Miracle-dz/UniFAR-FTUs huggingface dataset:
Équation de Continuité pour l ’ Estimation du Mouvement Fluide Div-Curl Regularization and Continuity Equation dedicated to Fluid Motion Estimation

In this paper we address the problem of estimating the motion in image sequences showing fluid phenomenon. Due to the great deal of spatial and temporal distortions that luminance patterns exhibit in images of fluid, standard techniques from Computer Vision, originally designed for quasirigid motions with stable salient features, are not well adapted in this context. In that prospect, we investigate a dedicated energy-based motion estimator. The considered functional includes an original data model relying on the continuity equation of fluid mechanics, associated to an original div-curl type regularization. To assess the quality of the motion fields obtained by the proposed method, we examine the trajectories that can be reconstructed from them. The relevance of this new estimator is demonstrated both on synthetic and real (meteorological) image sequences.
Educators' Perceptions of Factors Contributing to School Violence in Alexandra

School violence continues to be highly prevalent in many low-income communities in South Africa. This study made use of an interpretive research paradigm to explore educators' subjective views of school violence in Alexandra. Participants were 12 educators at selected government schools with at least 5 years teaching experience. Individual interviews were carried out to explore these educators' experiences and subjective understandings of factors contributing to school violence. Thematic content analysis was used to report results at the individual, family, school, community and societal levels which educators perceived to play a role in school violence. At an individual and familial level age, mental health and child rearing arose as risk factors of school violence. Educators also signalled relational concerns within the institutional and leadership structures in schools as well as tensions between staff and learners as school factors. Whereas socio-political changes in South Africa, poverty, a lack of resources and the availability of illicit substances within the community were raised as community and societal risk factors for school violence. Language: en
Tilburg University Dutch word sense disambiguation

We describe a new version of the Dutch word sense disambiguation system trained and tested on a corrected version of the SENSEVAL-2 data. The system is an ensemble of word experts; each word expert is a memory-based classifier of which the parameters are automatically determined through cross-validation on training material. The original best-performing system, which used only local context features for disambiguation, is further refined by performing additional parallel crossvalidation experiments for optimizing algorithmic parameters and the amount of local context available to each of the word experts’ memory-based kernels. This procedure produces an accuracy of 84.8% on test material, improving on a baseline score of 77.2% and the previous SENSEVAL-2 score of 84.2%. We show that cross-validation overfits; had the local context been held constant at two left and right neighbouring words, the system would have scored 85.0%.
An application of the Boone indicator to the South African banking sector

In this study, I use the Boone indicator to explore competition dynamics in the South African loan market. Using a standard pooled ordinary least squares estimation, I find that competition in the loan market was improving for most of the period between 2006 and 2016. Between 2006 and 2014 the trend was upwards, before reversing between 2014 and 2015 and then normalizing from 2015 onwards. Another important result obtained from the Boone indicator is the strength of the relationship between efficiency and competition. From the results, it is not clear that more efficient firms gain greater market shares. Therefore, I conclude that there are various exogenous factors that inhibit the sector from functioning optimally, namely regulatory, financial, and informational barriers. I recommend overcoming these factors to optimize the functionality of the sector. Recommendations include consolidating regulatory bodies, redesigning penalties, minimizing switching costs, and forcing innovation and interoperability.
Impact of Performance Management Systems on Employee Performance

Performance management systems are designed to improve employee performance. However, the introduction of the Balance Scorecard redesigned the objectives of the performance management system. This study examines the moderating effect of using Balance Scorecard on the impact of performance management systems on employee performance. This research was conducted at manufacturing companies listed on the stock exchange in Indonesia. The research tool is a survey that includes 192 top managers from 63 registered manufacturing companies. The findings show that the use of Balance Scorecard moderates the impact of performance management systems on employee performance. This implies that the use of Balance Scorecard strategically complements and enhances the relationship between employee performance and performance management systems. This study shows that the Balance Scorecard must be used as a multi-dimensional method for measuring performance and as a strategic management system to improve employee
Knowledge Framework Faced to Domain Knowledge Based on Ontology

According to the commonness and difference about some knowledge models,knowledge framework faced to field knowledge which expresses by XML is put forwards on the basis of ontology theory.Firstly,some definitions about ontology and knowledge modeling are presented,and analyzed the field knowledge.Therefore,from the different view of field knowledge pattern and field knowledge base,the different sub-knowledge frameworks are realized respectively.Then the whole framework about field knowledge is set up.
Financial Sector Reforms, Monetary and Output Uncertainties and the Behavior of Money Demand in Kenya: The Divisia Index Approach

