Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Verification of AC/DC Comparator Based on A/D Sampling

There is an important significance of AC/DC comparator with high precision for the precision measurement in electric power industry. An AC/DC comparator based on the non integer period Sampling and rapid data refactoring is designed. The AC/DC comparator consists of the input buffer, A/D change, DC reference standard, magnetic isolation and sampling control chip. AC/DC comparator is used to sample the AC Magnitude and realize the AC traceable to DC with the reference of the high precision DC reference standard. Through the error analysis of the AC/DC comparator for each part, the Maximum error of voltage amplitude measurement is ±8×10 -6 . The experimental results show that the accuracy of frequency, amplitude and phase is respectively 10 -6 、10 -6 、10 -5 ,and the stability is 10 -6 、10 -6 、 10 -5 . It proves that the AC/DC comparator can accurate
Facilitated expert judgment of environmental risks: acquiring and analysing imprecise data

Expert judgment is essential for most practical, science-based risk analyses and forecasts. Typically, experts are asked to provide judgments in precise quantitative language. This requirement is a significant impediment to accurate and well-calibrated judgments because many scientists routinely misinterpret formal statistical language and concepts. Cognitive biases compound these conceptual and linguistic misunderstandings, compromising potentially vital information. We designed a protocol to remedy this problem, using a trained facilitator who deployed a range of techniques to elicit knowledge for a case study of recycled water contamination. The elicitation generated a range of imprecise information about the parameter, which we translated into constraints for probability bounds analysis. This approach allows experts to use forms of reasoning and communication best suited to them. We show how to represent and combine expert knowledge, and discuss the use of probability bounds analysis to supplement expert elicitation by framing risk problems in heuristic and mathematical terms.
Measure for Measure: An Experimental Test of Online Political Media Exposure

Self-reported measures of media exposure are plagued with error and questions about validity. Since they are essential to studying media effects, a substantial literature has explored the shortcomings of these measures, tested proxies, and proposed refinements. But lacking an objective baseline, such investigations can only make relative comparisons. By focusing specifically on recent Internet activity stored by Web browsers, this article's methodology captures individuals' actual consumption of political media. Using experiments embedded within an online survey, I test three different measures of media exposure and compare them to the actual exposure. I find that open-ended survey prompts reduce overreporting and generate an accurate picture of the overall audience for online news. I also show that they predict news recall at least as well as general knowledge. Together, these results demonstrate that some ways of asking questions about media use are better than others. I conclude with a discussion of survey-based exposure measures for online political information and the applicability of this article's direct method of exposure measurement for future studies.
Envelope-Derived Timing Recovery in QAM and SQAM Systems

The problem of continuously recovering timing phase and frequency in a high-rate carrier-modulated data communication device is addressed. The communication link is assumed narrowband with respect to the baud rate of the device, thus necessitating the use of some type of equalization in the receiver. The receiver demodulator is assumed coherent in nature. A timing recovery technique based on narrow-band filtering and squaring of the received demodulated waveform(s) is examined. Analysis of the first moment of the waveforms so generated reveals a convenient indicator of sampling instants. If the timing recovery filters are properly designed, then these sampling instants exactly satisfy a requirement previously derived, ensuring efficient operation of the equalizer. Design criteria for the filters are given. Two principal types of linear modulation techniques are considered. These are quardrature-carrier amplitude modulation (QAM) and staggered QAM (SQAM). A set of experimental waveforms observed in a QAM modem signaling at 2400 Bd is included for illustration. Several conclusions are drawn as a result of the study. First, the mean timing waveforms in QAM and SQAM systems bear fixed time relationships to suitable receiver sampling instants. Second, adequate timing filter design is attained with very narrowband filters centered on frequencies equal to half the baud rate and the full baud rate. Third, recovery circuits used in QAM systems may be designed so as to have negligible timing jitter. In SQAM systems and, consequently, in vestigial sideband-amplitude modulation (VSB-AM) systems, this advantage is generally not found.
Web-based interactive TV services for older users

This article reports a study that explored user requirements for web-based interactive TV (iTV) services from the perspective of older users. Members of a University of the Third Age (U3A) group were recruited for the study in order to explore the potential for web-based iTV to be used as a tool for mediating social communities. Focus groups and user trials were used to explore participants’ reactions to the concept of web-based iTV and to identify barriers to use. The study found that the web-based iTV service offered in some contexts several advantages to personal computer use that were valued by study participants. However, significant physical and cognitive barriers to use were identified, such as cost of services, lack of privacy, information overload and learning interface equipment. Recommendations for designing web-based iTV services for older users, generated in response to these concerns, include keeping the user interface simple, avoiding multi-modal keys on the keypad and providing information portals tailored to the interests of older users.
A Model-Based Factored Bayesian Reinforcement Learning Approach

