Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligibility of time-compressed speech: three ways of time-compression

Studies on fast speech have shown that word-level timing of fast speech differs from that of normal rate speech in that unstressed syllables are shortened more than stressed syllables as speech rate increases. An earlier experiment showed that the intelligibility of time-compressed speech could not be improved by making its temporal organisation closer to natural fast speech. To test the hypothesis that segmental intelligibility is more important than prosodic timing in listening to time-compressed speech, the intelligibility of bisyllabic words was tested in three time-compression conditions: either stressed and unstressed syllable were compressed to the same degree, or the stressed syllable was compressed more than the unstressed syllable, or the reverse. As was found before, imitating word-level timing of fast speech did not improve intelligibility over linear compression. However, the results did not confirm the hypothesis either: there was no difference in intelligibility between the three compression conditions. We conclude that segmental intelligibility plays an important role, but further research is necessary to decide between the contributions of prosody and segmental intelligibility to the word-level intelligibility of time-compressed speech.
Annotations for Seamless Aspect-Based Software Evolution

We are developing a dynamic software evolution system that leverages aspect technology to encapsulate software updates. Ideally, an evolution system provides as much automation as possible. Certain changes, however, defeat automation. For instance, field additions cannot be concisely captured without the feedback of the programmer. Rather than reconstructing the missing information in retrospect, we propose to gather the necessary meta-data along with the development process. We use Java annotations for that purpose: for instance, programmers may annotate added fields with their corresponding initialization. In some cases, the software development environment may even infer annotations from the actions taken by the programmer, and therefore, annotations enable a seamless software evolution cycle.
A New Kind of Cluster-based Key Management Protocol in Wireless Sensor Network

Many cluster-based wireless sensor network routing protocols have been proposed to take the advantages of cluster computing. However, most of these protocols take little consideration on communication protection, which is important to ensure the network security. In this paper, a lightweight key management approach is presented. Analysis shows that this approach is an effective solution to the key management of layered cluster wireless sensor network.
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— An IEEE 802.16e-2005 (WiMAX) compliant, long-reach passive optical network is demonstrated, focusing on the development of next generation optical access with transparent wireless backhauling. In addition to the extended feeder reach, wavelength band overlay is used to enhance network scalability by maintaining passive splitting in the field and with some design modification at the optical line terminal and remote base station. Radio over fibre is used to minimise network installation and maintenance costs through the use of simple remote radio heads complemented by frequency division multiplexing to address individual base stations. The implementation of overlapping radio cells/sectors is also proposed to provide joint signal processing at wireless user terminals. Experimental measurements confirmed EVMs below-30 dB and-23 dB downstream and upstream respectively over fibre link lengths of up to 84.6 km. In addition, ACLR measurements demonstrated that a figure of-45 dB with 40 MHz subcarrier spacing, as specified by the standard can be readily achieved.
Panel and Cross-sectional Data 16.1 Generalized Method of Moments

Many recent macroeconomic applications use panel and cross-sectional data. For example, macroeconomic hypotheses are tested in micro data sets for households, industries, and business firms and in aggregate data sets for many countries. This chapter focuses on econometric issues that are particularly relevant for macroeconomic applications. This section discusses GMM from the cross-sectional average rather than from the time series average as in Chapter 9. The method here can be applied to both cross-sectional and panel data with many cross-sectional observations and to those with a relatively small number of observations over time. Given cross-sectional data for x i , let b 0 be a p-dimensional vector of the parameters to be estimated, and f (x i , b) a q-dimensional vector of functions. We refer to u i = f (x i , b 0) as the disturbance of GMM. We assume that x i is i.i. d. Consider the (unconditional) moment restrictions E(f (x i , b 0)) = 0.
Adolescent adjustment to maternal cancer: an interpretative phenomenological analysis (IPA)

Purpose As an Interpretative Phenomenological Analysis (IPA), the purpose of this paper is to provide an in-depth understanding of adolescent experiences of maternal cancer to identify the individual and contextual factors that shape adolescent experiences and evaluates the potential applicability of the Family Ecology Model to the illness context. Design/methodology/approach This analysis is focussed on three female adolescents who completed semi-structured interviews, which were subjected to IPA. Maternal illness is a challenge for adolescents, which can be improved or undermined by their contexts. The analysis yielded three sub-themes: family structure, social supports, experiencing maternal cancer at a time of transition and the lasting impact of cancer. Findings This study found that adolescent experiences of maternal cancer depend on their contexts from an ecological perspective the type and quality of adolescent interactions determine coping and adjustment. Maternal cancer can be difficult as adolescents are already facing specific developmental challenges. Future research can benefit from adopting an ecological perspective to further understand adolescent experiences to support adolescent that may be more vulnerable and benefit from additional supports. This is not a generalisable piece of research but it provides a very deep and detailed understanding of the impact of maternal cancer on adolescents’ developmental course and determines how the complexity of their contexts can serve as a risk or a protective factor at this challenging time. Originality/value This paper contributes to the body of research by providing a comprehensive understanding of adolescents facing maternal cancer. The Ecological Model supports the findings of this research and proves to be a good model to understand the complex interplay between adolescents and their environments when facing a difficult challenge like maternal cancer is.
Agent-mediated self-regulated learning: A conceptual model based on knowledge management at agent level