The financial sector reforms implemented by the Central Bank of Kenya (CBK) resulted in rapid financial innovation (such as the popular M-Pesa mobile money services) growth, and expansion of several interest earning financial instruments. These developments affect the definition and composition of monetary aggregates, posing a question on the correctness of the current money measures used by CBK. The simple sum aggregates were identified with several theoretical and empirical shortcomings. The rapid financial sector development might affect the stability of money demand function. This study constructs Divisia monetary aggregates for Kenya over the period of 2000? s first quarter to 2015? s third quarter and applies the ARDL method in investigating the stability of money demand function. For the first time, monetary uncertainty and output uncertainty variables are introduced to the Kenyan money demand model. The results reveal that both monetary and output uncertainty has significant influence on money demand in Kenya. This implies that omitting the two variables in the Kenya money demand function might lead to a wrong specification. The money demand function is stable over the period. It means that monetary aggregates targeting is the right framework for monetary policy formulation by the CBK.
Object identification and process planning using adaptive neuro-fuzzy systems

The inclusion of computer technology in process planning in the manufacturing industry has been an active area of research. The main of this work is to develop a software model that generates a process plan using a Neuro-fuzzy expert system. The model incorporates both retrieval and generative approaches towards Computer Aided Process Planning (CAPP). It makes use of a set of ANFIS structures in different levels for generating the operation sequences. Objects and expert knowledge systems identified and implemented for the
A New Integrated High-Latitude Thermal Laboratory for the Characterization of Land Surface Processes in Alaska's Arctic and Boreal Regions

Alaska’s Arctic and boreal regions, largely dominated by tundra and boreal forest, are witnessing unprecedented changes in response to climate warming. However, the intensity of feedbacks between the hydrosphere and vegetation changes are not yet well quantified in Arctic regions. This lends considerable uncertainty to the prediction of how much, how fast, and where Arctic and boreal hydrology and ecology will change. With a very sparse network of observations (meteorological, flux towers, etc.) in the Alaskan Arctic and boreal regions, remote sensing is the only technology capable of providing the necessary quantitative measurements of land–atmosphere exchanges of water and energy at regional scales in an economically feasible way. Over the last decades, the University of Alaska Fairbanks (UAF) has become the research hub for high-latitude research. UAF’s newly-established Hyperspectral Imaging Laboratory (HyLab) currently provides multiplatform data acquisition, processing, and analysis capabilities spanning microscale laboratory measurements to macroscale analysis of satellite imagery. The specific emphasis is on acquiring and processing satellite and airborne thermal imagery, one of the most important sources of input data in models for the derivation of surface energy fluxes. In this work, we present a synergistic modeling framework that combines multiplatform remote sensing data and calibration/validation (CAL/VAL) activities for the retrieval of land surface temperature (LST). The LST Arctic Dataset will contribute to ecological modeling efforts to help unravel seasonal and spatio-temporal variability in land surface processes and vegetation biophysical properties in Alaska’s Arctic and boreal regions. This dataset will be expanded to other Alaskan Arctic regions, and is expected to have more than 500 images spanning from 1984 to 2012.
Remote detection of human psycho-emotional state with the use of electro-photonic imaging method

Present work was focused on the remote registration of emotional state of a group of people and on study of its influence on the surrounding space with the use of electro-photonic imaging/gas discharge visualization method. Special procedure for conducting measurements and processing of the obtained results has been developed. This methodology was used for theatrical performances and opera concerts, seminars and lectures, and during group meditation.
An Approach to Tracking Problem for Linear Control System Via Invariant Ellipsoids Method

In this paper, a simple yet universal approach to the tracking problem for linear control systems via the linear static combined feedback is proposed. The approach is based on the invariant ellipsoid concept and LMI technique, where the optimal control design reduced to finding the minimal invariant ellipsoid for the closed-loop system. With such an ideology, the control design problem directly reduces to a semidefinite programming and one-dimensional minimization. Another attractive property of the proposed approach is that it is equally applicable to discrete-time control systems. The efficacy of the technique is illustrated via a benchmark problem.
FACIAL EXPRESSION RECOGNITION USING MULTISTAGE HIDDEN MARKOV MODEL

