Miracle-dz/UniFAR-FTUs huggingface dataset:
Consistent Annual Citations based Researcher Index

Different methods have been proposed for researcher indexing based on quantity and quality of papers. Unfortunately, none of them considered how consistently a paper of a researcher is able to get attention of other researchers over the years in scientific community. In this work, a new method named Consistent Annual Citations-Index (CAC-Index) is proposed to address this issue. Consistent annual citations mean that (1) after publication the number of years a paper attracted scientific community and (2) consistency among number of citations received each year. CAC-Index considers; quantity (papers), quality (citations) and temporal dimension in a unified way. Discussions show the importance of the proposed idea for researcher indexing.
On the acceleration of Eta Ferrier Cloud Microphysics Scheme in the Weather Research and Forecasting (WRF) model using a GPU

The Eta Ferrier cloud microphysics scheme is a sophisticated cloud microphysics module in the Weather Research and Forecasting (WRF) model. In this paper, we present the approach and the results of accelerating the Eta Ferrier microphysics scheme on NVIDIA Graphics Processing Units (GPUs). We discuss how our GPU implementation takes advantage of the parallelism in Eta Ferrier scheme, leading to a highly efficient GPU acceleration. We implement the Eta Ferrier microphysics scheme on NVidia GTX 590 GPU. Our 1-GPU implementation achieves an overall speedup of 37 as compared with a single thread CPU. Since Eta Ferrier microphysics scheme is only an intermediate module of the entire WRF model, the GPU I/O should not occur, i.e. its input data should be already available in the GPU global memory from previous modules and its output data should reside at the GPU global memory for later usage by other modules. The speedup without the host-device data transfer time is 272 with respect to its serial version running on 3.20GHz Intel® CoreTM i7 970 CPU.
Business Bankruptcy Prediction Based on Survival Analysis Approach

This study sampled companies listed on Taiwan Stock Exchange that examined financial distress between 2003 and 2009. It uses the survival analysis to find the main indicators which can explain the business bankruptcy in Taiwan. This paper uses the Cox Proportional Hazard Model to assess the usefulness of traditional financial ratios and market variables as predictors of the probability of business failure to a given time. This paper presents empirical results of a study regarding 12 financial ratios as predictors of business failure in Taiwan. It showed that it does not need many ratios to be able to anticipate potential business bankruptcy. The financial distress probability model is constructed using Profitability, Leverage, Efficiency and Valuation ratio variables. In the proposed steps of business failure prediction model, it used detail SAS procedure. The study proves that the accuracies of classification of the mode in overall accuracy of classification are 87.93%.
A novel synchronous multi-intensity IR illuminator hardware implementation for nighttime surveillance

For nighttime surveillance, it’s difficult for an ordinary IR illuminator (which has a fixed light intensity) to generate a one-size-fits-all light condition for objects at different distances. Often, we will get overexposed (underexposed) image for an object which is too close to (too far from) the illuminator/camera in the nighttime. To partly resolve such problems, a multi-intensity IR illuminator was proposed which can emit periodically varying intensities of IR light toward an object. However, two major problems remain to be resolved when using such an illuminator, i.e., the “synchronization issue” for background modeling and the “viewing issue” for manual monitoring. In this paper, we propose a novel synchronous multi-intensity infrared (SMIIR) illuminator hardware design to help address these two issues. Hopefully this innovation will open the door to a new era of nighttime surveillance.
Generation of CG Animations Based on Articulatory Features for Pronunciation Training

We describe a system for pronunciation training that dynamically generates CG animations to express pronunciation visually from speech based on articulatory features. The system speciﬁcally displays the results of phoneme recognition and CG animations of articulatory movements of both learners and a teacher that are estimated from their speech. Learners can thus notice their mispronunciation movements and ﬁnd the correct method of pronunciation by comparing their incorrect pronunciation movements with the correct ones on the animations. We conducted an experiment to evaluate the effectiveness of the animated pronunciations and we acquired a correctness of 93% for articulatory features with our proposed system. As a result, we clariﬁed that CG animations could adequately visualize the teacher’s articulatory movements and those of learners. Further, the improvement to the pronunciation score with the proposed system was double that with the existing system. These results veriﬁed that the new system was an effective training system.
Modeling and Simulation Method of Component Based Petri Net

The structure of Petri net model is relative fixed after it is built, so it cant describe dynamic change behavior of real system structure easily. Description formalness, coupling mechanism and simulation method were presented, the dynamic change of Petri net structure in simulation run was realized based on the idea of software component design. The reuse capability of Petri net model is enhanced, integration with other models is convenience and hierarchical modeling mode is realized, and the capability of modeling and simulation of Petri net is improved.
Synchronization support for efficient parallel discrete event simulation