Continuous improvement and self-regulation are often mentioned when a person talks about quality of education or teaching and learning. In 1990s, the concept of self-regulated learning (SRL) came into picture to describe the need for learners to consistently improve their study method, and it was seen cited again in recent years due to the emergence of new technologies that have opened more windows to the learners to improve their studies. Nevertheless, there is a gap in the research on knowing who is performing the SRL and who is not. This has brought into the attention of presenting this paper on knowing what a learner is doing, using software agents to intelligently trace the learners' online learning activities. The purpose of this paper is to conceptualise an idea of having software agents to mediate the tasks of the learners, by performing personal knowledge management (PKM) processes at agent level. The expected outcome is a conceptual model of agents' interaction with their environment, and how to analyse the information or knowledge retrieved from the sources in order to provide a better report to their learners for their benefit of SRL.
Human resource management as key pillar of company strategy: Analysis of the line managers’ perception

The increasingly frequent involvement of companies’ line managers in human resource (HR) activities leads us to investigate the strategic value that these managers grant HR. This research focusses on the line managers’ perception of the strategic importance of HR, versus the perception of HR managers themselves, as a relevant factor in line managers’ own involvement within the execution of HR-related tasks. If line managers do not perceive the relevance and strategic importance of HR and its key role in the company’s strategy and performance, it is difficult to guarantee the managers’ commitment and effectiveness in HR management implementation. Based on interviews with HR development and line managers in 100 Spanish firms, we verified discrepancies in HR versus line managers’ perceptions, confirming that the assessment given by the line managers is slightly lower and, in turn, providing us with references to draw useful conclusions for a successful implementation of HR practices.
Block Management in Solid-State Devices

Solid-state devices (SSDs) have the potential to replace traditional hard disk drives (HDDs) as the de facto storage medium. Unfortunately, there are several decades of spinning-media assumptions embedded in the software stack as an "unwritten contract" [20]. In this paper, we revisit these system-level assumptions in light of SSDs and find that several of them are invalidated by SSDs, breaking the unwritten contract and resulting in poor performance and lifetime. The underlying cause is the incorrect division of labor between file systems and storage. Block management must be removed from the file system and delegated to the SSD to prevent further accumulation of storage-specific assumptions. We find that object-based storage is an appropriate way to achieve this.
Facilitating creativity without restrictions: a pilot implementation of an idea generation game

In this paper the design, pilot implementation and results from an idea generation game are discussed. The Neo-Darwinian and Neo-Lamarckian models of creativity provided by Johnson-Laird are used to discuss differences between this game and other idea generation games. The emphasis of the study is on the facilitation of emergence in idea generation games. Results of the pilot were inconclusive, but offer an insight into the kinds of issues which could be faced should this study be carried out in full.
What Papers Should I Cite from my Reading List? User Evaluation of a Manuscript Preparatory Assistive Task

Literature Review (LR) and Manuscript Preparatory (MP) tasks are two key activities for researchers. While process-based and technological-oriented interventions have been introduced to bridge the apparent gap between novices and experts for LR tasks, there are very few approaches for MP tasks. In this paper, we introduce a novel task of shortlisting important papers from the reading list of researchers, meant for citation in a manuscript. The technique helps in identifying the important and unique papers in the reading list. Based on a user evaluation study conducted with 116 participants, the effectiveness and usefulness of the task is shown using multiple evaluation metrics. Results show that research students prefer this task more than research and academic staff. Qualitative feedback of the participants including the preferred aspects along with critical comments is presented in this paper.
Viii. Statistical Communication Theory A. Phase Modulation in Nonlinear Filtering

h a series of orthogonal polynomials. This representation corresponds physically to a system conFig. VIII-1. Nonlinear filter. taining only multipliers as nonlinear elements. The suggestion offered in this report is to represent f by an orthogonal trigonometric series that corresponds physically to a system containing phase modulators. To simplify matters, let us first assume that the signals x 1 . xn (Fig. VIII-1) are distributed uniformly and independently over the interval (-Tr, 7r). Then the trigonometric series for f is
APPLICATION OF CLUSTERING ALGORITHMS IN EYE GAZE VISUALIZATIONS

