Miracle-dz/UniFAR-FTUs huggingface dataset:
The 1×1 annular ring: Bandwidth enhancement of microstrip asymmetric couple-line bandpass filter with superior matching and lower insertion loss

Electromagnetic Band Gap (EBG) structures which is an exclusive innovation in modern microwave design arena by reason of their high potential and less intricacy of designing in the microwave and millimeter-wave integrated circuits. This paper examines the relationship between the filling factor (FF) of the annular ring structure on couple-line and capacitive coupling for improving the performance. A number of methods, method of moments (MOM) for numerical analysis and uniform distribution with constant aspect ratio (AR) for designing the circuit are used. In like manner, the parametric comparison has been demonstrated considering selectivity, 3-dB bandwidth (3-dB BW), insertion loss and return loss in the passband. Consequently, not only a very wide passband upto 49% increment but also a return loss upto 65 dB which illustrates great matching are achieved. Although, the insertion loss is less than 0.2 dB in the passband, but the selectivity shrinks with expanding bandwidth. In short, remodeling of the selectivity can be done by appropriately choosing the filling factor for annular ring pattern on the couple-line as well as on the transmission line.
Decentralized Execution of Composite Service in MANETs

Service oriented architecture is expected to be implemented in mobile ad hoc network. The execution of composite service is an important research issue in this area. Most previous works focus on the centralized architecture which assumes the existence of a central scheduler. In this paper, we propose the decentralized service execution architecture which do not need a central node to take responsibility of controlling the service execution process. With this architecture, we could significantly reduce the network traffic and so as the service failure rate. To construct the service execution path, a path detection approach is proposed on the purpose of minimizing the service response time. The simulation results prove the efficiency of our decentralized architecture and the path detection approach in different environments.
God Wat Þæt Ic Eom God - An Exploratory Investigation Into Word Sense Disambiguation in Old English

Natural Language Processing (NLP) of historical languages is an understudied area. Much previous work has focused on the problems of normalization and POS tagging. In contrast, we consider a new problem, word sense disambiguation (WSD). We provide a survey of previous work on processing of historical languages and discuss what we can and cannot apply to the problem of WSD, specifically WSD of Old English (OE). We then annotate a new resource for supervised WSD, which we make available. Finally, we carry out proof-of-concept experiments, followed by a discussion of several promising areas for future work.
The Collaborative Filtering Recommendation Mechanism Based on Bayesian Theory

In this paper, we propose a Collaborative filtering recommendation method based on Bayesian theory. It firstly divides the items that has been rated into two group, then uses Bayesian theory to study the users' preference. And analyze the degrees of the users' preference for the items' inherent characteristics. Then judge which group the item that has not been rated belongs to. At last It computes the similarities of ratings in the cluster which it belong to. Because it searches less, it can improve the response time. The problem of scalability and Real-time was resolved. Finally, we experimentally evaluate our result and compare them to the traditional item-based algorithms. Our experiments showed that this algorithm could effectively improve the real-time performance of recommendation systems.
Investigation of Temperature Dynamics in Small and Shallow Reservoirs, Case Study: Lake Binaba, Upper East Region of Ghana

An unsteady fully three-dimensional model of Lake Binaba (a shallow small reservoir) in semi-arid Upper East Region of Ghana has been developed to simulate its temperature dynamics. The model developed is built on the Reynolds Averaged Navier–Stokes (RANS) equations, utilizing the Boussinesq approach. As the results of the model are significantly affected by the physical conditions on the boundaries, allocating appropriate boundary conditions, particularly over a water surface, is essential in simulating the lake’s thermal structure. The thermal effects of incoming short-wave radiation implemented as a heat source term in the temperature equation, while the heat fluxes at the free water surface, which depend on wind speed, air temperature, and atmospheric stability conditions are considered as temperature boundary condition. The model equations were solved using OpenFOAM CFD toolbox. As the flow is completely turbulent, which is affected by the complex boundary conditions, a new heat transfer solver and turbulence model were developed to investigate the spatial and temporal distribution of temperature in small and shallow inland water bodies using improved time-dependent boundary conditions. The computed temperature values were compared with four days of observed field data. Simulated and observed temperature profiles show reasonable agreement where the root mean square error (RMSE) over the simulation period ranges from 0.11 to 0.44 °C in temporal temperature profiles with an average value of 0.33 °C. Results indicate that the model is able to simulate the flow variables and the temperature distribution in small inland water bodies with complex bathymetry.
Affect Recognition from Speech