As a case study on the applicability of the EDA (extended dataflow architecture) model, CE-graphs (concurrent event graphs) have been defined to achieve high performance of parallel discrete event simulations (PDES) on parallel computers. Two levels of communication and synchronization are devised to execute CE-graphs efficiently, namely coarse-grained or medium-grained token (message) passing and fine-grained variable operations. The CE-graph approach can expose the highest degree of parallelism among independent events, while most existing approaches for PDES can only exploit parallelism at the level of components of a real system. Furthermore, the CE-graph approach can handle pre-emption situations in a natural way, which can be a difficult task for other approaches. <<ETX>>
Dual staircase shaped microstrip patch antenna

The microstrip antenna finds its place in varied and upcoming technologies because it offers low profile, narrow bandwidth, high gain, and compact antenna element. The biggest disadvantage of microstrip antenna is its narrow bandwidth and poor impedance matching capacity. To make microstrip antenna compatible with commercial applications, the bandwidth enhancement and impedance matching of such antennas has to be done. The impedance matching of antenna depends upon type and position of the feed because impedance matching of source at the feeding point of antenna is very important for efficient operation of antenna. In order to improve the impedance matching a dual staircase shaped microstrip patch antenna is used in this paper. The dual staircase patch is fed with a coaxial feed technique. By using IE3D software, the simulated results gives good impedance matching and the bandwidth obtained is 6% at 3.79GHz frequency. The proposed antenna which comes under S-Band (2–4GHz) of microwaves can be used in RADAR applications like weather radar, surface ship radar, and some communications satellites.
Hard Copy Versus Online Services: Results of a Survey (Research Note)

A suroey was conducted of academic libraries at institutions with enrollments of 10,000 or fewer students to detennine if the availability of abstracting and indexing services online has resulted in the cancellation of the equivalent print subscriptions. While suroey findings did suggest that subscriptions to print abstracting and indexing seroices are being cancelled at a significant rate, the availability of online equivalents to those seroices was not found to be the primary reason for the cancellations.
The influence of library manager styles on continuing education of university library practitioners in Vietnam

Purpose – This paper aims to examine and then discuss the positive and negative influences of library manager styles on the engagement of Vietnamese university library staff in continuing education (CE) activities to improve their work performance.Design/methodology/approach – Based on the relevant literature, a preliminary contextual model of factors affecting CE for university library practitioners in Vietnam was developed. Using the model as a framework to guide the data gathering, 17 focus group interviews were held with operational staff and ten individual semi‐structured interviews were conducted with library managers at ten public university libraries across Vietnam.Findings – Three main managerial styles were identified, i.e. directive, decisive and formalistic styles, each of which affected academic library staff CE in different ways. These styles were reflected in the ways the managers operated their libraries and assessed their staff, as well as in the ways they dealt with and coordinated CE ac.
Dependency Structure Applied to Language Modeling for Information Retrieval

In this paper, we propose a new language model, namely, a dependency structure language model, for information retrieval to compensate for the weaknesses of unigram and bigram language models. The dependency structure language model is based on the first‐order dependency model and the dependency parse tree generated by a linguistic parser. So, long‐distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model, where the dependency structure model gives a better performance than recently proposed language models and the Okapi BM25 method, and the dependency structure is more effective than unigram and bigram in language modeling for information retrieval.
Oceanographic visualization interactive research tool (OVIRT)

The Oceanographic Visualization Interactive Research Tool (OVIRT) was developed to explore the utility of scalar field volume rendering in visualizing environmental ocean data and to extend some of the classical 2D oceanographic displays into a 3D visualization environment. It has five major visualization tools: cutting planes, minicubes, isosurfaces, sonic-surfaces, and direct volume rendering. The cutting planes tool provides three orthogonal cutting planes which can be interactively moved through the volume. The minicubes routine renders small cubes whose faces are shaded according to function value. The isosurface tool is conceptually similar to the well-known marching cubes technique. The sonic surfaces are an extension of the 2D surfaces which have been classically used to display acoustic propagation paths and inflection lines within the ocean. The surfaces delineate the extent and axis of the shallow and deep sound channels. The direct volume rendering (DVR) techniques give a global view of the data. Other features include the ability to overlay the shoreline and inlay the bathymetry. There are multiple colormaps, an automatic histogramming feature, a macro and scripting capability, a picking function, and the ability to display animations of DVR imagery. There is a network feature to allow computationally expensive functions to be executed on remote machines.
Simulation of Bubbles Aggregation Process Base on Interface Compression Method

