Miracle-dz/UniFAR-FTUs huggingface dataset:
Txtng: the Gr8 Db8

List of Cartoons 1. The Hype About Texting 2. How Weird is Texting? 3. What is Distinctive About it? 4. Why do They do it? 5. Who Texts? 6. What do They Text About? 7. How do Other Languages do it? 8. Why all the Fuss? Glossary Appendix Index
Top-Down Network Design

A systems analysis approach to enterprise network design Master techniques for checking the health of an existing network to develop a baseline for measuring performance of a new network design Explore solutions for meeting QoS requirements, including ATM traffic management, IETF controlled-load and guaranteed services, IP multicast, and advanced switching, queuing, and routing algorithms Develop network designs that provide the high bandwidth and low delay required for real-time applications such as multimedia, distance learning, and videoconferencing Identify the advantages and disadvantages of various switching and routing protocols, including transparent bridging, Inter-Switch Link (ISL), IEEE 802.1Q, IGRP, EIGRP, OSPF, and BGP4 Effectively incorporate new technologies into enterprise network designs, including VPNs, wireless networking, and IP Telephony Top-Down Network Design, Second Edition, is a practical and comprehensive guide to designing enterprise networks that are reliable, secure, and manageable. Using illustrations and real-world examples, it teaches a systematic method for network design that can be applied to campus LANs, remote-access networks, WAN links, and large-scale internetworks. You will learn to analyze business and technical requirements, examine traffic flow and QoS requirements, and select protocols and technologies based on performance goals. You will also develop an understanding of network performance factors such as network utilization, throughput, accuracy, efficiency, delay, and jitter. Several charts and job aids will help you apply a top-down approach to network design. This Second Edition has been revised to include new and updated material on wireless networks, virtual private networks (VPNs), network security, network redundancy, modularity in network designs, dynamic addressing for IPv4 and IPv6, new network design and management tools, Ethernet scalability options (including 10-Gbps Ethernet, Metro Ethernet, and Long-Reach Ethernet), and networks that carry voice and data traffic. Top-Down Network Design, Second Edition, has a companion website at http://www.topdownbook. com, which includes updates to the book, links to white papers, and supplemental information about design resources. This book is part of the Networking Technology Series from Cisco Press which offers networking professionals valuable information for constructing efficient networks, understanding new technologies, and building successful careers.
Extracting Core Events Based on Timeline and Retweet Analysis in Twitter Corpus

Many internet users attempt to focus on the issues which have posted on social network services in a very short time. When some social big issue or event occurred, it will affect the number of comments and retweet on that day in twitter. In this paper, we propose the method of extracting core events based on timeline analysis, sentiment feature and retweet information in twitter data. To validate our method, we have compared the methods using only the frequency of words, word frequency with sentiment analysis, using only chi-square method and using sentiment analysis with chi-square method. For justification of the proposed approach, we have evaluated accuracy of correct answers in top 10 results. The proposed method achieved 94.9% performance. The experimental results show that the proposed method is effective for extracting core events in twitter corpus.
Rules and principles in cognitive diagnoses

Author(s): Langley, Pat; Wogulis, James; Ohlsson, Stellan | Abstract: Cognitive simulation is concerned with constructing process models of human cognitive behavior. Our work on the ACM system (Automated Cognitive Modeler) is an attempt to automate this process. The basic assumption is that all goal-oriented cognitive behavior involves search through some problem space. Within this framework, the task of cognitive diagnosis is to identify the problem space in which the subject is operating, identify solution paths used by the subject, and find conditions on the operators that explain those solution paths and that predict the subject's behavior on new problems. The work presented in this paper uses techniques from machine learning to automate the tasks of finding solution paths and operator conditions. We apply this method to the domain of multi-column subtraction and present results that demonstrate ACM's ability to model incorrect subtraction strategies. Finally, we discuss the difference between procedural bugs and misconceptions, proposing that errors due to misconceptions can be viewed as violations of principles for the task domain.
Fast Fuzzy Search for Mixed Data Using Locality Sensitive Hashing