We aim to provide an automatic anti-bullying component in online text and speech based interaction for young people age 18 --25. Affect expression in speech generally differs from culture to culture, from female to male. In this study, we focus on affect sensing from speech for different gender user groups for young people. So far our work mainly concentrates on the sensing of five basic emotions (including 'happiness', 'sadness', 'fear', 'surprise', and 'anger') and 'neutral' from speech. Detailed acoustic features have been extracted after analysis of speech data from one chosen male and female speaker. Our affect sensing component has been implemented under the theory of naive Bayes classifier. We have also evaluated it using new test data. Our work contributes to the conference themes on intelligent technologies --machine learning and affective speech processing.
Board of directors’ quality and firms’ debt financing: the moderating effect of insider ownership – evidence from Omani firms

ABSTRACT This study aims to examine the direct effects of board of directors’ quality and insider ownership on firms’ capital structure and the moderating effect of insider ownership on the relationship between board of directors’ quality and firms’ capital structure. The sample of this study consists of the time period 2005–2011. The final sample consists of 535 firm-year observations of public nonfinancial firms listed on the Muscat Securities Market (MSM). The hypotheses are tested using a random effects model. The results reveal that the coefficient sign on the board of directors’ quality is as predicted, but the coefficient sign is insignificant. Further, the results show that insider ownership is positively associated with leverage at the significant level of 10%, suggesting that the inside substantial shareholders employ higher leverage. With regard to the moderating effect of insider ownership, the findings show that the effect of the board of directors’ quality-leverage relationship becomes negative and stronger with increasing insider ownership.
Integral Estimation from Point Cloud in R d : a Geometric View

Integration over a domain, such as a Euclidean space or a Riem annian manifold, is a fundamental problem across scientific fields. Many times, the underlying domain is only accessible through a discrete approximation, such as a set of points sampled from it, and it is crucial to be able to estimate integral in such discrete settings. In this paper, we study the proble m of estimating the integral of a function over ak-submanifold embedding in IR, from its function values over a set of sample points. Previo usly, such estimation is usually obtained in a statistical settin g, where input data is usually assumed to be drawn from certain probabilistic distribution. Our paper i s the first to consider this important problem for arbitrary point clouds data (PCD), and provide theoreti cal guarantees. We approaches the problem from a geometric point of view. Specifically, we model the int egral as a weighted sum, and propose two weighting schemes: the Voronoi and thePrincipal Eigenvectorweighting schemes. The running time of both methods depend mostly on the intrinsic dimension of t he underlying manifold, instead of on the ambient dimensions. We show that the estimation based on the Voronoi scheme converges to the true integral (explicit error bound is given). This is the first re sult of this sort for estimating integral from general PCDs. For the Principal Eigenvector scheme, we pres ent it connection to the Heat diffusion operator, and illustrate several justifications behind its construction. Experiments show that both new methods consistently out-perform common statistical meth ods under various sampling conditions.
Formal Schooling Influences Two- but Not Three-Dimensional Naming Skills

The modulatory influence of literacy on the cognitive system of the human brain has been indicated in behavioral, neuroanatomic, and functional neuroimaging studies. In this study we explored the functional consequences of formal education and the acquisition of an alphabetic written language on two- and three-dimensional visual naming. The results show that illiterate subjects perform significantly worse on immediate naming of two-dimensional representations of common everyday objects compared to literate subjects, both in terms of accuracy and reaction times. In contrast, there was no significant difference when the subjects named the corresponding real objects. The results suggest that formal education and learning to read and to write modulate the cognitive process involved in processing two- but not three-dimensional representations of common everyday objects. Both the results of the reaction time and the error pattern analyses can be interpreted as indicating that the major influence of literacy affects the visual system or the interaction between the visual and the language systems. We suggest that the visual system in a wide sense and/or the interface between the visual and the language system are differently formatted in literate and illiterate subjects. In other words, we hypothesize that the pattern of interactions in the functional-anatomical networks subserving visual naming, that is, the interactions within and between the visual and language processing networks, differ in literate and illiterate subjects.
Directory Structures for Scalable Internet Caches Directory Structures for Scalable Internet Caches

