Miracle-dz/UniFAR-FTUs huggingface dataset:
Benefit Perceived by Tourists. Role of the Hospitality Offered by the Tourist Destination

The aim of this study is to test the relationship between the functional benefit, symbolic benefit and affective evaluation as antecedent of the hedonic benefit and consequence of cognitive perception. The aim is to test the mediating effect of hedonic benefit as an antecedent of tourist satisfaction. This objective seeks to achieve, through a methodology which includes developing scales with a degree of content validity and analyze its psychometric properties. A survey of 750 people was conducted using a non-probability sampling based on quotas, in proportion to destinations visited in Chile. The results show a significant mediating effect on hedonic benefit on tourist satisfaction. The study provides the different stakeholders of the tourist destination with relevant decision making information relevant for tourism marketing in order to provide the desired satisfaction for the tourist.
Distributed topology construction of Bluetooth personal area networks

Wireless ad hoc networks have been a growing area of research. While there has been considerable research on the topic of routing in such networks, the topic of topology creation has not received due attention. This is because almost all ad hoc networks to date have been built on top of a single channel, broadcast based wireless media, such as 802.11 or IR LANs. For such networks the distance relationship between the nodes implicitly (and uniquely) determines the topology of the ad hoc network. Bluetooth is a promising new wireless technology, which enables portable devices to form short-range wireless ad hoc networks and is based on a frequency hopping physical layer. This fact implies that hosts are not able to communicate unless they have previously discovered each other by synchronizing their frequency hopping patterns. Thus, even if all nodes are within direct communication range of each other, only those nodes which are synchronized with the transmitter can hear the transmission. To support any-to-any communication, nodes must be synchronized so that the pairs of nodes (which can communicate with each other) together form a connected graph. Using Bluetooth as an example, this paper first provides deeper insights into the issue to link establishment in frequency hopping wireless systems. It then introduces the Bluetooth topology construction protocol (BTCP), an asynchronous distributed protocol for constructing scatternets which starts with nodes that have no knowledge of their surroundings and terminates with the formation of a connected network satisfying all connectivity constraints posed by the Bluetooth technology. To the best of our knowledge, the work presented in this paper is the first attempt at building Bluetooth scatternets using distributed logic and is quite "practical" in the sense that it can be implemented using the communication primitives offered by the Bluetooth 1.0 specifications.
Passive copy-move forgery detection using SIFT, HOG and SURF features

Copy-move is a common type of digital image forgery. In an image, Copy-Move tampering might be done to hide an undesirable region or to duplicate something in the image. These images might be used for the necessary purpose like evidence in the court of law. So, authenticity verification plays a vital role for digital images. In this paper, we compare the CMFD (Copy-Move Forgery Detection) using Image features like SIFT (Scale Invariant Features Transform), HOG (Histogram Oriented Gradient) and SURF (Speed-Up Robust Features) and hybrid features (SURF-HOG and SIFT-HOG). The comparison results show that CMFD using SIFT features provide better results as compared with SURF and HOG features. Also, considering hybrid features, SIFT-HOG and SURF-HOG produce better results for CMFD using SIFT, SURF or HOG alone.
Foundational competencies for enhancing work engagement in SMEs Malaysia

Purpose This study aims to examine the impact of foundational competencies on work engagement in the context of the Malaysian small and medium enterprises (SMEs) service sector. Design/methodology/approach This is a cross-sectional study conducted in Malaysia, and data were gathered via a survey on 312 employees representing SMEs service sectors. Partial least squares techniques were used in testing the hypothesized linkages. Findings The findings indicated that foundational competencies have a significant positive effect on work engagement. Research limitations/implications Considering the research approach, generalizing the findings of this study must be done with caution. Longitudinal data are recommended, as these could provide additional support to the results. Further studies can as well extend the framework by testing whether job resources play a positive role in enhancing the relationship between foundational competencies and work engagement in Malaysia. Practical implications The findings suggest that foundational competencies positively relate to work engagement. Therefore, training and coaching sessions could be organized by managers/policy makers to enhance these competencies, which will strengthen work engagement and career development of the employee; it may specifically be very helpful for fresh graduates whose careers would have just started. Originality/value To the authors’ knowledge, this is the first attempt to examine the effect of American Society of Training and Development foundational competencies on work engagement in SMEs in Malaysia.
Intergenerational Relationships and Psychological Well-Being of Chinese Older Adults With Migrant Children: Does Internal or International Migration Make a Difference?