Eye tracking devices generate enormous amount of data, which requires a well-balanced approach to selective visualization of the data. This approach involves employing some data clustering algorithm. Most of the tradi- tional algorithms, however, are too slow as well as inadequately deterministic to be applied to eye gaze data. This paper describes our software implementation of two modifications of the clustering algorithm suitable for visualization of eye gaze data. Such a visualization greatly facilitates data analysis by grouping the individual samples into more meaningful units referred to as gaze fixations.
A dynamically-allocated virtual channel architecture with congestion awareness for on-chip routers

In this paper, the dynamically-allocated virtual channels (VCs) architecture with congestion awareness is introduced. All the buffers are shared among VCs whose structure varies with traffic condition. In low rate, this structure extends VC depth for continual transfers to reduce packet latencies. In high rate, it dispenses many VCs and avoids congestion situations to improve the throughput. We modify the VC controller and VC allocation modules, while designing simple congestion avoidance logic. The experiment shows that the proposed routers outperform conventional ones under different traffic patterns. They provide 8.3% throughput increase and 19.6% latency decrease while saving 27.4% of area and 28.6% of power.
Consumer Behaviour in Fitness Club: Study of the Weekly Frequency of Use, Expectations, Satisfaction and Retention

This study aims to understand the relationship between the weekly frequency of use, expectations, satisfaction and retention of members in a fitness Clubs. In this market it is indispensable to understand the characteristics of faithful members, in order to act appropriately with each segment. Accordingly, the weekly frequency, which a member goes to the gym, their expectations, satisfaction and retention are key variables to understand them. Data was collected with a sample of 146 members. The instrument was constituted by the sample characterization and questions to understand the consumer behaviour in fitness (weekly frequency of use, expectations, satisfaction and retention behaviours). The statistical analysis consisted on descriptive and inferential analyses, using SPSS software. In this study the weekly frequency did not show the explanatory capacity to predict the satisfaction and retention. The relationship between expectations with satisfaction (73%) and retention (64%) was statistically significant. Finally, the relationship between satisfaction and retention (63%) was positive and significant. The results indicated that fitness club managers must act to keep those who are less likely to remain, who aren’t the ones who go less times a week to the club, but those who have a lower satisfaction and, consequently, lower retention.
An End to End Recognition for License Plates Using Convolutional Neural Networks

License Plate Recognition technology, as a critical part in Intelligent Transportation Systems, enjoys a broad application. LPR is a technology to read license plates (which include the character and the color) by performing vehicle detection and character recognition on surveillance videos or images. Recently, many different algorithms and models can be applied in license plate detection and character recognition. Yet given that the accuracy of the system will be impacted by image blurring caused by motion blur, low-resolution, low-luminosity, low-contrast and noisy point, concerns have been concentrated in the accuracy uncertainty of complex application scenarios impact. To address these problems, an end-to-end fast solution for number-plate recognition is proposed in this paper. The mainstream algorithm, i.e. Single Shot MultiBox Detector, is employed to auto-detect the location of the license plate. Also, to recognize consecutive characters in the detected license plate area, an end-to-end Convolutional Neural Network classification model is proposed here. The test dataset contains over 6 thousand vehicle images collected in different types of complex scenarios, which include low-resolution, low-luminosity and motion blurring. Besides, the solution proposed in this work is able to achieve 99% accuracy on the test dataset with average computing time being 30 fps, which suggests that the system can meet the requirements of real-time analysis.
Permanent and Transient Fault Tolerance for Reconfigurable Nano-Crossbar Arrays

This paper studies fault tolerance in switching reconfigurable nano-crossbar arrays. Both permanent and transient faults are taken into account by independently assigning stuck-open and stuck-closed fault probabilities into crosspoints. In the presence of permanent faults, a fast and accurate heuristic algorithm is proposed that uses the techniques of index sorting, backtracking, and row matching. The algorithm’s effectiveness is demonstrated on standard benchmark circuits in terms of runtime, success rate, and accuracy. In the presence of transient faults, tolerance analysis is performed by formally and recursively determining tolerable fault positions. In this way, we are able to specify fault tolerance performances of nano-crossbars without relying on randomly generated faults that is relatively costly regarding that the number of fault distributions in a crossbar grows exponentially with the crossbar size.
Parallelization of voxel based multiview stereo for arbitrarily configured viewpoints