Bayesian reinforcement learning has turned out to be an effective solution to the optimal tradeoff between exploration and exploitation. However, in practical applications, the learning parameters with exponential growth are the main impediment for online planning and learning. To overcome this problem, we bring factored representations, model-based learning, and Bayesian reinforcement learning together in a new approach. Firstly, we exploit a factored representation to describe the states to reduce the size of learning parameters, and adopt Bayesian inference method to learn the unknown structure and parameters simultaneously. Then, we use an online point-based value iteration algorithm to plan and learn. The experimental results show that the proposed approach is an effective way for improving the learning efficiency in large-scale state spaces.
Interactive whiteboards and schooling: the context

This paper seeks to set the scene for the ensuing articles in this themed issue by placing the recent developments with interactive whiteboards (IWBs) in their historical context. It argues that use of this instructional technology has already had a profound impact upon teaching and highlights the importance of educational researchers considering both the micro and the macro context when they explore the impact of any instructional technology use. The paper moreover suggests that the IWB is emerging as the second great revolutionary instructional technology and is shaping as the development that is fundamentally changing the nature of schooling, moving it from its traditional paper‐based form to digitally based organisation. The author’s provocative stance stems from a perspective of directing and networking schools. Many of the subsequent papers elaborate examples of IWB use in classrooms, helping the reader to evaluate the argument using recent empirical research.
Robust disparity estimation in terrain modeling for spacecraft navigation

Navigation and imagery in the orbit, descent, and landing phases during an interplanetary mission require methods that are able to derive the elevation map of a planetary body using remote sensing tools. The authors propose stereovision techniques for this task. An algorithm for correspondence matching, which is one of the crucial steps in automatic terrain modeling, is introduced. It uses well known pyramid-based data structures, but is novel in its direct application of methods from statistical pattern recognition. Feature vectors for correspondence matching and feature selection techniques are used to find optimal features. These include grey-level statistics (mean variance) as well as more sophisticated features derived from operators like local frequency edge gradient or, as an extension, Moravec-, Gabor- or Fourier-features. The applicability of the algorithm in the remote sensing scenario of interplanetary missions is verified using a mockup simulation of the Martian surface. <<ETX>>
Facial expressions recognition using an ensemble of feature sets based on key-point descriptors

Abstract The authors in this study proposed facial expression recognition system in order to improve the expression recognition performance over the recently proposed systems. Feature sets for all training samples are constructed based on speed up robust feature descriptors. An ensemble of feature sets is then created incrementally. To achieve high diversity of ensemble, the dissimilarities between the training samples for each class are computed. This high diversity led to a high recognition rate. Experimentation on two publicly available datasets is performed. The system achieved 98·6% accuracy on JAFFE dataset and 96·3% accuracy on Multimedia Understanding Group dataset. The results of proposed system are compared with recently proposed work in this area and proved the soundness of the proposed method.
A Novel Adaptive Fast Learning Algorithm for 2DPCA

We propose a novel adaptive fast learning (AFL) algorithm for two-dimensional principal component analysis (2DPCA) in this paper. As opposite to conventional PCA which is based on 1D data vectors, 2DPCA is based on 2D image matrices and thus has higher accuracy than conventional PCA when applied to applications such as face recognition, facial expression recognition, palmprint recognition, etc. Our proposed AFL algorithm simultaneously estimates both eigenvectors and corresponding eigenvalues, and then adaptively sets the learning rate parameters of neurons to ensure all neurons learning with almost the same fast speed. Requiring no image covariance matrix evaluation, the desired multiple eigenvectors of 2DPCA can thus be learned effectively in the form of weight vectors of neurons. The proposed AFL algorithm can also be applied to learning for T-2DPCA. Simulation experiments performed on face database such as the YaleB database clearly demonstrate that the proposed AFL algorithm performs very well and thus is a very effective computational tool for both 2DPCA and T-2DPCA.
Studying the Elusive Experience in Pervasive Games

