Miracle-dz/UniFAR-FTUs huggingface dataset:
FAN EMPIRICAL STUDY OF THE RELATIONSHIP BETWEEN ASSIGNMENT PRACTICES IN TRAINING PROGRAMS AND TRAINING MOTIVATION

The purpose of this study was to measure the relationship between training assignment and training motivation using self-report questionnaires collected from cadet officers in a military training academy, Malaysia. The outcomes of testing hypothesis using a stepwise regression analysis showed two important findings: firstly, mandatory assignment is significantly correlated with training motivation. Secondly, voluntary assignment is also significantly correlated with training motivation. This result demonstrates that training assignment practices act as important predictors of training motivation in the studied organization. In addition, this paper elaborates discussion, implications and conclusion.
Nighttime Brake-Light Detection by Nakagami Imaging

Given the rapid expansion of car ownership worldwide, vehicle safety is an increasingly critical issue in the automobile industry. The reduced cost of cameras and optical devices has made it economically feasible to deploy front-mounted intelligent systems for visual-based event detection for forward collision avoidance and mitigation. While driving at night, vehicles in front are generally visible by their taillights and brake lights. The brake lights are particularly important because they signal deceleration and potential collision. Therefore, in this paper, we propose a novel visual-based approach, based on the Nakagami-m distribution, for detecting brake lights at night by analyzing the taillights. Rather than using the knowledge of the heuristic features, such as the symmetry, position, and size of the rear-facing vehicle, we focus on finding the invariant features to model brake light scattering by Nakagami imaging and therefore conduct the detection process in a part-based manner. Experiments on an extensive data set show that our proposed system can effectively detect vehicle braking under different lighting and traffic conditions and thus demonstrates its feasibility in real-world environments.
Augmenting visual object classifiers with a full-image latent-topic model for object occurrence

We consider the visual object classification problem of labelling polygonal segments of an image according to the object each bounds, investigating whether the use of a topic model for objects within each image improves performance. We develop a basic model for image features generated from objects that does not aim for particularly high recognition accuracy but provides a basis for more complex models. Then, two models for the labelling of entire images are described — firstly, as a baseline, one that treats all the objects in an image as independently occurring; secondly, one that assumes they are related through a single latent topic, being drawn from a multinomial distribution conditioned on this. We discuss the relevant training and inference procedures for the two models, and compare their performance on a large dataset of photographs. Our results show that adding a topic model yields a small but statistically significant improvement in recognition accuracy of around 1.2%, provided the number of topics is reasonably well matched to the dataset. We discuss the implications of this, concluding that further work is required to determine the extent to which the improvement would still hold in the presence of both a more sophisticated model of image features, and a larger vocabulary of object classes.
The Forecast Performance of Long Memory and Markov Switching Models

Recent research has focused on the links between long memory and structural change, stressing the long memory properties that may arise in models with parameter changes. In this paper, we contribute to this research by comparing the forecasting abilities of long memory and Markov switching models. Two approaches are employed: a Monte Carlo study and an empirical comparison, using the quarterly Consumer Price inflation rate in Portugal in the period 1968-1998. Although long memory models may capture some in-sample features of the data, when shifts occur in the series considered, their forecast performance is relatively poor, when compared with simple linear and Markov switching models. Moreover, our findings, in a more general framework, are in accordance with the works of Clements and Hendry (1998) and Clements and Krolzig (1998), reinforcing the idea that simple linear time series models remain useful tools for prediction.
Influences of initial plankton biomass and mixed layer depths on the outcome of iron-fertilization experiments