The drastic increase in data volume strongly demands efficient search techniques for similar data to queries. It is sometimes useful to specify data of interest with fuzzy constraints. When data objects contain both numerical and categorical attributes, it is usually not easy to define commonly-accepted distance measures between data objects. With no efficient indexing structure, it costs much to search for specific data objects because a linear search needs to be conducted over the whole data set. This paper proposes a method to use locality sensitive hashing technique and fuzzy constrained queries to search for interesting ones from big data. The method builds up a locality sensitive hashing-based indexing structure only with constituting continuous attributes, collects a small number of candidate data objects to which query is examined, and then evaluates their satisfaction degree to fuzzy constrained query so that data objects satisfying the query are determined.
A Commuting Diagram Relating Threaded and Non-threaded JVM Models

We establish a commuting diagram that relates two models of the Java Virtual Machine (JVM). The first model, M3, supports much of Java, including classes, objects, and dynamic method resolution. The second model, M4, builds upon M3 by adding threads, monitors, and synchronized methods. We describe a theorem, Main, that asserts that running certain "single-threaded" states on M4 is equivalent to transforming those states to the domain of M3, running the transformed state there, and translating the result back to the domain of M4. We define the criteria we use to determine if the resulting states are equivalent, and we define our notion of "single-threaded". We then discuss a few lessons learned during the development of Main.
A New Cooperative Spectrum Sensing in Fading Channels

The proposed protocol is to get better performance compared to the existing method, also it can save the node number. There is needed a lot of secondary user for cooperative spectrum detection in Rayleigh fading channel. In this proposal, we suggest the new cooperative spectrum detector which can choose one secondary user with high power, while other SUs can be used as relays.
Optimal dynamic range integrators

The design of optimal dynamic range integrators that are meant as building blocks for optimal dynamic range analog continuous-time filters is discussed. A fundamental limit for the dynamic range of an integrator is given. This limit is a function of the supply voltage and the available amount of capacitance. It is shown how integrators are to be designed if this limit is to be reached. Different conventional integrator realizations are evaluated and optimized so that they can be compared to the fundamental limit. A design example of a filter with integrators that approach this limit is given. >
Connectionist learning for control

THIS REPORT IS AN INTRODUCTORY OVERVIEW OF LEARNING BY CONNECTIONIST NETWORKS, ALSO CALLED ARTIFICIAL NEURAL NETWORKS, WITH A FOCUS ON THE IDEAS AND METHODS MOST RELEVANT TO THE CONTROL OF DYNAMICAL SYSTEMS. IT IS INTENDED BOTH TO PROVIDE AN OVERVIEW OF CONNECTIONIST IDEAS FOR CONTROL THEORISTS AND TO PROVIDE CONNECTIONIST RESEARCHERS WITH AN INTRODUCTION TO CERTAIN ISSUES IN CONTROL. THE PERSPECTIVE TAKEN EMPHASIZES THE CONTINUITY OF THE CURRENT CONNECTIONIST RESEARCH WITH MORE TRADITIONAL RESEARCH IN CONTROL, SIGNAL PROCESSING, AND PATTERN CLASSIFICATION. CONTROL THEORY IS A WELL-DEVELOPED FIELD WITH A LARGE LITERATURE, AND MANY OF THE LEARNING METHODS BEING DESCRIBED BY CONNECTIONISTS ARE CLOSELY RELATED TO METHODS THAT ALREADY HAVE BEEN INTENSIVELY STUDIED BY ADAPTIVE CONTROL THEORISTS. ON THE OTHER HAND, THE DIRECTIONS THAT CONNECTIONISTS ARE TAKING THESE METHODS HAVE CHARACTERISTICS THAT ARE ABSENT IN THE TRADITIONAL ENGINEERING APPROACHES. THIS REPORT DESCRIBES THESE CHARACTERISTICS AND DISCUSSES THEIR POSITIVE AND NEGATIVE ASPECTS. IT IS ARGUED THAT CONNECTIONISTS APPROACHES TO CONTROL ARE SPECIAL CASES OF MEMORY--INTENTIVE APPROACHES, PROVIDED A SUFFICIENTLY GENERALIZED VIEW OF MEMORY IS ADOPTED. BECAUSE ADAPTIVE CONNECTIONIST NETWORKS CAN COVER THE RANGE BETWEEN STRUCTURELESS LOOKUP TABLES AND HIGHLY CONSTRAINED MODEL-BASED PARAMETER ESTIMATION, THEY SEEM WELL-SUITED FOR THE ACQUISITION AND STORAGE OF CONTROL INFORMATION. AD
Control of manipulator/vehicle system for man-robot cooperation based on human intention

