Miracle-dz/UniFAR-FTUs huggingface dataset:
0.9-V sense-amplifier-based reduced-clock-swing MTCMOS flip-flops

Scaling-down V/sub DD/ as well as adopting a reduced-swing clock simultaneously is the key technique proposed in this work which results in remarkable power reduction for VLSI chips. Several configurations of 0.9-V MTCMOS (multi-threshold CMOS) flip-flops with reduced clock-swing (RCSFFs) are investigated in this work. By using the MVT technique, and the pulsed-low reduced-swing clock, the performance of the master stage of the new low-V/sub DD/ RCSFF is improved significantly. If the slave stage is constructed with the low-V/sub T/ SR latch, the new RCSFF is even 5% faster and 10% more power efficient than the low-V/sub DD/ full-clock-swing FF. If the slave stage is constructed with the MVT improved SR latch, the new RCSFF does not require a standby-controlled PMOS and is 18% faster than the low-V/sub DD/ RCSFF with a high well bias and 15% more power efficient than the low-V/sub DD/ full-clock-swing flip-flop.
Automatic real-time identification of fingerprint images using wavelets and gradient of Gaussian

In this paper, a fingerprint recognition algorithm is suggested. The algorithm is developed based on the wavelet transform, and the dominant local orientation which is derived from the coherence and the gradient of Gaussian. By using the wavelet transform, the algorithm does not require conventional preprocessing procedures such as smoothing, binarization, thinning and restoration. Computer simulation results show that when the rate of Type II error-incorrect recognition of two different fingerprints as identical fingerprints-is held at 0.0%, the rate of Type I error-incorrect recognition of two identical fingerprints as different ones-turns out as 2.5% in real time.
An artificial neural network model to predict thunderstorms within 400 km2 South Texas domains

Artificial neural network (ANN) models were developed to predict thunderstorm occurrence within three separate 400 km2 regions, 9, 12 and 15 h (±2 h) in advance. The predictors include output from deterministic Numerical Weather Prediction models and from sub‐grid scale soil moisture magnitude and heterogeneity estimates. The feed‐forward multi‐layer perceptron ANN topology, with one hidden layer and one neuron in the output layer, was chosen. Two sets of nine ANN models each were developed; one set was developed after a filtering‐based feature selection technique was used to determine the predictor subset from 43 potential predictors. The other models were developed based on all 43 predictors. For each of the 18 models, a wrapper technique was used to determine the optimal number of neurons in the hidden layer. Thunderstorm artificial neural network (TANN) model performance was compared to that of multi‐linear regression (MLR) models, and to human forecasters (NDFD), based on a novel data set. Results reveal that for several of the nine box/prediction hour combinations with respect to at least one skill‐based performance metric, the TANN model's performance exceeded that of the MLR models and NDFD. Yet, the performance of both the MLR models and NDFD were superior to that of the corresponding TANN models in several other cases. Results indicate that the TANN models can provide automated predictions with skills similar to that of operational forecasters. Comparisons of the two sets of TANN models reveal utility in the use of feature selection.
An intelligent computer tutor to guide self-explanation while learning from examples

Many studies in cognitive science show that self-explanation—the process of clarifying and making more complete to oneself the solution of an example—improves learning, and that guiding self-explanation extends these benefits. This thesis presents an intelligent computer tutor that aims to improve learning from examples by supporting self-explanation. The tutor, known as the SE (self-explanation) Coach, is innovative in two ways. First, it represents the first attempt to develop a computer tutor that supports example studying instead of problem solving. Second, it explicitly guides a domain-general, meta-cognitive skill: self-explanation. The SE-Coach is part of the Andes tutoring system for college physics and is meant to be used in conjunction with the problem solving tasks that Andes supports. In order to maximize the system capability to trigger the same beneficial cognitive processes, every element of the SE-Coach embeds existing hypotheses about the features that make self-explanation effective for learning. Designing the SE-Coach involved finding solutions for three main challenges: (1) To design an interface that effectively monitors and supports self-explanation. (2) To devise a student model that allows the assessment of example understanding from reading and self-examination actions. (3) To effectively elicit further self-explanation that improves student's example understanding. In this work we present our solutions to these challenges: (1) An interface including principled, interactive tools to explore examples and build self-explanations under the SECoach's supervision. (2) A probabilistic student model based on a Bayesian network, which integrates a model of correct self-explanation and information on the student's knowledge and studying actions to generate a probabilistic assessment of the student's example understanding. (3) Tutorial interventions that rely on the student model to detect deficits in the student's example understanding and elicit self-explanations that overcome them. In this thesis we also present the results of a formal study with 56 college students to evaluate the effectiveness of the SE-Coach. We discuss some hypotheses to explain the obtained results, based on the analysis of the data collected during the experiment.
Research Perspectives on English for Academic Purposes: Reading academic English: Carrying learners across the lexical threshold