Abstract. Several in situ iron-enrichment experiments have been conducted, where the response of the phytoplankton community differed. We use a marine ecosystem model to investigate the effect of iron on phytoplankton in response to different initial plankton conditions and mixed layer depths. Sensitivity analysis of the model results to the mixed layer depths reveals that the modeled response to the same iron enhancement treatment differed dramatically according to the different mixed layer depth. The magnitude of the iron-induced biogeochemical responses in the surface water, such as maximum chlorophyll, is inversely correlated with the mixed layer depth, as observed. The significant decrease in maximum surface chlorophyll with mixed layer depth results from the difference in diatom concentration in the mixed layer, which is determined by vertical mixing. Sensitivity of the model to initial mesozooplankton (as grazers on diatoms) biomass shows that column-integrated net community production and export production are strongly controlled by the initial mesozooplankton biomass. Higher initial mesozooplankton biomass yields high grazing pressure on diatoms, which results in less accumulation of diatom biomass. The initial diatom biomass is also important to the outcome of iron enrichment but is not as crucial as the mixed layer depth and the initial mesozooplankton biomass. This modeling study suggests not only mixed layer depth but also the initial biomass of diatoms and its principle grazers are crucial factors in the response of the phytoplankton community to the iron enrichments, and should be considered in designing future iron-enrichment experiments.
Robust connected word speech recognition using weighted viterbi algorithm and context-dependent temporal constraints

This paper addresses the problem of connected word speech recognition with signals corrupted by additive and convolutional noise. Context-dependent temporal constraints are proposed and compared with the ordinary temporal restrictions, and used in combination with the weighted Viterbi algorithm which had been tested with isolated word recognition experiments in previous papers. Connected-word recognition tests show that the weighted Viterbi algorithm depends on the accuracy of the state duration modelling and the approach here covered can lead to reductions as high as 90 or 95% in the error rate at moderate SNR using spectral subtraction, an easily implemented technique, even with a poor estimation for noise and without using any information about the speaker. It is also shown that the weighting procedure can reduce the error rate when cepstral mean normalization is also used to cancel both additive and convolutional noise.
Rknots: topological analysis of knotted biopolymers with R

MOTIVATION Rknots is a flexible R package providing tools for the detection and characterization of topological knots in biological polymers. The package is well documented and provides a simple syntax for data import and preprocessing, structure reduction, topological analyses and 2D and 3D visualization. Remarkably, Rknots is not limited to protein knots and allows researchers from interdisciplinary fields to analyze different topological structures and to develop simple yet fully custom pipelines.
Usability of Pilot's Gaze in Aeronautic Cockpit for Safer Aircraft

With increasing complexity of aircraft equipment, the demands on the aircraft crew are unprecedented. The modern cockpit must adjust its pace of communication for optimal workload of the crew and ensure that the information presented to the pilot is actually received. We contribute to the solution of these problems by designing a passive (purely video-based and unobtrusive) gaze tracking solution. It allows for detection of the pilot missing a piece of information communicated by the cockpit equipment, and for estimation of pilot's workload and immediate focus. We collected a dataset at an aeronautic simulator by using the OptiTrack tracking equipment for development and evaluation of similar gaze tracking systems and we make it publicly available. The experiments show that proposed algorithms for passive and unobtrusive gaze estimation work satisfactorily for the targeted purposes.
Distributed Semantic Content Creation and Publication for Cultural Heritage Legacy Systems

Cultural heritage is by nature strongly interlinked, e.g. thematically and historically, but at the same time distributed in heterogeneous collections of different memory organizations at different locations. In order to provide the end-users with aggregated homogeneous views to distributed heterogeneous contents, semantic portals have been created successfully based on metadata and shared (or aligned) ontologies. This paper discusses two problems encountered in such a distributed semantic content creation environment. First, during the content creation work, how could a publisher start using shared ontologies in legacy cataloguing and annotation systems that do not support ontologies. Second, during content publication, how could a publisher re-use the aggregated content in its own legacy publication system, e.g., on the ordinary web pages of a museum or in a collection browser. As a solution, we present the ONKI Ontology Server for adding shared ontological annotation functionalities to legacy cataloguing systems in a practical, cost-efficient and lightweight way. For distributed publishing of the aggregated semantic portal services, we introduce the lightweight mash-up web widget components called “floatlets”. A major idea behind both the ONKI functionalities and floatlets is that they can be easily integrated with legacy systems on the user interface level, in the same spirit as e.g. Google Maps.
Towards a theory of random walk planning: Regress factors, fair homogeneous graphs and extensions