Proposes a control method for the manipulator/vehicle system which has redundant degrees of freedom. In order to control the system, the authors decompose the motion of the end effector in the inertial coordinate system into the vehicle's motion and the manipulator's motion because the motion of the end effector with respect to the inertial coordinate system is realized by both the manipulator's motion and the vehicle's motion. How to decompose the end effector's motion is one of the key issues for the manipulator/vehicle system. If one wants to move the system to another place, the motion of the end effector should be realized by the vehicle's motion. On the other hand, if one wants to manipulate an object, the motion of the end effector should be done by the manipulator's motion. That is, the decomposition of the end effector's motion in the inertial coordinate system should be done based on the human intention. The method proposed controls the motion of the manipulator and the motion of the vehicle based on the operator's intention modelled by fuzzy rules. The experimental results illustrates the effectiveness of the proposed control method for the manipulator/vehicle system for man-robot cooperation. <<ETX>>
Changes in the Approaches of Teachers Following Computerization of Schools

This article examines the change in teachers' attitudes and instruction following computerization of their schools. Parameters chosen to assure the success of the study were: teachers' training courses, establishment of teachers' teams, teachers' expectations and willingness, and teachers' use of the computer in the school. The study took place in Israeli schools using the following tools: interviews, questionnaires, observations, case studies, and focus groups. The groups examined were teachers and students. The results showed a significant change in the teachers' attitude and instruction following computerization. Those who took training courses on computers, used computer tools as aids, and made greater use of software, had a greater willingness to employ the computer. Yet the teachers were not yet open to changing their methods by using the computer as a teaching tool and still preferred traditional frontal teaching. They stated that they did not have expectations for changing the learning process significantly as a result of computerization.
Learning approximate diagnosis

In earlier work on incorporating explanation-based learning (EBL) in model-based diagnosis (MBD), a diagnostic architecture integrating EBL and MBD components was suggested. The authors relax the requirement on completeness and specificity of the diagnostic candidates. They allow the learning component to make errors in a training phase where it is given feedback on its actual performance. A method is described for trading off accuracy for efficiency. In this approach, most diagnosis problems are handled by the associational rules learned from previous problems. Model-based reasoning and learning are activated only when performance drops below a given threshold. Empirical results are presented on circuits with an increasing number of components illustrating how this approach scales up. <<ETX>>
A human-machine cooperation route planning method based on improved A* algorithm

To avoid the limitation of common route planning method to blindly pursue higher Machine Intelligence and autoimmunization, this paper presents a human-machine cooperation route planning method. The proposed method includes a new A* path searing strategy based on dynamic heuristic searching and a human cooperated decision strategy to prune searching area. It can overcome the shortage of A* algorithm to fall into a local long term searching. Experiments showed that this method can quickly plan a feasible route to meet the macro-policy thinking.
Analysis of circular cylindrical dielectric rod leaky wave antenna with periodic strips and TE circularly symmetric wave

A leaky wave circular dielectric rod antenna with periodic transverse conducting strips and an TE propagating wave is analyzed using a Galerkin solution in the Fourier domain. The effect of the frequency on the angle of the main beam and the attenuation constant is investigated in a wide band. The effects of the structural parameters; the dielectric constant, the rod radius, the strip width and the period length are considered. The use of double strips in each unit cell is examined to enhance radiation in the broadside direction.
Pedagogic Approaches to Using Technology for Learning Literature Review

This literature review is intended to address and support teaching qualifications and CPD through identifying new and emerging pedagogies; "determining what constitutes effective use of technology in teaching and learning; looking at new developments in teacher training qualifications to ensure that they are at the cutting edge of learning theory and classroom practice and making suggestions as to how teachers can continually update their skills. " - Page 4.
D-RAMA: a new deterministic MAC protocol for wireless multimedia communications