Studying pervasive games is inherently difficult and different from studying computer or board games. This article builds upon the experiences of staging and studying several playful pervasive technology prototypes. It discusses the challenges and pitfalls of evaluating pervasive game prototypes and charts methods that have proven useful in previous research. The aim is to open discussion on the situated methodology of qualitative study of evaluating and researching pervasive play.
The use of animation in higher education teaching to support students with dyslexia

Purpose – The purpose of the research reported in this paper was to examine the potential usefulness of animated learning materials for supporting students with dyslexia in a UK higher education setting.Design/methodology/approach – An experiment was conducted with a set of 13 undergraduate computing students with dyslexia and a control group of 13 non‐dyslexic undergraduate computing students.Findings – Overall it appeared that appropriate animated learning materials were perceived as being more useful than equivalent static learning materials by both the students with dyslexia and the control group of non‐dyslexic students. However, the control group appeared to find them more useful than the students with dyslexia.Research limitations/implications – Although the experiment reported in this paper was small in scale it did appear to indicate that animated learning materials may potentially be useful for undergraduate students with (and without) dyslexia.Originality/value – There appears to have been litt.
Image preprocessing to improve the reliability of normalized correlation

Machine vision based alignment is a fundamental feature of many types of semiconductor manufacturing equipment, and normalized correlation based pattern finding remains commercially popular as a core of the alignment systems. Despite its strengths, normalized correlation search (NCS) alone often fails to find the correct pattern when given the kind of degradations and nonlinear image variations seen in some semiconductor processes. This paper discusses the utilization of image processing prior to the execution of the NCS algorithm as a way to improve alignment reliability.
Predicting Values and Trends from Tables and Graphs

The issue of optimal display design was re-examined under the hypothesis that the nature of the data set would influence the efficacy of displays. An experiment assessed the effect of data structure on the relative efficacy of line graphs and tables. Participants saw a sequence of graphic or tabular displays which presented data from functions. The displays showed either values taken from the simple form of a single sinusoid function (structured data conditions), or from the complex and seemingly unstructured form of five summed sinusoid functions (unstructured data conditions). After seeing four consecutive displays participants were asked to predict the behavior of the data set in the next display which they had not yet seen. They were required to predict either the behavior of specific points, or the direction of trends. While the performance of the point comparison task was not influenced by display format, graphs were found to have an advantage for the prediction of future trends. Graphs also led more frequently to the identification of structure than did tables, both for structured and unstructured conditions.
A New SVM Active Learning Algorithm Based on KNN

Of all the active learning research, the study on the active learning algorithm for SVM is much less. In this paper, based on K-Nearest Neighbors (KNN), we propose a new SVM active learning algorithm. The algorithm first collects the potential informative samples to form a potential informative sample set, and then in this sample set, based on KNN it evaluates the sparseness for each sample. The sample that locates at a sparser region is taken as an informative one, and is selected for training. Experimental results show that the proposed algorithm can greatly improve the classification performance, and can efficiently accelerate the convergence of the classifier.
An Ethnographic Study of Code Choice in Two Neighbourhoods of Indonesia

Calls for more holistic sociolinguistics have increased in recent years, especially ones that enable multi-level approaches to the interpretation of language use. This paper shows how such an approach was used to examine code choice in an Indonesian urban setting. In particular it looks at and compares code choice in a low income neighbourhood and a middle-income neighbourhood of Semarang, the provincial capital of Central Java. I show that patterns of language exchange are different to what we might expect, especially as they relate to inter-ethnic conversations. I account for these patterns using ethnographic data.
Performance analysis of closed loop power control methods in the UTRA-FDD mode

In this paper, closed loop power control procedures in UTRA-FDD are studied. Fast power control is perhaps the most important aspect in a WCDMA system like this. The research has been carried out through a physical layer simulator that has been implemented in the C language from the standard of the UTRA-FDD mode. Different parameters have been taken into account: The research has been made in parallel for two mobile speeds, 3 km/h and 50 km/h, getting very different results. Different step sizes have been tested. The implementation of an adaptive algorithm to predict the received power to try to improve the power control has been studied, but large improvements have not been obtained. The probability distribution of the power control error and the influence of the distance between the base station and the mobile have also been studied.
An extended cellular automaton using case‐based reasoning for simulating urban development in a large complex region