Relying on a purposive sample of 550 Chinese older adults in Beijing, China, this study examined the potential influence of children’s out-migration, internally or internationally, on intergenerational relationships and parental well-being. By comparing older adults in nonmigrant, internal migrant, transnational, and hybrid (have both migrant and nonmigrant children) families, we found that children’s out-migration only compromised intergenerational contact and instrumental support, but not monetary or emotional support. Older adults of different family types had similar levels of depression and life satisfaction. Those in internal migrant families were most likely to report financial worries, and those in transnational families were most likely to worry about lack of care. The influence of children’s out-migration was further confounded by parents’ coping resources. The findings suggest that the vulnerability of the “left-behind” elderly might have been exaggerated. Future studies shall distinguish different types of migration and the diversity among older adults with migrant children.
Enhanced Context-Aware Architecture for Ubiquitous Computing

Privacy is the ability of an individual to keep related information, out of public view, traditionally, privacy enhancing techniques have been used as a tool for hiding and control disclosure. It is necessary to think about how tech nology can be used to create visibility and awarene ss of information. I proposed an architecture in which a user can configure his personal preferences for privacy preservation functionalities and services, once he enters an UbiComp environment. The design is based on an analysis of various approaches to pr ivacy protection for UbiComp settings. A composite entity called “privon” is introduced to encapsulate related information based on their privacysensitivity levels. A privon allow users to underst and how their personal information may be used by others.
Targeted Inactivation of Dipeptidyl Peptidase 9 Enzymatic Activity Causes Mouse Neonate Lethality

Dipeptidyl Peptidase (DPP) 4 and related dipeptidyl peptidases are emerging as current and potential therapeutic targets. DPP9 is an intracellular protease that is regulated by redox status and by SUMO1. DPP9 can influence antigen processing, epidermal growth factor (EGF)-mediated signaling and tumor biology. We made the first gene knock-in (gki) mouse with a serine to alanine point mutation at the DPP9 active site (S729A). Weaned heterozygote DPP9wt/S729A pups from 110 intercrosses were indistinguishable from wild-type littermates. No homozygote DPP9S729A/S729A weaned mice were detected. DPP9S729A/S729A homozygote embryos, which were morphologically indistinguishable from their wild-type littermate embryos at embryonic day (ED) 12.5 to ED 17.5, were born live but these neonates died within 8 to 24 hours of birth. All neonates suckled and contained milk spots and were of similar body weight. No gender differences were seen. No histological or DPP9 immunostaining pattern differences were seen between genotypes in embryos and neonates. Mouse embryonic fibroblasts (MEFs) from DPP9S729A/S729A ED13.5 embryos and neonate DPP9S729A/S729A mouse livers collected within 6 hours after birth had levels of DPP9 protein and DPP9-related proteases that were similar to wild-type but had less DPP9/DPP8-derived activity. These data confirmed the absence of DPP9 enzymatic activity due to the presence of the serine to alanine mutation and no compensation from related proteases. These novel findings suggest that DPP9 enzymatic activity is essential for early neonatal survival in mice.
51 years of the Arboricultural Journal; a commentary

This paper provides a brief overview of the Arboricultural Journal from its inception in 1965 until 2015, with comments on some of the main topics, and thoughts on the development of arboriculture over this time, together with the role of the journal, both now and in the future. There were 163 issues of the journal over this period, and it has not been possible to mention every single item or topic, so the comments which follow are inevitably selective, and there may be some bias towards the subjects that have been of greatest interest to the reviewer or which appear to be of particular importance.
Image-Based Rendering for Teleconference Systems

To obtain an image-based immersive presence in a virtual world, two important factors should be considered: system configuration and multiview representation. We present two non-adversary system configurations. The first is the well-known convergent wide-baseline set-up while the second is a unique proposal under investigation at our institute, which is based around a parallel multiple narrow-baseline camera set-up. In the domain of multiview representation we introduce two non-conflicting representations that can be implemented independent of the chosen system configuration, dependent on whether compression or scalability is important to the overall system. We then discuss our implementation of an image-based rendering system for an immersive teleconferencing application where three conferees meet around a shared virtual table. The system uses a wide-baseline configuration with two stereo camera pairs capturing the reference images. The system is designed to deal with hand gestures as well as the synthesis of areas occluded in one or more of the reference images but required in the derived view. We introduce the notion of a confidence map designed to indicate, for the derived image, which reference image should provide the required texture and disparity information for a surface.
Optimization of the response of magnetoresistive elements

