Miracle-dz/UniFAR-FTUs huggingface dataset:
DESIGNING A CARTOGRAPHIC ONTOLOGY FOR USE WITH EXPERT SYSTEMS

The democratization of mapping represents a positive trend for society, however within this movement, little focus to cartographic quality has been paid. The result is often poorly presented maps which fail to convey the information intended. Software packages offer very little, if any, guidance to the user when it comes to offering cartographic assistance such as symbolization recommendations. Additionally, users often do not read software manuals, and, instead, opt to solve the problem by trial and error or ask an available expert. The combination of scarce cartographic documentation and the resistance of users to use documentation present an opportunity for expert systems to provide better assistance through a non-passive help system. The paper presents the development of ontologies and their potential contribution to the construction and successful execution of an expert system. Traditionally, expert systems learned the situation through a dialog with the user after which it would infer an expert answer. A problem with this model is that if the users do not have sufficient knowledge of cartography to answer these questions, the expert system will be less effective at helping. If the expert system can understand the situation without querying the user, it can make proactive inferences, thus, reducing the burden on the user and reducing the amount of time spent interacting with the expert system. To effectively replace user input to an expert system, it must be able to understand the concept of a map. The construction of an ontology will provide this understanding.
Multilevel Phase-Change Memory Modeling and Experimental Characterization

Phase-change memory (PCM) has emerged in recent years as the most promising technology for non-volatile memory due to its very high throughput performance and read/write endurance as well as future scalability. Multilevel functionality is crucial for increasing the capacity and thus enhancing the cost per GByte competitiveness of the PCM technology. However, storage of multiple resistance levels in a PCM cell is a challenging problem; issues like process variability, as well as intra-cell and inter-cell material parameter variations give rise to deviations in the achieved resistance levels from their intended target values. Therefore, iterative programming schemes are required employing multiple write-verify steps until the desired resistance level is reached. A thorough understanding of the characteristics of the PCM cells is essential for the design of an effective programming algorithm. In this paper, we present a systems-based model that captures the essential behavior of the PCM cell. In particular, the model captures the interplay between the electrical, thermal and phase-change parts of the system. Experimental results at various intermediate cell states were utilized to characterize the PCM cell behavior and fit the model parameters. The systemsbased model of the PCM cell is a powerful tool for the design of efficient iterative multilevel programming algorithms. Finally, schemes that adaptively control parameters of the programming pulse in an iterative manner are presented, and experimental results on PCM cells demonstrating the efficacy of the presented programming schemes in achieving multilevel functionality are discussed.
UNSUPERVISED NONLINEAR FEATURE EXTRACTION METHOD AND ITS EFFECTS ON TARGET DETECTION IN HIGH-DIMENSIONAL DATA

The principal component analysis (PCA) is one of the most effective unsupervised techniques for feature extraction. To extract higher order properties of data, researchers extended PCA to kernel PCA (KPCA) by means of kernel machines. In this paper, KPCA is applied as a feature extraction procedure to dimension reduction for target detection as a preprocessing on hyperspectral images. Then the detection was done with a support vector data description (SVDD) algorithm which is another type of one-class support vector machines (SVMs). The SVDD constructs a minimum hypersphere enclosing the target objects as much as possible. For the supervised learning, SVDD has been trained with a training set which has been chosen from target class. Balanced classification rate (BCR) and F-measure have been used to evaluate the performance of proposed technique against full-band target detection. The experimental results on hyperspectral data from the HYDICE sensors show that the KPCA based dimension reduction offers high performance for target detection applications by SVDD. Index Terms—Feature extraction, hyperspectral imagery, kernel principal component analysis, support vector data description, target detection.
Two layer optimal control for a class of “gray-box” system, theory and experiment

This paper presents a two layer optimal control scheme with a control layer and an optimize layer. The control layer realize the close-loop control while the optimize layer is used to optimize the control parameters. The optimize layer identify the model of plant on-line and optimize the control parameters based on the identified model. Because the optimize layer is running parallel with controller therefore, the two layer controller is able to adapt plant's slow time-variant. The real system will stable and reliable due to the optimization process is only acting on the identified model and only stable and reliable control parameter will update to the real controller. This study only consider the class of “gray-box” system which plant model is clear except the model parameter is unknown. Both of model parameters identification and control parameters optimization are realized by Particle Swarm Optimization (PSO) algorithm. An experiment study of output voltage control of a second order electronic filter is presented. The experiment results demonstrate that the two layer optimal control scheme has ability of control parameter optimization.
Wireless Power Transfer Using Class E Inverter With Saturable DC-Feed Inductor