Interface compression method is a VOF model presented in recent years. In this paper, the difference between interface compression method and classic acceptor-donor methods was revealed. The advection test shown the interface compression method was more accurate than HRIC scheme. The bubble aggregation process was simulated by this method using open source CFD tool OpenFOAM. Simulation shown the method can accurately capture the interface in the process of bubble aggregation and the loop flow is a key factor in the process.
An efficient class association rule‐pruning method for unified intrusion detection system using genetic algorithm

Genetic network programming (GNP)‐based class association rule mining has been demonstrated to be efficient for misuse and anomaly detection. However, misuse detection is weak in detecting brand new attacks, while anomaly detection has a defect of high positive false rate. In this paper, a unified detection method is proposed to integrate misuse detection and anomaly detection to overcome their disadvantages. In addition, GNP‐based class association rule mining method extracts an overwhelming number of rules which contain much redundant and irrelevant information. Therefore, in this paper, an efficient class association rule‐pruning method is proposed based on matching degree and genetic algorithm (GA). In the first stage, a matching degree‐based method is applied to preprune the rules in order to improve the efficiency of the GA. In the second stage, the GA is implemented to pick up the effective rules among the rules remaining in the first stage. Simulations on KDDCup99 show the high performance of the proposed method. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Automated Segmentation of MRI of Brain Tumors

An automated brain tumor segmentation method was developed and validated against manual segmentation on 3D-MRI of 20 patients with meningiomas and low grade gliomas. The automated method allows the rapid identification (5-10 minutes operator time) of brain and tumor tissue with accuracy and reproducibility comparable to manual segmentation (3-5 hours operator time) making automated segmentation practical for low grade gliomas and meningiomas.
Gaussian process classification using automatic relevance determination for SAR target recognition

In this paper, a Synthetic Aperture Radar Automatic Target Recognition approach based on Gaussian process (GP) classification is proposed. It adopts kernel principal component analysis to extract sample features and implements target recognition by using GP classification with automatic relevance determination (ARD) function. Compared with k-Nearest Neighbor, Naïve Bayes classifier and Support Vector Machine, GP with ARD has the advantage of automatic model selection and hyper-parameter optimization. The experiments on UCI datasets and MSTAR database show that our algorithm is self-tuning and has better recognition accuracy as well.
Practical Statistics by Example Using Microsoft Excel

From the Publisher: A practical introduction to statistics that fully incorporates Microsoft Excel as the computational tool for data analysis. Detailed instructions for using Excel are built into the book. Statistical concepts are introduced through real data based examples with fully worked out solutions. Key ideas are followed immediately by Self-Test questions for practice. For those with a background in college algebra, but no previous experience with Excel is assumed.
Chemotherapy Drug Scheduling: A Particle Swarm Optimization Approach

This paper presents an investigation into a model of chemotherapy drug scheduling for cancer treatment using feedback control and a bioinspired optimization algorithm, called Particle Swarm Optimization (PSO). The main aim of chemotherapy treatment is to reduce the tumour size. Earlier a feedback control method, namely Integral-Proportional-Derivative (I-PD) was developed in [1] for non-phase specific tumour model to control the drug to be infused into the patient’s body by maintaining a tolerable level of drug concentration at tumour sites. In this work, PSO is used to devise an effective drug scheduling for a phase-specific tumour model by optimizing the parameters of earlier developed I-PD controller. The phase specific cancer tumour model is used to analyze the effects of drug on different cell populations, plasma drug concentration and toxic side effects. Results show that proposed method can reduce the tumour size up to 72% with relatively lower drug doses. Moreover, the method results very low toxic side effects throughout the whole period of treatment. Dhaka Univ. J. Sci. 61(1): 35-40, 2013 (January) DOI: http://dx.doi. org/10.3329/dujs.v61i1. 15093
The Fault Diagnosis Model of FMS Workflow Based on Adaptive Weighted Fuzzy Petri Net

This paper proposes a method for using neural network and weighted fuzzy Petri net to diagnose fault. Aiming at the traditional Petri net can not precisely predict the complex relation of the default phenomenon and the cause, neural network, fuzzy logic and the traditional Petri net are combined, and a constructing method for adaptive weighted fuzzy Petri net model is proposed. Based on this, an improved BP algorism is introduced to train the weight of the model, and the specific process for using the model to diagnose the fault is given. Finally, the model was applied to the instance of FMS, and the model was proved to have the advantages of Petri net and neural network and have reasoning and adaptive ability.
A functional data approach to missing value imputation and outlier detection for traffic flow data