Voxel based multiview stereo is a method for reconstructing a 3D scene from a set of images taken at different viewpoints by determining the voxels corresponding to object boundaries in a voxel space. To accelerate the voxel based multiview stereo, we propose a novel parallel method based on the voxel space division approach, which divides the voxel space into several domains and assigns them to multiple Processing Elements (PEs). The existing methods based on this approach cannot work well for viewpoints arranged in complex configurations, since their process decomposition increases communication between PEs. Unlike those methods, we take new process decomposition, where the calculation for every voxel is decomposed into its component for each viewpoint. Our method processes the calculations for each of two viewpoints in parallel applying the voxel space division, and then integrates their results. This method can reduce the amount of inter-PE communication for arbitrarily configured viewpoints.
Self efficiency and social influence of computer support collaborative learning teaching and learning blog

This paper is a report of self efficiency of student teachers conducting blog as their collaborative teaching and learning tool. The report analysis social influence of collaborative activities and its impact on the content development of teaching and learning blog. A number of 35 undergraduates of Bachelor of Education in Teaching English as Second Language (TESL) programme participated in the study. The participants were required to create a teaching and learning blog based on Computer Support Collaborative Learning (CSCL) definition. A questionnaire and SWOT analysis were design to collect data. The questionnaire items were analysed using Rasch Model Analysis. The findings revealed that self efficiency varies among individual and the hardest task to achieve in collaborative learning outcome is to give comments to others. Therefore collaborative strategy must be taught before any collaborative learning strategy is pursued in order to achieve quality learning outcome.
IEEE 802.11 Wi-Fi access point density estimation with capture-recapture models

The estimation of IEEE 802.11 Wi-Fi access point (AP) densities is an important cornerstone in deriving accurate models for the deployment structure of opportunistic wireless networks. Such densities are usually derived through large-scale wardriving-like measurement campaigns with COTS devices. Due to shielding, limited receiver sensitivity, and sampling density constraints, in general only a subset of Wi-Fi APs can be observed. Furthermore, repeated measurement campaigns show that even if an AP has been observed in one visit to a study area, it may not be observed in subsequent visits, due to small-scale deviations in the measurement locations and unavoidable changes in the radio environment such as moving vehicles and pedestrians. This motivates our study of the application of capture-recapture models to establish more accurate estimates of the actual number of APs in a study area. We approach this problem by first developing a general system model and mathematical framework for AP observability. As we assume temporally constant population sizes but potential inhomogeneities in observation probabilities, we then assess the performance of two applicable population density estimators, namely the Lincoln-Petersen and jackknife estimators, through a simulation study. We demonstrate the practical significance of the proposed capture-recapture methodology by applying it to a data set from an extensive urban Wi-Fi measurement campaign that we have carried out in Cologne, Germany, quantifying the achievable gains and the estimators' sensitivity to the measurement campaign design. We show that applying the capture-recapture techniques provides the practical advantage of yielding a similar accuracy in the estimation of Wi-Fi density even with significantly fewer measurement locations than surveyed in the full campaign. However, our results indicate that a high receiver sensitivity remains essential for such wardriving-like measurements, i.e. less sophisticated measurement setups such as smartphones will introduce high errors in the AP density estimation.
The discrete cosine transform (DCT) plus local normalization: a novel two-stage method for de-illumination in face recognition

To deal with illumination variations in face recognition, a novel two-stage illumination normalization method is proposed in this paper. Firstly, a discrete cosine transform (DCT) is used on the original images in logarithm domain. DC coefficient is set based on the average pixel value of all the within-class training samples and some low frequency AC coefficients are set to zero to eliminate illumination variations in large areas. Secondly, local normalization method, which can minimize illumination variations in small areas, is used on the inverse DCT images. This makes the pixel values on the processed images be close to or equal to that of the normal illumination condition. Experimental results, both on Yale B database and Extended Yale B database, show that the proposed method can eliminate effect of illumination variations effectively and improve performance of face recognition methods significantly. The present method does not demand modeling step and can eliminate the effect of illumination variations before face recognition. In this way, it can be used as a preprocessing step for any existing face recognition method.
Pseudo-binomial degree distributions from derivative matching

In this paper, a method to design check-concentrated LDPC degree distributions for the erasure channel is proposed. This method is obtained taking a derivative matching approach. It consists of matching the first and high-order derivatives of the variable node decoder EXIT function and inverse check node decoder EXIT function in order to reduce the gap between the two curves in the EXIT chart. A sufficient condition for a check-concentrated distribution to achieve derivative matching up to some order is first developed, and then a design algorithm is proposed exploiting this sufficient condition. A comparison with other deterministic design techniques is provided, revealing the potentialities of the proposed algorithm.