The ESP reading problem With the growth of English as the lingua franca of work and study, many non-English speakers find themselves needing to attain some level of proficiency in English in order to function in jobs or courses. However, they may have limited time to devote to language learning, and little interest in knowing English outside the work or study context. Responding to these circumstances, English for Specific Purposes (ESP) curriculum designers have attempted to reduce the time frame of learning through domain targeting. They attempt to identify and teach the lexis, syntax, functions and discourse patterns most commonly used in a domain (for chemistry students, test tubes, passive voice, clarification requests and laboratory reports). This approach has given waiters, tour guides and airline pilots enough English to function in their domains after relatively short periods in the classroom. But it runs into complications when the specific purpose is to read extended texts in a professional or academic domain. It now seems clear that the cross-domain generalities of English (pronoun system, verb tenses, basic vocabulary, etc.) can be introduced and practised within a subset of the language. Simple reading tasks such as understanding signs and instructions can be undertaken knowing only the English used in a particular job or profession. But does this hold true for reading longer texts? Consider the position of the learner who knows the grammar of English and the technical terms of a domain: text analysis shows that these terms are typically rather few (Flowerdew, 1993c), roughly 5% of tokens (Nation, 1990).
Efficient Compressive Sensing based Technique for Routing in Wireless Sensor Networks

Energy consumption and prolonging network lifetime are a primary challenge in many studies on Wireless Sensor Networks (WSN). Thus, since radio communication and routing protocol transmission are in general the main cause of power consumption, different techniques proposed in lit- erature to improve energy efficiency have mainly focused on l imiting transmission/reception of data. To this aim, we propose an adaptive and efficient technique base d on compressive sensing for improving the performance of routing in wireless sensor network. The performance of our technique is evaluated by applying it to PEGASIS (power efficient gathering in senso r information systems), which is one of the most popular protocols for routing in wireless sensor network. A comparison of PEGASIS and PE- GASIS with Huffman coding shows the advantage of the proposed technique in terms of reducing the energy consumption and network lifetime.
Event‐triggered control for networked Markovian jump systems

The problem of H∞ control for networked Markovian jump system under event‐triggered scheme is studied in this paper. In order to reduce the utilization of limited network bandwidth, a dynamic discrete event‐triggered scheme to choose the transmitted data is designed. A Markovian jump time‐delay system model is employed to describe the event‐triggered scheme and the network related behavior, such as transmission delay, data package dropout, and disorder. Furthermore, a sufficient condition is derived to guarantee that the resulting closed‐loop system is stable and has a prescribed performance index. A co‐design method for the H∞ controller and the event‐triggered scheme is then proposed. The effectiveness and potential of the theoretic results obtained are illustrated by a simulation example. Copyright © 2014 John Wiley & Sons, Ltd.
Multi-Algorithm Ontology Mapping with Automatic Weight Assignments and Background Knowledge

Ontology mapping is an area dealing mainly with matching of entities from two different ontologies. To date, many different approaches have been proposed from those employing a single algorithm to those employing multiple algorithms. However multiple algorithms have proven to be able to give better results than just applying a single algorithm. This paper presents a multi-algorithm approach with key contribution in its pre-processing and choice of background knowledge to further improve the mapping results.
The integrated use of optical and InSAR data for urban land‐cover mapping

The aim of this study is to classify urban land‐cover types using the features derived from optical and spaceborne synthetic aperture radar (InSAR) data sets. For the efficient discrimination of the selected classes, a rule‐based algorithm that uses the initial image segmentation procedure based on a minimum distance rule and the constraints on spectral parameters and spatial thresholds is constructed. The result of the rule‐based method is compared with the results of a standard supervised classification and it demonstrates a higher accuracy. Overall, the research indicates that the integrated features of the optical and InSAR images can significantly improve the classification of land‐cover types and the rule‐based classification is a powerful tool in the production of a reliable land‐cover map.
Design and Development of Lower Limb Chair Exercise Support System with Depth Sensor