Resonant converters used as coil drivers in inductive links generally operate efficiently at optimum switching conditions for constant load values and ranges. Changes in load and range can shift the operation of the coil driver to a nonoptimum switching state which results in higher switching losses and reduced output power levels. This paper presents a method to adapt to variations in range for a Class E inverter used as a coil driver in a wireless power transfer (WPT) system based on inductive coupling. It is shown that by controlling the duty cycle of the inverter's switch and the value of its dc-feed inductance, the Class E inverter can be tuned to operate at optimum switching conditions as the distance between the coils of the WPT system changes. Mathematical analysis is presented based on a linear piecewise state-space representation of the inverter and the inductive link. Extensive experimental results are presented to verify the performed analysis and validity of the proposed tuning procedure.
On the application of predictive control techniques for adaptive performance management of computing systems

This paper addresses adaptive performance management of real-time computing systems. We consider a generic model-based predictive control approach that can be applied to a variety of computing applications in which the system performance must be tuned using a finite set of control inputs. The paper focuses on several key aspects affecting the application of this control technique to practical systems. In particular, we present techniques to enhance the speed of the control algorithm for real-time systems. Next we study the feasibility of the predictive control policy for a given system model and performance specification under uncertain operating conditions. The paper then introduces several measures to characterize the performance of the controller, and presents a generic tool for system modeling and automatic control synthesis. Finally, we present a case study involving a real-time computing system to demonstrate the applicability of the predictive control framework.
An Advanced Cascade Controller for Series-Connected VSC for Voltage Dip Mitigation

Power electronic converters connected in series with the grid can provide protection of sensitive processes against voltage dips. In this paper, an advanced cascade controller for series-connected voltage-source converter (VSC), constituted by an outer voltage loop and an inner current loop, is presented. Positive and negative sequence components of voltage and current are controlled separately to achieve perfect compensation of unbalanced dips. Limitation of the VSC output voltage and compensation of the computational time delay are included in the current controller for both sequence components. Experimental results for the series-connected VSC with the proposed cascade controller under balanced and unbalanced voltage dips are presented.
A dusty gust front of synoptic scale initiated and maintained by moist convection over the Sahara desert

11 In this study we document the evolution, the synopt ic trigger and the characteristics of an intense du st 12 event which occurred over the Sahara desert on Augu st 3-5, 2006. The motivation for this study is to 13 highlight the large scale dust production over the Sahara associated with gust fronts of squall lines. 14 The dust emission during this event was initiated b y a large-scale cold pool emanated from a squall 15 line that developed over Niger and Mali on August 3 . We examine the development of this squall line 16 and its subsequent dust lifting using high temporal resolution false color dust product images from th e 17 Meteosat Second Generation Spinning Enhanced Visibl e and Infrared Imager (MSG-SEVIRI). 18 Observations from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are 19
Model-oriented non-negative matrix factorization based music separation

Music separation is performed by learning the parameters that could best interpret the given piece of music under specific criteria. In this paper, we illustrate this by modelling a music part using the source-filter model. We made two improvements to this linear representation: Moving-Average method is used to give each partial a distinct Attack-Sustain-Release pattern and Gaussian function is used to build peaks in every excitation, leading to a unified linear representation of both pitched and unpitched components, e.g., Noise generator can be set in our excitation model which shares the same structure with harmonic components. Also, numerical stability of multiplicative update rules, which is the intrinsic flaw of the classical Nonnegative Matrix Factorization algorithm is addressed in our approach. We use both median filter and cut-off to sparsify and smooth the result to get rid of overhead introduced by a constraint in the cost function.
Apical–basal Polarity: Why Plant Cells Don't Stand on Their Heads