A way to optimize the output signal of a general thin-film magnetoresistive element with a homogeneous magnetization field as used in applications with a saturating external magnetic field is presented. The element is assumed to be operated by four-point measurement. In order to be able to compare different elements, a figure of merit is defined. The general theory of the anisotropic magnetoresistance (AMR) effect is treated, and a few general rules for optimization are formulated. It is concluded that in order to obtain a maximum signal voltage amplitude, the current density in the elements should be constant, i.e. not affected by the AMR effect. It is shown, that the AMR effect on the current density in the element usually cannot be neglected. Some special configurations of magnetoresistive elements are treated in detail. The problem of four point contacts in an infinitely wide thin film is solved analytically, with the aid of a special transformation. It is found, that there is an optimum thickness of the thin film in an AMR device, which depends on the material and the deposition technique. For pseudo-Hall elements, an optimum length-to-width ratio is found ( approximately=1.35). >
Computational problems in multiagent systems

There has been recent interest from the computer science community on multi-agent systems, where multiple autonomous agents, each with their own utility functions, act according to their own interests. In this thesis, we apply techniques developed in other areas of CS to solve two computational problems in multiagent systems: Action Graph Games: Action Graph Games (AGGs), first proposed in [Bhat and Leyton-Brown 2004], are a fully expressive game representation which can compactly express strict and context-specific independence and anonymity structure in players' utility functions. We present an efficient algorithm for computing expected payoffs under mixed strategy profiles. This algorithm runs in time polynomial in the size of the AGG representation (which is itself polynomial in the number of players when the in-degree of the action graph is bounded). We also present an extension to the AGG representation which allows us to compactly represent a wider variety of structured utility functions. Learning and planning in online auction environments: There is much active research into the design of automated bidding agents, particularly for environments that involve multiple auctions. These settings are complex partly because an agent's optimal strategy depends on information about other bidder's preferences. When bidders' valuation distributions are not known ex ante, machine learning techniques can be used to approximate them from historical data. It is a characteristic feature of auctions, however, that information about some bidders' valuations is systematically concealed. This occurs in the sense that some bidders may fail to bid at all because the asking price exceeds their valuations , and also in the sense that a high bidder may not be compelled to reveal her valuation. Ignoring these " hidden bids " can introduce bias into the estimation of valuation distributions. To overcome this problem, we proposed an EM-base algorithm. We validate the algorithm experimentally using agents that react to their environments both decision-theoretically and game-theoretically, using both synthetic and real-world (eBay) datasets. We show that our approach estimates bidders' valuation distributions and the distribution over the true number of bidders significantly more accurately than more straightforward density estimation techniques. Bidding agents using the estimated distributions from our EM approach were able to outperform bidding agents using the straightforward estimates, in both decision-theoretic and game-theoretic settings.
Advanced Feature Normalization and Rapid Model Adaptation for Robust In-Vehicle Speech Recognition

In this study, we present advanced feature normalization and rapid model adaptation for robust in-vehicle speech recognition. For feature normalization, we use a combination of recently established quantile-based cepstral dynamics normalization (QCN) and low pass temporal filtering (RASTALP). Similar to cepstral mean normalization (CMN), QCN aims at alleviating the mismatch between ASR acoustic models and the decoded speech signal. QCN relaxes CMN assumptions concerning feature distributions, making the normalization more stable in varying adverse environments. RASTALP is a low-pass approximation of RASTA filtering which significantly reduces transient distortions introduced by the original band-pass filter. Using the normalized features, we adapt the speaker-independent acoustic model to specific speakers. The adaptation method is based on an aspect model (a “mixture-ofmixtures” model). To enable adaptation requiring only extremely small amounts of adaptation data (i.e., a few seconds), we train a small number of mixture models which can be interpreted as models of probabilistic “speaker clusters” for in-vehicle environments. In this work, we use fMLLR to represent individual speaker models. The speaker models are mixed using weights determined from adaptation data. Experimental results show that the normalization employing QCN-RASTALP is consistently superior to CMN. We also observe that in contrast to the conventional methods, the adaptation based on the aspect model improves word error rates for the in-vehicle noise environments.
An asymmetric stochastic language model based on multi-tagged words

Tag definition in stochastic language models (n-grams and npos) is based on grouping together words with similar right and left context behavior. In [2] a modification of the n-gram model for French using multi-tagged words and unsupervised clustering was introduced. Their corpus was millions of non-tagged words. We present a variation of bi-pos language model where two tag sets are defined and assigned to each word (multi-tagged model) using grammatical information. Each tag set is based on different context behavior. We use linguistic expert knowledge and a simple automatic clustering procedure to obtain groups of words with similar left context behavior (first set of tags) and with similar right context (second set of tags). We propose a grammatical based model useful when no big text corpus is available and a performance increase has been observed when multi–tagged words are used because of its better adaptation to the language.
Unsupervised Risk Estimation with only Structural Assumptions