Random walks are a relatively new component used in several state of the art satisficing planners. Empirical results have been mixed: while the approach clearly outperforms more systematic search methods such as weighted A* on many planning domains, it fails in many others. So far, the explanations for these empirical results have been somewhat ad hoc. This paper proposes a formal framework for comparing the performance of random walk and systematic search methods. Fair homogenous and Infinitely Regressable homogenous graphs are proposed as graph classes that represents characteristics of the state space of prototypical planning domains, and is simple enough to allow a theoretical analysis of the performance of both random walk and systematic search algorithms. This gives well-founded insights into the relative strength and weaknesses of these approaches. The close relation of the models to some well-known planning domains is shown through simplified but semi-realistic planning domains that fulfill the constraints of the models. One main result is that in contrast to systematic search methods, for which the branching factor plays a decisive role, the performance of random walk methods is determined to a large degree by the Regress Factor, the ratio between the probabilities of progressing towards and regressing away from a goal with an action. The performance of random walk and systematic search methods can be compared by considering both branching and regress factors of a state space.
Mechanism of Formation of Vision Based on Learning of Correlation between Sensation and Motion

In this paper, we aim to support the scheme that visual cognition is organized by natural neural networks based on experiences. In this scheme the networks learn correlation between temporal variation of visual stimulation and self-motion to organize visual cognition. We propose a neural network that detects temporal variations of visual stimulation and combine them with corresponding self-motion. The network does not use any prior knowledge about an image such as optical process of formation or pixels arranged in two dimensional plane. By experiments, we show that the proposed network can combine sensation and motion correctly and can recall pattern of one side by input pattern of the other side.
Adaptive optics imaging survey of the Tucana-Horologium association

We present the results of an adaptive optics (AO) imaging survey of the common associations of Tucana and Horologium, carried out at the ESO 3.6 m telescope with the ADONIS/SHARPII system. Based on our observations of two dozen probable association members, HIP 1910 and HIP 108422 appear to have low-mass stellar companions, while HIP 6856 and GSC 8047-0232 have possible sub-stellar candidate companions. Astrometric measurements, performed in November 2000 and October 2001, indicate that HIP 1910 B likely is bound to its primary, but are inconclusive in the case of the candidate companion to HIP 6856. Additional observations are needed to confirm the HIP 6856 companionship as well as for HIP 108422 and GSC 8047-0232.
Classification Based on Pruning and Double Covered Rule Sets for the Internet of Things Applications

The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy.
An Ultrasonic Navigation System for Blind People

The aim of this paper is to investigate the development of a navigation aid for blind and visually impaired people. It is based on a microcontroller with synthetic speech output. This aid is portable and gives information to the user about urban walking routes to point out what decisions to make. On the other hand, and in order to reduce navigation difficulties of the blind, an obstacle detection system using ultrasounds and vibrators is added to this device. The proposed system detects the nearest obstacle via stereoscopic sonar system and sends back vibro-tactile feedback to inform the blind about its localization.
Is There Increasing Regional Specialisation within the General Process of Deindustrialisation?

Trade theory and economic geography suggest that the removal of trade barriers is likely to bring about more economic specialisation and potentially more diverse development paths between countries and regions. Thus, the deepening and extending European integration should be accompanied by an increasing regional specialisation. In contrast, our results for the period from 1995 to 2004 show considerably declining differences in the share of manufacturing in total value added across nations and regions of the EU. The decrease in sectoral specialisation is accompanied by a strong and almost uniform process of deindustrialisation. However, this trend is slowing down and manufacturing shares appear to be gradually approaching lower limits. These bounds are specific according to national affiliation and settlement types of regions.
System Combination for Machine Translation Using N-Gram Posterior Probabilities

This paper proposes using n-gram posterior probabilities, which are estimated over translation hypotheses from multiple machine translation (MT) systems, to improve the performance of the system combination. Two ways using n-gram posteriors in confusion network decoding are presented. The first way is based on n-gram posterior language model per source sentence, and the second, called n-gram segment voting, is to boost word posterior probabilities with n-gram occurrence frequencies. The two n-gram posterior methods are incorporated in the confusion network as individual features of a log-linear combination model. Experiments on the Chinese-to-English MT task show that both methods yield significant improvements on the translation performance, and an combination of these two features produces the best translation performance.
Sites by Parallel DNA Capturing and Sequencing Genome-Wide Identification of Human RNA Editing

