Miracle-dz/UniFAR-FTUs huggingface dataset:
A Web Server Cluster Solution Based on Twitter Storm

Single Web server would become a bottleneck that influences the availability and stability of Web service. Ten years ago, what had been proposed is to add Web servers for resolving this problem—Web Server Cluster. In recent years, the concept of cloud computing has got rapid development, and is becoming the future development trend of the IT industry. One of the characteristics of cloud computing is putting lots of computing resources together to provide users with a unified service. In this paper, we have proposed a new Cloud-Based Web Server Cluster Solution, based on the existing cloud computing model—Twitter Storm. It involves a new way to handle the web request from client and some other new features compared to the traditional Web Server Cluster. Combining with cloud computing, it would be the new trend of Web Server Cluster, and its feasibility is described in the paper too.
A synchronous reluctance generator for a wind application-compared with an interior mounted permanent magnet synchronous generator

This article presents a performance comparison of an interior mounted permanent magnet synchronous generator (IPMSG) with a synchronous reluctance generator (SynRG) with the same size for a wind application. It is found that using the same geometrical dimensions, a SynRG can convert 74 % of the power that an IPMSG can convert, while it has 80% of the IPMSG weight. Moreover it is found that the efficiency for the IMPSG is 99 % at rated power compared to 98.7 % for the SynRG.
From Performance Targets to Service Design and Healthcare Infrastructure

Challenging performance targets have been set for the UK National Health Service. In order to respond to these targets, the NHS has adopted approaches such as Lean Healthcare to reduce waste and improve its effectiveness. This paper explores the topic of Performance Management and the consequent redesign of services in the NHS. Using the lens of the TFV model, changes in the NHS are examined in an effort to understand the impact on the demand for infrastructure. The traditional approach in the health service is based on a transformation model concept where functional areas are central and the emphasis is on clinical specialties. Lean Healthcare concepts are now being applied to reduce lead times in the NHS. This represents a transition from a transformation model to one where the flow of patients is the main perspective. In an effort to reduce the lead time for patients to access services, major service redesign efforts have led to changes in the demand for infrastructure and the need for refurbishment and new buildings where functional areas are co-located. The NHS is also moving towards being ‘patient led’ and increasing value to the taxpayer and to the patient. It has been demonstrated that infrastructure has a high impact on patient satisfaction and their choice of health service provider. Thus, as the NHS moves towards a competitive healthcare marketplace, high quality infrastructure is of increasing importance.
Luminance and texture variation analysis for motion detection

We present a statistic algorithm of motion detection in sequences of images acquired by a fixed camera. This algorithm associates two methods of segmentation. The first, called 'unrefined', destined for a perminary determination of zones in movement, is based on a segmentation by division of an original operator making use of three successive images. The second uses a Markovian approach combined with statistic parameters of the second order. This algorithm is characterized by its robustness, its simplicity, and its calculation time.
A VLSI architecture for lifting-based wavelet transform with power efficient

In this paper, an efficient VLSI architecture for biorthogonal 9/7 wavelet transform by lifting scheme is presented. The proposed architecture has many advantages including, symmetrical forward and inverse wavelet transform as a result of adopting pipeline parallel technique, as well as area and power efficient because of the decrease in the amount of memory required together with the reduction in the number of read/write accesses on account of using embedded boundary data-extension technique. We have developed a behavioral Verilog HDL model of the proposed architecture, which simulation results match exactly that of the Matlab code simulations. The design has been synthesized into XILINX xcv50e-cs144-8, and the estimated frequency is 100MHz.
Inference and labeling of metric-induced network topologies