Use of Internet caches is a cheap and eeective way to improve performance for all Internet users. Distributed caches ooer the potential to serve larger user communities and to deliver higher hit ratios on shared Web documents. The key to building eeective distributed caches is a directory structure that allows individual caching servers to locate objects cached at neighboring sites, combining them into a logically uniied collective cache. This paper uses Web traces to evaluate a range of alternatives for managing directories in distributed Internet caches. We use trace-driven executions and simulations of prototype caches to compare multicast-based queries of local maps (Harvest) with unicast queries of a global map (CRISP). We then use properties of the traces to predict performance of CRISP variants in which the global map is partitioned or replicated. Finally, we propose a novel lazy CRISP structure based on weakly consistent replication of the most valuable subset of the global map. We show that this scheme combines the best aspects of several distributed cache structures, while maintaining a near-ideal hit ratio.
Motion blur removal in nonlinear sensors

We address the problem of motion blur removal from an image sequence that was acquired by a sensor with nonlinear response. Motion blur removal in purely linear settings has been studied extensively in the past. In practice however, sensors exhibit nonlinearities, which also need to be compensated for. In this paper we study the problem of joint motion blur removal and nonlinearity compensation. Two naive approaches for treating this problem are to apply the inverse of the nonlinearity prior to a deblurring stage or following it. These strategies require a preliminary motion estimation stage, which may be inaccurate for complex motion fields. Moreover, even if the motion parameters are known, we provide theoretical arguments and also show through simulations that theses methods yield unsatisfactory results. In this work, we propose an efficient iterative algorithm for joint nonlinearity compensation and motion blur removal. Our approach relies on a recently developed theory for nonlinear and nonideal sampling setups. Our method does not require knowledge of the motion responsible for the blur. We show through experiments the effectiveness of our method compared with alternative approaches.
A management model for SDN-based data center networks

Current data centers often employ SDNs (Software-Defined Networks) that are layered in most cases. However, information overwhelming has been a limitation to SDN deployment. We present a management model for data center networks. In this approach, regional networks on lower layers will be aggregated and viewed as single switches to upper layers. Management information will be divided into three parts, which can be seen by network managers, regional controllers and tenants, respectively.
Modular Modeling of Cellular Systems with ProMoT/Diva

MOTIVATION Need for software to setup and analyze complex mathematical models for cellular systems in a modular way, that also integrates the experimental environment of the cells. RESULTS A computer framework is described which allows the building of modularly structured models using an abstract, modular and general modeling methodology. With this methodology, reusable modeling entities are introduced which lead to the development of a modeling library within the modeling tool ProMot. The simulation environment Diva is used for numerical analysis and parameter identification of the models. The simulation environment provides a number of tools and algorithms to simulate and analyze complex biochemical networks. The described tools are the first steps towards an integrated computer-based modeling, simulation and visualization environment Availability: Available on request to the authors. The software itself is free for scientific purposes but requires commercial libraries. SUPPLEMENTARY INFORMATION http://www.mpi-magdeburg.mpg. de/projects/promot
Strategic Clusters and Strategic Space: The Case of the Short Break Market

Short breaks are one important segment of the overall tourism market. They provide a distinct market sector with, it appears, special competitive strategies. While the growth and value of this market sector is undisputed, little attention has been paid to the strategic nature of short break provision, or the potential of movement in strategic space. Using data gathered from personal interviews and applying the concept of strategic group clusters and strategic space, explores strategy and performance differences of companies operating in the UK short break market. Examines correlation between structural variables of market scope, company size; and sources of competitive advantage in relation to prime strategies adopted. Draws conclusions with regard to shifts in strategic space for performance enhancement, and potential future market developments.
The five-year change process at a secondary school: A case study

The Five-Year Change Process at a Secondary School: A Case Study by LuAnne Marie Forrest Dr. Patti Chance, Committee Chair Associate Professor of Education University of Nevada, Las Vegas This study explored the five-year change process as experienced by members of a secondary school community who intentionally sought out and pursued school improvement through the establishment of a continuous improvement process with the goal of increased student achievement. Three questions guided this exploration: (a) What contextual factors contributed to the change process at a secondary school pursuing continuous improvement processes; (b) How did these contextual factors interact and inter-relate to contribute to the implementation and sustainability of the eontinuous improvement process; and (c) How did these contextual factors interact and inter-relate to contribute to the change process itself. As such, this study involved a close examination of the phenomenon of change as it existed within the complex situational and contextual conditions of the school organization. This was a longitudinal, exploratory single-case study with an historical analysis. Qualitative tools used to conduct this exploration included an extensive document review, an open-ended observation and twenty semi-structured interviews of teachers, teacher-leaders, administrators, parents and district leaders. Data were analyzed on three levels: chronologically, categorically and from the perspectives of multiple stakeholder groups. Within categories, data were analyzed for patterns and relationships using content and constant comparative analyses.
COMPLEXITY CONTROL OF H . 264 BASED ON A BAYESIAN FRAMEWORK