Facial Expression Recognition is an application used for biometric software that can be used to identify special expressions in a digital image by comparing and analysing the different patterns. These software are popularly used for the purpose of security and are commonly used in other applications such as home security, human-computer interface, credit card verification, surveillance systems, medicines etc. Recognizing faces becomes very difficult when there is a change occurs in facial expressions. In this paper two layer extension of HMM is used to recognized continuous effective facial expressions. Gabor wavelet technique is used for feature extraction. Two layered extension of HMM consists of bottom layer which represents the atomic expression made by eyes, nose and lips. Further upper layer represents the combination of these atomic expressions such as smile, fear etc. In HMM, Baum-Welch method is used for parameter estimation. Viterbi Method and Forward Procedure are used for calculating the optimal state sequence and probability of the observed sequence respectively. This proposed system consists of three level of classification. Output of the first level is used for the training purposes for the second level and further this level is used for the third level for testing. Six basic facial expressions are recognised i.e. anger, disgust, fear, joy, sadness and surprise. Experimental result shows that Proposed System performs better than normal HMM and has the overall accuracy of 85% using JAFFE database.
THE SOUTHWESTERN NATURALIST

Larvae of the sawfly Neodiprion edulicolus fed on needles of Pinus edulis and were abundant below 1,850 m in elevation at sites near Sunset Crater, Arizona. Coleosporium needle rust, in contrast, infected P. edulis above 1,850 m in elevation and rarely occurred on the same trees as did N. edulicolus. Larval sawfly transferred to P. edulis above 1,850 m fed less frequently on infected twigs and moved off twigs more quickly when transferred to infected trees. Though the negative interactions involved only a small percentage of larval sawfly and rust fungus during this study, the interaction might become more significant in the future if sawflies spread to higher elevations in response to changes in climate or habitat. RESUMEN—Larvas de moscas serra Neodiprion edulicolus se alimentaron de agujas de Pinus edulis y fueron abundantes a una elevación inferior a 1,850 m en zonas cercanas a Sunset Crater, Arizona, USA. En contraste, 474 vol. 58, no. 4 The Southwestern Naturalist
Transductive Learning Via Improved Geodesic Sampling

Transductive learning exploits the connection between training and test data to improve classiﬁcation performance, and the geometry of the manifold underlying the training and the test data is essential to make this connection explicit. Existing approaches pri-marily focus on Grassmannian manifolds, while much less is known regarding other manifolds, which can potentially bring increased computational and learning performance. In this paper, we close the gap and formulate a novel and more general geodesic sampling approach on Riemannian manifolds (GSM) that encompasses Sphere, Kendall, and Grassmannian manifolds. To provide practical guidance for classiﬁcation, we explore extensive hyperparameter settings and baselines, including deep transfer learning models. The results show that the new method can enable more accurate and less computationally expensive geodesic sampling on the sphere manifold, which is not possible to achieve using the existing Grassmannian manifold.
Incidental Vocabulary Learning through Reading

A lot of studies on vocabulary learning have demonstrated that one of the ways of acquiring vocabulary is through reading. The purpose of this study was to replicate the study conducted by Zahar, Cob and Spada (2001) and compare the results with the original study and another replication study using the same design. The study was conducted with university students who were in their first year of studying English language and literature. In order to establish the participants’ vocabulary size, Nation’s Vocabulary Levels Test (1990) was used. The effect of the reading treatment was determined by using a pretest-posttest design. The results of the posttest showed that the participants learned one in three previously unknown words. There was a positive relationship between the participants’ vocabulary size and the relative gains as well as between the frequency of the words in the text and the relative gain scores. The findings suggest that learners’ general knowledge and cognitive abilities may be significant factors that affect the rate of vocabulary acquisition through reading.
Addressing the Computational Cost of the Immersed Boundary Method through Multi-Implicit and Multi-Rate Strategies with Time Parallelism