A new deterministic packet access method, name D-RAMA, suitable for operation in a wireless multiservice environment is presented. It is shown that the method has excellent fairness properties, while yielding a very low packet loss probability and achieving a statistical multiplexing gain of 2, which is roughly 30% better than the capacity provided by PRMA under equivalent conditions.
An efficient 3D object management and interactive walkthrough for the 3D facility management system

An efficient 3D facility management system is developed based on the spatial data structure(MD-tree). In the system, 3D objects in a city are semi-automatically generated from 2D-maps by using appropriate rules. The entire 3D space is sliced into multiple layers depending on the height. The objects that belong to each layer are managed by corresponding 2D MD-tree. Potentially visible objects are obtained efficiently by the data management method based on the multiple MD-trees. The objects close to the viewpoint are searched in the MD-tree that corresponds to the lower layer. Far objects are, on the other hand, searched in the higher layer. The search operation is performed very rapidly by MD-tree. Furthermore, the rendering complexity of each object is adaptively controlled with respect to the the distance from the viewpoint so that the scene complexity is kept bounded. With these techniques, desirable frame update rates for the interactive walkthrough (around 10 frames/sec by SGI CRIMSON/Reality Engine) is realized. <<ETX>>
Facilitating Technology-Enhanced Problem-based Learning (PBL) in the Middle School Classroom: An Examination of How and Why Teachers Adapt

Despite the challenges inherent in adopting problem-based learning (PBL) in the middle school classroom, successful PBL teachers are able to effectively address these challenges. In this exploratory study we examined the perceptions and practices of five successful middle school PBL teachers regarding the specific challenges posed by PBL and the strategies they used to address them. Results suggest that while successful PBL teachers faced multiple challenges when implementing PBL, they created and adapted effective strategies to successfully mitigate the challenges. Planning approaches included adapting previous units, using a backwardsplanning process, and starting small. Implementation strategies included using a variety of grouping strategies and providing greater structure through mini-lessons, daily checks, and access to a list of relevant web resources. Assessment methods included using rubrics to outline project requirements and monitoring daily journals to assess progress. Teachers articulated that the use of these techniques enabled them to engage their students in interdisciplinary content and learning processes that met or exceeded required academic standards.
Gaussian Markov Random Fields: Theory and Applications

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics, a very active area of research in which few up-to-date reference works are available. Gaussian Markov Random Field: Theory and Applications is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. The book includes extensive case studies and online a c-library for fast and exact simulation. With chapters contributed by leading researchers in the field, this volume is essential reading for statisticians working in spatial theory and its applications, as well as quantitative researchers in a wide range of science fields where spatial data analysis is important.
Computational Auditory Scene Analysis