A computational complexity control algorithm is proposed for an H.264 encoder running on a processor/power constrained platform. Computational savings are achieved by early prediction of skipped macroblocks prior to motion estimation. The early prediction is based on a Bayesian framework which accurately predicts skipped macroblocks using probability models. Complexity control is achieved by calculating a pre-skip decision threshold that corresponds to a given target complexity. Results show that the algorithm can effectively control the encoding complexity whilst maintaining good rate distortion performance.
Virtual noise based detection in cooperative systems with co-channel interference

In this paper, we have investigated the effect of cochannel interference on Decode-and-forward based cooperative communication system by providing the analytical results and computer simulations over Rayleigh fading channels. Virtual noise technique is used with co-channel interference at relay and destination terminals. It can be clearly observed that virtual noise techniques provides full diversity gain for proposed system. The accuracy of the analytical results shown by computer simulations.
Sequential selection approach for selecting the best system

Consider the problem of selecting the best simulated system with high probability, from a finite and huge set of alternative systems. The best system might be the one that has the maximum or minimum performance measure. In this paper, we present a sequential method that uses the Ordinal Optimization procedure to select randomly a subset that overlaps with the set of the actual best m% systems with high probability from the search space. The next step, we use Optimal Computing Budget Allocation technique to allocate the available computing budget in a way that maximizes the probability of correct selection. This follows by a Subset Selection procedure to get a smaller subset that contains the best system from the subset that is selected before. Finally, we use the Indifference-Zone procedure to select the best system among the survivors in the previous stage. The results of the empirical experiments show that this approach selects the best simulated system with high probability and a minimum number of simulation replication, when the number of alternatives is huge.
An Objective Criterion for the Selection of an Optimum DIC Pattern and Subset Size

The grey level co-occurrence matrix (GLCM) is used in this work for quantitative spatial texture description. The two GLCM metrics, oset and contrast, are used to quantify spatial intensity variation. It is shown that the optimal DIC pattern must possess low critical GLCM oset and high nominal GLCM contrast. A very strong correlation between the critical GLCM contrast and the correlation window size is observed. It is shown that displacement resolution of 0.01 pixel can be achieved if the correlation window size is approximately 3 times bigger than the critical GLCM oset. The method has been tested on over 60 structured speckle and non-structured random smoothed patterns. The proposed method is fully automatic and allows for the optimal pattern and the optimal correlation window size to be chosen in a systematic and fully objective manner based the pattern alone, with no need for the trial DIC experiments.
Social Dynamics and the Cortisol Response to Immobilization Stress of the African Wild Dog,Lycaon pictus


The aims of the study were to characterize the cortisol response to immobilization stress in African wild dogs (Lycaon pictus) and to investigate the relationship between stress and sociality in these pack-living canids. Ad lib. observations were made on a captive pack of 19 wild dogs. Individuals were classified as either dominant or subordinate. Cardinal and ordinal dominance indices were also calculated for pack members, as were three other behavioral indices. Active and passive dominance styles were distinguished. Serial blood samples were drawn from animals after chemical immobilization and again after ACTH challenge. The relationship among rank, plasma cortisol concentration, and behavioral style was investigated. When data were combined over the entire study period, there was no obvious relationship between rank and cortisol concentrations or cortisol responsiveness to immobilization stress. Instead, younger animals had higher cortisol concentrations than older ones. Age cohorts were also clearly separated on the basis of behavioral profiles. For males, these distinctions among age classes were especially apparent during the second part of the study period, when subadults occupied dominant positions in the hierarchy. Adult males maintained relatively low cortisol concentrations, despite being involved in and losing a high proportion of dominance interactions. Age-related differences in cortisol profiles of dominant individuals may be explained by differences in the style of dominance employed, with younger males using proportionately more active dominance (threats used to elicit submission). The separation of age classes according to rank, behavioral styles, and cortisol concentrations may reflect improved social skillfulness with age.
Medical Image Denoising Using Hierarchical Hidden Markov Model in the Wavelet Domain

Wavelet-domain hidden Markov models (HMMs) have been recently proposed and applied to image processing, e.g., image denoising. In this paper, we develop a new HMM, called hierarchical hidden Markov tree model (HHMT), by adopting a feasible and fast two stage algorithm which avoids the time-consuming training process to estimate the HMT model parameters. The HHMT can exploit both the local statistics and the interscale dependencies of wavelet coefficients at a low computational complexity. We show that the HHMT model can achieve state-of-the-art medical image denoising performance.