle Swarm Optimization (PSO) algorithm was used to optimize these parameters. The extracted features from lung nodules were applied to the proposed optimized LogitBoost algorithm as its predictor variables. Then the data set was randomly divided into two sets: 80% as the training set and the remaining 20% as testing set. Simulation results revealedthat the proposed algorithm could detect nodules with high performance of AUC=86%.