Sustaining lower limb functionality is extremely important in the preventative care of the elderly. Chair exercise, in which the exerciser sits on an ordinary chair, offers a way for seniors with little physical strength to exercise without a great deal of effort. Meanwhile, Microsoft's Kinect sensor that is capable of detecting human motion without the subject having to wear any kind of a special marker are becoming widely available. Exploiting this new sensor technology, this paper describes the design and development of a prototype lower limb chair exercise support system. The system supports five different chair exercises designed to strengthen the lower limbs, recognizes and evaluates exercises based on 3D position data and joint angles for each joint obtained from the Kinect sensor. The system illustrates how to do the exercises by voice instructions and model images, and superimposes the muscles used onto an image of the exerciser in real time. The system also provides exercise assessment results and advice by voice and text. In a series of trials involving seven elderly subjects in their late 70s and early 80s, an overall average recognition rate of 89% was obtained for the five exercises. Feedback was obtained through a questionnaire given to subjects ranging in age from 50 to 65, which highlighted a number of issues that should be addressed to improve the effectiveness of the system.
Building a Better Mousetrap

Points out dozens of examples of government agencies whose records in the National Archives pertain to some aspect of science or technology. The remainder of the paper describes in detail all the many types of documents connected with the development of atomic weapons, dating back to 1939, when scientists alerted President Roosevelt to the danger of German creation of an atomic weapon. The paper carries through to the use of the weapons in Japan and to the famous spy trial in 1950.
Transient discrimination in nonlinear time series using linear-phase time-delay neural networks

A linear-phase adaptive nonlinear predictor is presented which allows the adaptive discrimination of short-term transient signals from longer-term periodic signals whilst maintaining the phase integrity of both. A linear-phase time-delay neural network is used in an adaptive predictor configuration and is adaptively trained as a predictor for signals arising from nonlinear processes. The targeted application of this technique is the decomposition of electroencephalograms to classify waveform transient and background rhythmic characteristics which is useful in managing neurophysiological conditions such as epilepsy.
Professional Learning Networks Designed for Teacher Learning

Abstract In the information age, students must learn to navigate and evaluate an expanding network of information. Highly effective teachers model this process of information analysis and knowledge acquisition by continually learning through collaboration, professional development, and studying pedagogical techniques and best practices. Many teachers have extended their learning by developing online professional learning networks (PLNs). PLNs connect teachers to other individuals worldwide who can offer support, advice, feedback, and collaboration opportunities. PLNs also allow teachers to collect information from various Web sites and access it in one organized area so they can efficiently stay up to date on the latest teaching techniques, pedagogies, and changes in the field of education. This paper reviews three popular PLNs created for educators—Edmodo, Classroom 2.0, and The Educator’s PLN—and provides an analysis of how teachers use the PLNs for learning, sharing, and communicating.
The Logic of Events, a framework to reason about distributed systems

We present a logical framework to reason about distributed systems called the Logic of Events. This logic has been formalized in Nuprl. We developed a suite of tools and tactics in Nuprl to reason about event classes. We also developed a programming language called EventML which allows programmers to write specifications of distributed protocols.
Congres voor Lerarenopleiders 2016

Hoe autonomie-ondersteunend werkt een docent binnen honours onderwijs? Presentatie VELON conferentie voor lerarenopleiders 2016, 4 en 5 februari 2016. Docenten werken graag met intrinsiek gemotiveerde studenten. Vanuit de literatuur weten we dat studenten, die intrinsiek gemotiveerd zijn, een sterke behoefte hebben aan een docent die hen ondersteunt in hun autonomie, gecombineerd met het bieden van structuur en verbondenheid. Deze studie richt zich op excellentieprogramma’s. Studenten in deze leeromgeving zijn veelal intrinsiek gemotiveerd. Om een autonomie-ondersteunende doceerstijl in te kunnen zetten, is het van belang dat de docent zelf gemotiveerd is en vrijheid ervaart. Deze studie richt zich op de samenhang tussen hoe een docent bepaalde aspecten in zijn werkomgeving ervaart en welke invloed dat heeft op zijn doceerstrategie – en dan in het bijzonder de strategie die autonomie ondersteunt.
Performance of wavelet analysis and neural networks for pathological voices identification

Within the medical environment, diverse techniques exist to assess the state of the voice of the patient. The inspection technique is inconvenient for a number of reasons, such as its high cost, the duration of the inspection, and above all, the fact that it is an invasive technique. This study focuses on a robust, rapid and accurate system for automatic identification of pathological voices. This system employs non-invasive, non-expensive and fully automated method based on hybrid approach: wavelet transform analysis and neural network classifier. First, we present the results obtained in our previous study while using classic feature parameters. These results allow visual identification of pathological voices. Second, quantified parameters drifting from the wavelet analysis are proposed to characterise the speech sample. On the other hand, a system of multilayer neural networks (MNNs) has been developed which carries out the automatic detection of pathological voices. The developed method was evaluated using voice database composed of recorded voice samples (continuous speech) from normophonic or dysphonic speakers. The dysphonic speakers were patients of a National Hospital ‘RABTA’ of Tunis Tunisia and a University Hospital in Brussels, Belgium. Experimental results indicate a success rate ranging between 75% and 98.61% for discrimination of normal and pathological voices using the proposed parameters and neural network classifier. We also compared the average classification rate based on the MNN, Gaussian mixture model and support vector machines.
Efficient Computation in the Brain