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Locality and Attachedness-based Temporal Social Network Growth Dynamics Analysis A case study on evolving nanotechnology scientific networks

The rapid advancement of nanotechnology research and development during the past decade presents an excellent opportunity for a scientometric study because it can provide insights on the dynamic growth of the fast evolving social networks associated with this exciting field. In this paper we conduct a case study on nanotechnology in order to discover the dynamics that govern the growth process of rapidly advancing scientific collaboration networks. This paper starts with the definition of temporal social networks and demonstrates that the nanotechnology collaboration network, in resemblance of other real-world social networks, exhibits a set of intriguing static and dynamic topological properties. Inspired by the observations that in collaboration networks new connections tend to be augmented between nodes in proximity, we explore the locality factor and the attachedness factor in growing networks. In particular, 2 Haizheng Zhang et al. we develop two distance-based computational network growth schemes, namely DG and DDG. The DG model considers only locality element while the DDG is a hybrid model that factors into both locality and attachedness elements. The simulation results of these models indicate that both clustering coefficient rates and the average shortest distance are closely related to the edge densification rates. In addition, the hybrid DDG model exhibits higher clustering coefficient values and decreasing average shortest distance when the edge densification rate is fixed, which implies that combining locality and attachedness can better characterize the growing process of the nanotechnology community. Based on the simulation results we conclude that social network evolution is related to both attachedness and locality factors.
Regulation of the Pancreatic Islet-Specific GeneBETA2 (neuroD) by Neurogenin 3

ABSTRACT The BETA2 (neuroD) gene is expressed in endocrine cells during pancreas development and is essential for proper islet morphogenesis. The objective of this study is to identify potential upstream regulators of the BETA2 gene during pancreas development. We demonstrated that the expression of neurogenin 3 (ngn3), an islet- and neuron-specific basic-helix-loop-helix transcription factor, partially overlaps that of BETA2 during early mouse development. More importantly, overexpression of ngn3 can induce the ectopic expression of BETA2 in Xenopus embryos and stimulate the endogenous RNA of BETA2 in endocrine cell lines. Furthermore, overexpression of ngn3 could cause a dose-dependent activation on the 1.0-kb BETA2 promoter in islet-derived cell lines. Deletion and mutation analyses revealed that two proximal E box sequences, E1 and E3, could bind to ngn3-E47 heterodimer and mediate ngn3 activation. Based on these results, we hypothesize that ngn3 is involved in activating the expression of BETA2 at an early stage of islet cell differentiation through the E boxes in theBETA2 promoter.
Predicting the Winner in One Day International Cricket

Winning a One Day International (ODI) cricket match depends on various factors related to scoring as well as the athletic strengths of the two teams. While some of these factors have been well analyzed in the literature, others have yet to be investigated. In this analysis, statistical significance for a range of variables that could explain the outcome of an ODI cricket match is explored. In particular, home field advantage, winning the toss, game plan (batting first or fielding first), match type (day or day & night), and the effect of the Duckworth-Lewis method for matches shortened due to weather interruptions will be key interests in our investigation. The comparison of the accuracy of the Duckworth-Lewis method is done by receiver operating characteristics (ROC) curves. For purposes of model-building, logistic regression is applied retrospectively to data already obtained from previously played matches. Some surprising results emerge.
Effects of the Unscented Kalman Filter Process for High Performance Face Detector

—This paper concerns with a high performance algorithm for human face detection in still image. The algorithm has been developed for increasing the face detection rate under different environmental conditions. In this paper, the skin color detector is modified by combining with the low pass filter, Sobel edge detector and the modified Viola Jones eye detector. The Haar cascade classifier is modified by using the Clustering algorithm. The modified algorithms are then combined together with the Unscented Kalman filter (UKF) in our process. The use of the UKF in case of the face detection algorithm simply reflects the novelty of our paper. The UKF is used for removing the film grain noise from the still image. To clarify the effectiveness of our proposed algorithm, we compare our proposed algorithm with other face detection algorithms through the benchmark tests using different facial databases. The ROC curve clarifies the effectiveness of our proposed algorithm with best face detection results of 98.3%.