Contents: Preface. A.S. Bregman, Psychological Data and Computational ASA. J. Rouat, M. Garcia, A Prototype Speech Recognizer Based on Associative Learning and Nonlinear Speech Analysis. M. Slaney, A Critique of Pure Audition. R. Meddis, L. O'Mard, Psychophysically Faithful Methods for Extracting Pitch. F. Berthommier, C. Lorenzi, Implications of Physiological Mechanisms of Amplitude Modulation Processing for Modelling Complex Sounds Analysis and Separation. D. Wang, Stream Segregation Based on Oscillatory Correlation. G.J. Brown, M. Cooke, Temporal Synchronization in Neural Oscillator Model of Primitive Auditory Stream Segregation. F. Klassner, V. Lesser, H. Nawab, The IPUS Blackboard Architecture as a Framework for Computational Auditory Scene Analysis. K. Kashino, K. Nakadai, T. Kinoshita, H. Tanaka, Application of the Bayesian Probability Network to Music Scene Analysis. D.J. Godsmark, G.J. Brown, Context-Sensitive Selection of Competing Auditory Organizations: A Blackboard Model. M. Goto, Y. Muraoka, Musical Understanding at the Beat Level:Real-Time Beat Tracking for Audio Signals. S.H. Nawab, C.Y. Espy-Wilson, R. Mani, N.N. Bitar, Knowledge-Based Analysis of Speech Mixed With Sporadic Environmental Sounds. T. Nakatani, M. Goto, T. Ito, H.G. Okuno, Multiagent Based Binaural Sound Stream Segregation. M.K. Bhandaru, V.R. Lesser, Discrepancy Directed Model Acquisition for Adaptive Perceptual Systems. A. Fishbach, Auditory Scenes Analysis:Primary Segmentation and Feature Estimation. J.W. Grabke, J. Blauert, Cocktail Party Processors Based on Binaural Models. D. Ellis, D. Rosenthal, Midlevel Representations for Computational Auditory Scene Analysis: The Weft Element L. Solbach, R. Wohrmann, J. Kliewer, The Complex-Valued Continuous Wavelet Transform as a Preprocessor for Auditory Scene Analysis. N. Saint-Arnaud, K. Popat, Analysis and Synthesis of Sound Textures. S.M. Boker, Predicting the Grouping of Rhythmic Sequences Using Local Estimators of Information Content. B.L. Karlsen, G.J. Brown, M. Cooke, M. Crawford, P. Green, S. Renals, Analysis of a Simultaneous-Speaker Sound Corpus. H. Kawahara, Hearing Voice: Transformed Auditory Feedback Effects on Voice Pitch Control. L. Wyse, S.W. Smoliar, Toward Content-Based Audio Indexing and Retrieval and a New Speaker Discrimination Technique. E.D. Scheirer, Using Musical Knowledge to Extract Expressive Performance Information from Audio Recordings.
New models and algorithms for bandits and markets

Inspired by advertising markets, we consider large-scale sequential decision making problems in which a learner must deploy an algorithm to behave optimally under uncertainty. Although many of these problems can be modeled as contextual bandit problems, we argue that the tools and techniques for analyzing bandit problems with large numbers of actions and contexts can be greatly expanded. While convexity and metricsimilarity assumptions on the process generating rewards have yielded some algorithms in existing literature, certain types of assumptions that have been fruitful in offline supervised learning settings have yet to even be considered. Notably missing, for example, is any kind of graphical model approach to assuming structured rewards, despite the success such assumptions have achieved in inducing scalable learning and inference with high-dimensional distributions. Similarly, we observe that there are countless tools for understanding the relationship between a choice of model class in supervised learning, and the generalization error of the best fit from that class, such as the celebrated VCtheory. However, an analogous notion of dimensionality, which relates a generic structural assumption on rewards to regret rates in an online optimization problem, is not fully developed. The primary goal of this dissertation, therefore, will be to fill out the space of models, algorithms, and assumptions used in sequential decision making problems. Toward this end, we will develop a theory for bandit problems with structured rewards that permit a graphical model representation. We will give an efficient algorithm for regretminimization in such a setting, and along the way will develop a deeper connection between online supervised learning and regret-minimization. This dissertation will also introduce a complexity measure for generic structural assumptions on reward functions, which we call the Haystack Dimension. We will prove that the Haystack Dimension characterizes the optimal rates achievable up to log factors. Finally, we will describe more application-oriented techniques for solving problems in advertising markets, which again demonstrate how methods from traditional disciplines, such as statistical survival analysis, can be leveraged to design novel algorithms for optimization in markets. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Michael Kearns
A sixth-order triple-loop sigma-delta CMOS ADC with 90 dB SNR and 100 kHz bandwidth

The conversion rate of high-resolution wideband sigma-delta ADCs is limited by need for high oversampling ratio, typically 64 or more, for rejection of quantization noise. These rates lead to high amplifier power, large power-hungry digital filters, and difficult-to-drive signal and reference inputs. For 16b performance with low oversampling ratio it is necessary to use high-order noise shaping and/or multi-level quantizers and DACs, both leading to problems in design or manufacture if realized directly. The ADC described here consists of three cascaded second-order three-level loops giving a sixth-order noise-shaping function without linearity and stability problems from component mismatch or quantizer overload. <<ETX>>