It has been long proposed that the brain should perform computation efficiently to increase the fitness of the organism. However, the validity of this prominent hypothesis remains debated. In this thesis, I investigate how this idea of efficient computation can guide us to understand the operational regimes underlying various cognitive functions, in particular perception and spatial cognition. In the first study, I demonstrate that such idea leads to a well-constrained yet powerful model framework for human perceptual behaviors by assuming the system is efficient both in term of encoding and decoding. This framework, when applying to human visual perception, explains many reported perceptual biases, including the repulsive biases away from prior peak, which are counter-intuitive according to the traditional Bayesian view. This framework also offers a principle way to address the common criticisms of Bayesian models in perception, which argue that Bayesian models are lack of constraints. In the second study, I demonstrate that the idea of efficiency, coupled with a few assumptions, allows us to make quantitative predictions on the functional architecture of the grid cell system in rodents. One such prediction is that the spatial scales of grid modules should follow a geometric progression, importantly, with the scaling factor to be close to the square root of transcendental number e ~1.6. Such zero-parameter predictions closely match the data reported in recent neurophysiological experiments. The theory also makes several other predictions, some of which have been confirmed by the data. This study suggests that achieving efficiency computation may also apply to neural circuits involving a high-level cognition, i.e. representation of space. In the third study, I analytically derive a generic connection between mutual information and Fisher information. This clarifies an important theoretical issue which has been misunderstood in previous neural coding literature. Additionally, it provides some powerful signatures of the Efficient coding hypothesis, which could guide future experimental tests. Together, the results presented in this thesis suggest that achieving efficient computation serves as a basic design principle which generalizes across neural systems processing low-level and high-level cognitive functions. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Psychology First Advisor Alan A. Stocker Second Advisor Vijay Balasubramanian
THU TREC 2002: Novelty Track Experiments

duplication. Accordingly, expansion-based technologies are the key points. Studies of expansion technologies have been performed on three levels: efficient query expansion based on thesaurus and statistics, replacement-based document expansion, and term-expansion-related duplication elimination strategy based on overlapping measurement. Besides, two issues have been studied: finding key information in topics, and dynamic result selection. A new IR system has been developed for the task. In the system, four weighting strategies have been implemented: ltn.lnu [1] , BM2500 [2] , FUB1 [3] , FUB2 [3] . It provides both similarity and overlapping measurements, based on term expansion. Comparisons can be made on sentence-to-sentence or sentence-to-pool level.
English Text: System and structure

This book is a comprehensive introduction to text forming resources in English, along with practical procedures for analysing English texts and relating them to their contexts of use. It has been designed to complement functional grammars of English, building on the generation of discourse analysis inspired by Halliday and Hasan's Cohesion in English . The analyses presented were developed within three main theoretical and applied contexts: (i) educational linguistics (especially genre-based literacy programmes) (ii) critical linguistics (as manifested in the development of social semiotics) and (iii) computational linguistics (in dialogue with the various text generation projects based on systemic approaches to grammar and discourse). English Text's major contribution is to outline one way in which a rich semantically oriented functional grammar can be systematically related to a theory of discourse semantics, including deconstruction of contextual issues (i.e. register, genre and ideology). The chapters have been organized with the needs of undergraduate students in theoretical linguistics and postgraduate students in applied linguistics in mind.
ε-first policies for budget-limited multi-armed bandits

We introduce the budget-limited multi-armed bandit (MAB), which captures situations where a learner's actions are costly and constrained by a fixed budget that is incommensurable with the rewards earned from the bandit machine, and then describe a first algorithm for solving it. Since the learner has a budget, the problem's duration is finite. Consequently an optimal exploitation policy is not to pull the optimal arm repeatedly, but to pull the combination of arms that maximises the agent's total reward within the budget. As such, the rewards for all arms must be estimated, because any of them may appear in the optimal combination. This difference from existing MABs means that new approaches to maximising the total reward are required. To this end, we propose an ∊-first algorithm, in which the first ∊ of the budget is used solely to learn the arms' rewards (exploration), while the remaining 1 - ∊ is used to maximise the received reward based on those estimates (exploitation). We derive bounds on the algorithm's loss for generic and uniform exploration methods, and compare its performance with traditional MAB algorithms under various distributions of rewards and costs, showing that it outperforms the others by up to 50%.