Missing values and outliers are frequently encountered in traffic monitoring data. We approach these problems by sampling the daily traffic flow rate trajectories from random functions and taking advantage of the data features using functional data analysis. We propose to impute missing values by using the conditional expectation approach to functional principal component analysis (FPCA). Our simulation study shows that the FPCA approach performs better than two commonly discussed methods in the literature, the probabilistic principal component analysis (PCA) and the Bayesian PCA, which have been shown to perform better than many conventional approaches. Based on the FPCA approach, the functional principal component scores can be applied to the functional bagplot and functional highest density region boxplot, which makes outlier detection possible for incomplete functional data. Our numerical results indicate that these two outlier detection approaches coupled with the proposed missing value imputation method can perform reasonably well. Although motivated by traffic flow data application, the proposed functional data methods for missing value imputation and outlier detection can be used in many applications with longitudinally recorded functional data.
Underground Water Assessment using Water Quality Index

This study was designed to assess the quality of selected hand dug wells in Maikunkele area of Niger State, Nigeria using Water Quality Index (WQI). Ten hand dug wells were randomly selected in Maikunkele area of Bosso Local Government and were tested for nine (9) parameters of National Sanitation Foundation (NSF) using standard analytical procedures. WQI results indicated that the quality of the selected well water samples were medium except for sample 2 that was extremely bad. The findings also revealed that all the samples except samples 2 and 3 had high coliform levels as high as 91 coliform/100cm. This was an indication of faecal contamination substantiating the proximity of some of the wells to septic systems. The nitrate levels in all the samples exceeded the Maximum Contaminant Level (MCL) of WHO, EPA, APHA and the Nigerian Drinking Water Standards. Based on the results obtained, the quality of the well water samples was therefore not suitable for human consumption without adequate treatment. Regular monitoring of groundwater quality, abolishment of unhealthy waste disposal practices and introduction of modern techniques were highly recommended.
Neighbor-Referencing Cooperative Cache Policy in Content-Centric Network

In-network caching is one of the key aspects of content-centric networks (CCN), while the cache replacement algorithm of LRU does not consider the relation between the cache contents and its neighbor nodes in the cache replacement process, which reduced the efficiency of the cache. In this paper, a Neighbor-Referencing Cooperative Cache Policy (NRCCP) in CCN has been proposed to check whether the neighbors have cached the content. Node will cache the content while none of its neighbors has cached it, therefore reduce redundancy of cached content and increase the variety of contents. Simulation results show that NRCCP has better performance, as the network path had more caching ability and more content popularity densely distributed.
Three-Dimensional Information

Optical depth information, or longitudinal resolution, can be obtained either by using the transversal resolution of two spatially separated observation systems or by using one single system that is sensitive to the traveling time of a signal. Optical transversal information is obtained by comparing the traveling time of signals arriving at two spatially separated points of observation. Thus every optical method to obtain optical three-dimensional information is based on the comparison of time delays caused by the pathlengths that the information carrying signals have to travel. The holodiagram is a bi-polar diagram made to simplify the comparison of distances. It can therefore be used as one unifying method that assists in explaining the possibilities of every method to obtain three-dimensional information.
IR and visual image registration based on mutual information and PSO-Powell algorithm

Infrared and visual image registration has a wide application in the fields of remote sensing and military. Mutual information (MI) has proved effective and successful in infrared and visual image registration process. To find the most appropriate registration parameters, optimal algorithms, such as Particle Swarm Optimization (PSO) algorithm or Powell search method, are often used. The PSO algorithm has strong global search ability and search speed is fast at the beginning, while the weakness is low search performance in late search stage. In image registration process, it often takes a lot of time to do useless search and solution’s precision is low. Powell search method has strong local search ability. However, the search performance and time is more sensitive to initial values. In image registration, it is often obstructed by local maximum and gets wrong results. In this paper, a novel hybrid algorithm, which combined PSO algorithm and Powell search method, is proposed. It combines both advantages that avoiding obstruction caused by local maximum and having higher precision. Firstly, using PSO algorithm gets a registration parameter which is close to global minimum. Based on the result in last stage, the Powell search method is used to find more precision registration parameter. The experimental result shows that the algorithm can effectively correct the scale, rotation and translation additional optimal algorithm. It can be a good solution to register infrared difference of two images and has a greater performance on time and precision than traditional and visible images.
Combined vision/force control at corners in planar robotic contour following

The accuracy and execution speed of a force controlled contour following task is limited if the shape of the workpiece is unknown. This is even more true when the workpiece contour contains corners. This paper shows how a combined vision/force control approach at corners in planar contour following results in a more accurate and faster executed task. The vision system is used to measure on-line the contour and to watch out for corners. The edge is correctly located by incorporating the compliance of the tool/camera set-up in the contour measurement. A simple corner detection algorithm is presented. Once a corner is detected, the finite state controller is activated to take the corner in the best conditions. Experimental results are presented to validate the approach.