Rule‐based cellular automata (CA) have been increasingly applied to the simulation of geographical phenomena, such as urban evolution and land‐use changes. However, these models have difficulties and uncertainties in soliciting transition rules for a large complex region. This paper presents an extended cellular automaton in which transition rules are represented by using case‐based reasoning (CBR) techniques. The common k‐NN algorithm of CBR has been modified to incorporate the location factor to reflect the spatial variation of transition rules. Multi‐temporal remote‐sensing images are used to obtain the adaptation knowledge in the temporal dimension. This model has been applied to the simulation of urban development in the Pearl River Delta which has a hierarchy of cities. Comparison indicates that this model can produce more plausible results than rule‐based CA in simulating this large complex region in 1988–2002.
Load Management Techniques for Distributed Stream Processing

Distributed and parallel computing environments are becoming inexpensive and commonplace. The availability of large numbers of CPUs makes it possible to process more data at higher speeds. Stream-processing systems are becoming more important, as broad classes of applications require results in real-time. Since load can vary in unpredictable ways, exploiting the abundant processor cycles requires effective load management techniques. Although load distribution has been extensively studied for the traditional pull-based systems, it has not yet been fully studied in the context of push-based continuous query processing. Push-based data streams commonly exhibit significant variations in their rates and burstiness over all time-scales. Even though medium-to-long term load variations can be dealt with using dynamic load migration techniques rather effectively, short-term variations are much more problematic---since capturing such variations and reactively migrating load can be prohibitively expensive or even impractical. We need robust solutions whose performances do not degrade significantly in the presence of time-varying load. In this dissertation, we present both a static operator distribution technique and a dynamic operator re-distribution technique for a cluster of stream processing engines. First, for operators that are impractical to be moved between processing engines, on the fly, we propose a resilient static operator distribution algorithm that aims at avoiding overload by maximizing the feasible space of the operator distribution plan. Second, for operators with relatively small load migration overheads, we propose a correlation-based dynamic operator distribution algorithm that aims at minimizing end-to-end latency by minimizing load variance and maximizing load correlation. Our experiment results quantify the effectiveness of the proposed approaches and demonstrate that they significantly outperform traditional operator distribution approaches.
Mapping Ecologically to Modalities

Ecological interface design (EID) is an approach to designing user interfaces that is based on the objective of providing functional system relationships to users in ways that reduce perceptual load. While EID has strong methods for determining the needed functional relationships through the analytical methods of Cognitive Work Analysis (Vicente, 1999), relatively little attention has been paid to establishing the design mappings that reduce perceptual load. We propose that different kinds of information should be assessed for perceptual fit to various modalities. In particular, those mappings that combine appropriate forms of reference with strong perceptual affordances will likely be the most successful. A case study applying this approach to the design of a multimodal ground control station for uninhabited aerial vehicle control is discussed.
Security Analysis to an Biometric Authentication Protocol for Wireless Sensor Networks

【A novel authentication mechanism is biometric authentication where users are identified by their measurable human characteristics, such as fingerprint, voiceprint, and iris scan. The technology of biometrics is becoming a popular method for engineers to design a more secure user authentication scheme. In terms of physiological and behavioral human characteristics, biometrics is used as a form of identity access management and access control, and it services to identity individuals in groups that are under surveillance. In this article, we review the biometric-based authentication protocol by Althobati et al. and provide a security analysis on the scheme. Our analysis shows that Althobati et al.'s scheme does not guarantee server-to-user authentication. The contribution of the current work is to demonstrate this by mounting threat of data integrity and bypassing the gateway node on Althobati et al.'s scheme. In addition, we analysis the security vulnerabilities of Althobati et al.'s protocol. 】
Real-Time Objects Detection Using Layered Codebook Model

In surveillance system,it was challenging to improve real-time in the presence of dynamic background motions.We presented a real-time algorithm for foreground-background segmentation based on codebook model.Pixels were converted from RGB space to YCrCb space,background model used layered model.Firstly we established a basic codebook background model and then got rough background pixels by twice frame difference,and then only trained rough background pixels which have removed foreground pixels.Secondly the foreground was segmented from the background and we updated the background in real-time. The experimental results show that this method can save time of establishment of codebook background model and has small calculation and high accuracy in scenes such as illumination changes,swaying trees and stopped objects should be considered part of the background objects.
Polymer Ratio Optimization Based on Support Vector Machine and Genetic Algorithm

For getting rid trial-and-error method of polymer blending modification depends on experience, nonlinear relation of polymer ratio and its properties is obtained by Support Vector Machine and Genetic Algorithm. While the application of genetic algorithms to optimize the SVM model. Decision function in the relational model of SVM as the objective function, and optimized the value of polymer properties and its ratio by genetic algorithms. Experiments show that the method has some values.