Given a model θ and unlabeled samples from a distribution p∗, we show how to estimate the labeled risk of θ while only making structural (i.e., conditional independence) assumptions about p∗. This lets us estimate a model’s test error on distributions very different than its training distribution, thus performing unsupervised domain adaptation even without assuming the true predictor remains constant (covariate shift). Furthermore, we can perform discriminative semi-supervised learning, even under model mis-specification. Our technical tool is the method of moments, which allows us to exploit conditional independencies without relying on a specific parametric model. Finally, we introduce a new theoretical framework for grappling with the non-identifiability of the class identities fundamental to unsupervised learning.
A High-Speed/Low-Power Multiplier Using an Advanced Spurious Power Suppression Technique

This study provides the experience of applying an advanced version of our former spurious power suppression technique (SPST) on multipliers for high-speed and low-power purposes. To filter out the useless switching power, there are two approaches, i.e. using registers and using AND gates, to assert the data signals of multipliers after the data transition. The simulation results show that the SPST implementation with AND gates owns an extremely high flexibility on adjusting the data asserting time which not only facilitates the robustness of SPST but also leads to a 40% speed improvement. By adopting a 0.18-mum CMOS technology, the proposed SPST-equipped multiplier dissipates only 0.0121 mW per MHz in H.264 texture coding applications, and obtains a 40% power reduction.
Three-Level Gray-Scale Images Segmentation using Non-extensive Entropy

The segmentation of images into meaningful and homogenous regions is a crucial step in many image analysis applications. In this paper, we present a three-level thresholding method for image segmentation. The method is based on maximizing non-extensive entropy. After segmentation, the output image will consist of three homogenous regions, namely, dark, gray and white. The threshold value for each region is decided by maximizing an extended form of Tsallis entropy. To improve the performance of the proposed algorithm, an efficient and computationally fast method for initializing the search for maximum entropy is also presented. Results obtained using the proposed algorithm are compared with those obtained using Shannon entropy.
Unsupervised Semantic Action Discovery from Video Collections

Human communication takes many forms, including speech, text and instructional videos. It typically has an underlying structure, with a starting point, ending, and certain objective steps between them. In this paper, we consider instructional videos where there are tens of millions of them on the Internet. We propose a method for parsing a video into such semantic steps in an unsupervised way. Our method is capable of providing a semantic "storyline" of the video composed of its objective steps. We accomplish this using both visual and language cues in a joint generative model. Our method can also provide a textual description for each of the identified semantic steps and video segments. We evaluate our method on a large number of complex YouTube videos and show that our method discovers semantically correct instructions for a variety of tasks.
INVESTIGATING EARLY SPATIAL AND NUMERICAL SKILLS IN JUNIOR KINDERGARTEN CHILDREN LEARNING IN AN INQUIRY- AND PLAY-BASED ENVIRONMENT

In the current study, three possible interpretations of children’s number line estimation (NLE) performance were examined for appropriateness and possible correlates of performance were tracked over time in a classroom exemplifying recommended mathematics pedagogy for young children. In December and May, 21 4-year-olds completed the NLE task (0-10 range) and measures of numerical knowledge, spatial skills, and visual-motor integration. With high-quality teaching, children made large gains in these skills (d = 0.96– 1.28). Due to uniformly high achievement, few expected correlations were observed, however. A strategy account of NLE performance was supported over the traditional logarithmic-to-linear shift account and the newly proposed proportion-judgement account. Patterns of error in estimation provide a better indication of understanding of the linear number line than models of best fit. Indeed, interpreting linearity of NLE as indicative of an underlying representation of number could lead to inappropriate conceptualizations of math learning disabilities and misguided interventions.
Composite self-concordant minimization

We propose a variable metric framework for minimizing the sum of a self-concordant function and a possibly non-smooth convex function, endowed with an easily computable proximal operator. We theoretically establish the convergence of our framework without relying on the usual Lipschitz gradient assumption on the smooth part. An important highlight of our work is a new set of analytic step-size selection and correction procedures based on the structure of the problem. We describe concrete algorithmic instances of our framework for several interesting applications and demonstrate them numerically on both synthetic and real data.