LAUREN E FOVARGUE: Addressing the Computational Cost of the Immersed Boundary Method through Multi-Implicit and Multi-Rate Strategies with Time Parallelism (Under the direction of Michael L Minion) Many problems in biological fluid-structure interaction have been studied with Peskin’s immersed boundary method (IBM). This method defines a relatively simple mathematical modeling framework, and allows for the use of standard fluid solvers. However, when evaluated computationally in many applications, a very small time step is required. This time step is not restricted by accuracy, but stability, causing the temporal problem to be stiff. Previous attempts to address the stability restriction of IBM use semi or fully implicit schemes that require the code, including the fluid solver, to be rewritten, and these have yet to be implemented in application focused studies. Here, new ideas for addressing the computational cost of IBM are presented, which rely on a novel method for splitting the spatial field into stiff and non-stiff components. With this splitting, the impact on the largest stable time step and computational cost for stiff problems is investigated through multi-implicit, multirate and time parallel techniques. All of the algorithms presented here improve upon the stability restriction of IBM, whether with a larger stable time step or overall speedup. In addition, they are focused on the treatment of the immersed boundary and no changes to the fluid solver are necessary, making them more accessible to applications.
Affordance-based Generation of Pretend Object Interaction Variants For Human-Computer Improvisational Theater

This paper describes DeepIMAGINATION, a neural architecture to generate variants of movement-based object interactions with props using the physical attributes of props while playing the Props game. The agent can generate these action variants while searching a learned action space in real-time to provide improvised responses to its human partner. Convolutional and recurrent variants of CVAEs are used for experimentation. The paper presents an evaluation of the architecture by benchmarking its ability to learn the human data set and generate believable, recognizable, and high-quality action variants from it. Results showed that the agent could generate believable, high-quality action variants. but that rec-ognizability requires improvement.
Factors Affecting Employment Choices in Rural Northwest Pakistan

U in rural northwest Pakistan. The study uses census data of 2825 households in six villages of Peshawar district Pakistan collected by an interdisciplinary research team of Göttingen University, Germany. The results comparing six distinct occupations versus non-farm informal activities suggest that asset endowment of households has a significant effect on households' head choice of employment. Livestock holding increases the odds of practicing farm related occupations relative to informal wage activities. Wealth, defined here as household per capita income, matter a lot in the likelihood of pursuing occupations other than informal activities in northwest Pakistan. Similarly, the likelihood increases for household with fairly younger and illiterate heads to engage in non-farm informal sector. Household size is positively and significantly related to all the occupational groups while an additional working member reduces the odds to work as farmer. Credit and farm land constraint also drive households into informal jobs. Finally, comparing the six villages in term of labour market, we find that business activities are significant in villages with better infrastructure. Due to lack of natural, financial and human capital more and more people in the study area have to find their livelihood in the informal casual sector. However, majority of these non-farm activities are survival oriented and have little to do with wealth accumulation. The implication of the study is that rural non-farm sector needs its due share in development policies as it has the potential to uplift the rural areas.
The importance of power dynamics in the development of asynchronous online learning communities

This research explored how a more student-directed learning design can support the creation of togetherness and belonging in a community of distance learners in formal higher education. Postgraduate students in a New Zealand School of Education experienced two different learning tasks as part of their online distance learning studies. The tasks centered around two online asynchronous discussions each for the same period of time and with the same group of students, but following two different learning design principles. All messages were analyzed using a twostep analysis process, content analysis and social network analysis. Although the findings showed a balance of power between the tutor and the students in the first high e-moderated activity, a better pattern of group interaction and community feeling was found in the low e-moderated activity. The paper will discuss the findings in terms of the implications for learning design and the role of the tutor.
Orchestration of Network Service Chains for Network Function Virtualization Using Optoelectronic Routers

This article presents an efficient network function virtualization infrastructure for the orchestration of network service chaining. Network Service Chaining is a service deployment concept that enables network operators to configure network services dynamically in software without making any changes to the hardware. With the infrastructure presented in this paper, applications can create and monitor their service chains. For this, we construct a virtual network with optoelectronic routers. Optoelectronic router helps in managing the service chains in virtual environments. Its optical domain can be used for deploying network functions and electronic for data storage and buffering. Due to this, optoelectronic routers can assist us in saving expensive optical/electronic/optical conversion. Keyword— network virtualization; network service chaining; performance of data center; network orchestration; network function virtualization; SDN; O/E/O Conversion Cost
The 17 th Annual Conference of the Japanese Society for Artificial Intelligence , 2003-1-A Framework for Interactive Video Based Explanatory System Burin

A large amount of worthy video data becomes meaningless for the user if he doesn’t satisfy the information provided to him especially for the explanatory system. Thus, to satisfy the user, we have to serve him the information that can fulfill not only his explicit requests but also his implicit wants. This paper introduces a framework for an active system that creates video-based contents according to the knowledge about the user that the system can detect via the action that the user performs towards each video. We describe the basis framework of the system by evaluating our framework through the domain of cooking.