The anatomical terminology that is used to describe the orientation and polarity in the plant body (for distinction indicated here in italics) is based on the consensus agreement that the transition zone between the aerial and the soil-borne part (the root–shoot junction) is defined as the base of the plant, and the ends of a body axis – root and shoot tips – are defined as apices [1]. For embryos, another terminology is equally well established (indicated here by underlining), which defines the root embryo pole as the base and its opposite end as the apex [1]. Over the past few years, studies dealing with issues of cellular polarity in plants have used either anatomical or embryological terminology to describe the 'upper' and 'lower' side of individual cells. Recently, this confusion in terminology was highlighted, and it was proposed that the anatomical terminology should be used in all cases [2]. However, we believe that the anatomical terminology, when used for the description of cellular polarity, has important deficiencies. Here, based on a number of arguments, we propose what we consider to be a suitable compromise. The anatomically well defined base of the plant is only vaguely defined functionally, and this becomes particularly apparent if one considers the transport of substances , for example, the strictly controlled unidirectional transport of the plant signalling molecule auxin [3]. All transported substances passing the anatomical base of the plant find their direction of movement suddenly changed from basipetal (towards the base) to acropetal (toward the apex) because the same transport stream is termed differently in shoots and roots. Physiolo-gists have become used to this challenge, and it even proves to be an excellent selection criterion for distinguishing the bright but less attentive biology students from the more dutiful ones. All adult plants originate from a single-celled zygote, whose pole of attachment to the maternal tissue is defined as basal and its opposite pole as apical [1]. This early polarity serves as a reference for a well-established term, the apical–basal body axis, and also for delineating the opposite poles of the embryo. Because it is not clear when and where the anatomical base of the embryo arises, all reports dealing with early development delineate the root pole (future position of the root apex) as the base of the embryo. It is an historical irony that the contrasting anatomical and embryological terminologies were coined in the …
Family Interference with Work and its Relationship with Organizational Citizenship and Counterproductive Work Behaviors

Most employed adults must manage both work and family responsibilities. Consequently, many employees also experience conflict between their roles, which often leads to compromised performance in one or both domains. We examined family interference with work (FIW) as a potential drain on resources leading to increased counterproductive work behaviors (CWB) and decreased organizational citizenship behaviors (OCB) in three samples with a multi‐measure, time‐lagged design. Results demonstrated that employees who experience FIW display higher levels of CWB and lower levels of OCB, especially for organizationally targeted behaviors.
Is waking electroencephalographic activity a predictor of daytime sleepiness in sleep-related breathing disorders?

No data are available in the literature assessing the potential use of waking electroencephalographic (EEG) activity in the detection of excessive daytime sleepiness (EDS) in patients with sleep-related breathing disorders (SRBD). The aim of this study was to evaluate whether waking EEG spectral power reflects the level of EDS in SRBD patients. The study was performed in 48 patients in whom quantitative EEG analysis, including the alpha attenuation coefficient (AAC), was performed. Sleepiness was assessed by the Epworth Sleepiness Scale, the Stanford Sleepiness Scale, the Visual Analogue Scale and the maintenance of wakefulness test. Although AAC and EEG spectral power tended to vary throughout the day, none of these variations correlated with EDS measures. Waking EEG measures were not different between snorers and apnoeic patients. Compared to nonsleepy patients, sleepy patients had greater theta and slow alpha powers, but the differences did not reach statistical significance. The EEG slowing was independent of hypoxaemia, severity of SRBD, or degree of sleep disruption. The authors conclude that waking electroencephalographic measures are not sensitive enough to predict variation in alertness or to differentiate sleepy from nonsleepy sleep-related breathing disorders patients. The degree of electrocephalographic slowing was related neither to sleep disruption nor to severity of sleep-related breathing disorders.
Lexical Richness in EFL Students’ Narratives

The present paper compares different measures of lexical richness in narratives written by Czech EFL learners. The focus is on three groups of lexical richness measures: measures of lexical diversity (saying how many different words are used), lexical sophistication (saying how many advanced words are used) and lexical density (saying what is the proportion of content words in the text). The most frequently used measures representing each group were selected (Tweedie & Baayen 1998; McCarthy 2005; Daller et al. 2007; McCarthy & Jarvis 2010) and used to analyse students’ stories. The main focus of the study is on comparing the relationships between different measures, both within and between the three respective groups. The results show that the three groups are to some extent distinct and therefore measure different kinds of vocabulary knowledge but also that there are relationships between them: the strongest correlations are between measures of lexical diversity and sophistication; measures of lexical diversity and density correlate very weakly, and there are no significant correlations between measures of lexical density and sophistication.
Empirical Processes Based on Pseudo-Observations II : theMultivariate

Universit e du Qu ebec a Trois-Rivi eres In practice, it is often necessary to study empirical distribution functions constructed from residuals when estimating the distribution of functions (X) of an observable random variable X. More generally, one can be interested in estimating the distribution function of a non-observable random variable (Q; X) depending on an observable random variable X together with its unknown law Q. When Q is estimated by some Q n , the quantities (Q n ; X i) are called pseudo-observations. The aim of this paper is to continue the study of empirical processes constructed from general pseudo-observations in the multivariate case. Examples of pseudo-observations will be given together with applications to copulas, multivariate regression and time series. formation de chercheurs et l'aide a la recherche du Gouvernement du Qu ebec and by the 1. Introduction. There is always a price to pay for not being able to ob
From Photo Streams to Evolving Situations

Photos are becoming spontaneous, objective, and universal sources of information. This paper develops evolving situation recognition using photo streams coming from disparate sources combined with the advances of deep learning. Using visual concepts in photos together with space and time information, we formulate the situation detection into a semi-supervised learning framework and propose new graph-based models to solve the problem. To extend the method for unknown situations, we introduce a soft label method which enables the traditional semi-supervised learning framework to accurately predict predefined labels as well as effectively form new clusters. To overcome the noisy data which degrades graph quality, leading to poor recognition results, we take advantage of two kinds of noise-robust norms which can eliminate the adverse effects of outliers in visual concepts and improve the accuracy of situation recognition. Finally, we demonstrate the idea and the effectiveness of the proposed model on Yahoo Flickr Creative Commons 100 Million.
Review on statistical approaches for automatic image annotation

This paper will discuss the literature survey on statistical approaches for automatic annotation on digital images. There are some researches on image annotation and produced very good knowledge theoretically or technically and lead to produce such promising surveys. However, not all produced related knowledge on image annotation and discussing until the latest research on that area. This paper aims to cover the statistical approaches for automatic image annotation from it starts until the latest research finding. Using the earlier literature reviews, approaches, frameworks and evaluation results as guidelines, an attempt is made to outline the automatic image annotation which is combination of image analysis and statistical learning approaches. Summary and analysis of some of the approaches have been used as references to produce a framework in designing an automatic image annotation model.
Providing sustainable performance in thermally constrained mobile devices

State-of-the-art smartphones can generate excessive amounts of heat during high computational activity or long durations of use. While throttling mechanisms ensure safe component and outer skin level temperatures, frequent throttling can largely degrade the user-perceived performance. This work explores the impact of multiple different thermal constraints in a real-life smartphone on user experience. In addition to high processor temperatures, which have traditionally been a major point of interest, we show that applications can also quickly elevate battery and device skin temperatures to critical levels. We introduce and evaluate various thermally-efficient runtime management techniques that slow down heating under performance guarantees so as to sustain a desirable performance for maximum durations. Our techniques achieve up to 8x longer sustainable QoS.
The Limit Analysis in Continuum Mechanics

The limit analysis method for an estimation of mechanical and electrical strength safety conditions for nonlinear elastic and dielectric solids, respectively, is examined. By the variational method and the duality theory, the appropriate initial and dual Limit Analysis Problems (LAPs) are formulated. It is marked that the initial LAPs need a relaxation, but the appropriate fully relaxed problems have no clear physical interpretation. On the other hand, the dual LAPs have a clear physical interpretation. The general initial and dual LAPs for quasistatic problems of Continuum Mechanics are formulated. The appropriate 2-D finite-element approximations and numerical solutions are presented.
Azide-Alkyne Huisgen [3+2] Cycloaddition Using CuO Nanoparticles

Recent developments in the synthesis of SpellECuO nanoparticles (NPs) and their application to the [3+2] cycloaddition of azides with terminal alkynes are reviewed. With respect to the importance of click chemistry, CuO hollow NPs, CuO hollow NPs on acetylene black, water-soluble double-hydrophilic block copolymer (DHBC) nanoreactors and ZnO–CuO hybrid NPs were synthesized. Non-conventional energy sources such as microwaves and ultrasound were also applied to these click reactions, and good catalytic activity with high regioselectivity was observed. CuO hollow NPs on acetylene black can be recycled nine times without any loss of activity, and water-soluble DHBC nanoreactors have been developed for an environmentally friendly process.
Supervision of Indigenous research students: considerations for cross-cultural supervisors

Essential to Indigenous research students’ development is their preparation to undertake Indigenous research with an appropriate Indigenous approach. Preparing Indigenous students to conduct research with Indigenous communities requires their adequate and proper preparation, although this does not always happen. Getting the research story right is key to better understanding the persistent, complex and multidimensional inequities in the access, use and quality of health services Indigenous peoples face. Successful preparation of Indigenous students is contingent on quality student–supervisor relationships. The literature indicates that Indigenous student supervision undertaken by non-Indigenous supervisors can be hindered. Two vignettes demonstrate problems with cross-cultural supervision of Indigenous research students’ experiences. An examination of cross-cultural supervision practices highlights the need to prepare Indigenous students in Indigenous research methodologies to optimise outcomes to reflect Indigenous peoples’ realities. Following an overview of approaches to undertake research with Indigenous people, strategies to support cross-cultural supervision are suggested.