The development and deployment of distributed network-aware applications and services require the ability to compile and maintain a model of the underlying network resources with respect to one or more characteristic properties of interest. To be manageable, such models must be compact; and to be general-purpose, should enable a representation of properties along temporal, spatial, and measurement resolution dimensions. In this paper, we propose MINT - a general framework for the construction of such metric-induced models using end-to-end measurements. We present the basic theoretical underpinnings of MINT for a broad class of performance metrics, and describe PERISCOPE, a Linux embodiment of MINT constructions. We instantiate MINT and PERISCOPE for a specific metric of interest - namely, packet loss rates - and present results of simulations and Internet measurements that confirm the effectiveness and robustness of our constructions over a wide range of network conditions.
A model for dynamic configuration of light-weight protocols

This paper discusses the configuration of light-weight protocols. As opposed to other proposals which introduce a predetermined protocol hierarchy, the configuration is dynamic in the sense that an application can specify requirements of the underlying services and the configuration of a protocol is done with respect to these requirements. The approach discussed in this paper introduces a model for the configuration of protocols comprising of three layers: application, end-to-end communications, and transport infrastructure layer. The dynamic configuration takes place in the end-to-end communications layer. The emphasis of the paper is on the description of the model, a notation for the description of application requirements, and the configuration of protocol entities. The approach is particularly well-suited to make use of any progress in implementations of protocol functions. <<ETX>>
Lossy compression of floating point high-dynamic range images using JPEG2000

In recent years, a new technique called High Dynamic Range (HDR) has gained attention in the image processing field. By representing pixel values with floating point numbers, recorded images can hold significantly more luminance information than ordinary integer images. This paper focuses on the realization of a lossy compression scheme for HDR images. The JPEG2000 standard is used as a basic component and is efficiently integrated into the compression chain. Based on a detailed analysis of the floating point format and the human visual system, a concept for lossy compression is worked out and thoroughly optimized. Our scheme outperforms all other existing lossy HDR compression schemes and shows superior performance both at low and high bitrates.
Ultrafast image reconstruction of a dual-head PET system by use of CUDA architecture

Positron emission tomography (PET) is an important imaging modality in both clinical usage and research studies. For small-animal PET imaging, it is of major interest to improve the sensitivity and resolution. We have developed a compact high-sensitivity PET system that consisted of two large-area panel PET detector heads. The highly accurate system response matrix can be computed by use of Monte Carlo simulations, and stored for iterative reconstruction methods. The detector head employs 2.1x2. 1x20 mm3 LSO/LYSO crystals of pitch size equal to 2.4 mm, and thus will produce more than 224 millions lines of response (LORs). By exploiting the symmetry property in the dual-head system, the computational demands can be dramatically reduced. Nevertheless, the tremendously large system size and repetitive reading of system response matrix from the hard drive will result in extremely long reconstruction times. The implementation of an ordered subset expectation maximization (OSEM) algorithm on a CPU system (four Athlon x64 2.0 GHz PCs) took about 2 days for 1 iteration. Consequently, it is imperative to significantly accelerate the reconstruction process to make it more useful for practical applications. Specifically, the graphic processing unit (GPU), which possesses highly parallel computational architecture of computing units can be exploited to achieve a substantial speedup. In this work, we employed the state-of-art GPU, NVIDIA Tesla C2050 based on the Fermi-generation of the compute united device architecture (CUDA) architecture, to yield a reconstruction process within a few minutes. We demonstrated that reconstruction times can be drastically reduced by using the GPU. The OSEM reconstruction algorithms were implemented employing both GPU-based and CPU-based codes, and their computational performance was quantitatively analyzed and compared.
Refining the terminographer's concept-analysis methods: How can phraseology help?

Concept analysis is both the cornerstone and Achilles ' heel of terminography. This paper describes a linguistically oriented approach to concept analysis, in which terminological phrasemes are seen as potential indicators of conceptual meaning. We propose that phrasemes can assist the terminographer in acquiring conceptual information related to: (1) external influences on the domain, (2) concept identification, (3) concept systems, and (4) particular meaning problems. We conclude that phrasemes provide important conceptual "footholds " for terminologists working in phraseologically rich domains and ideally with machine-readable texts.
Evolution science and infrastructural analysis of the second stir

Argues that the purpose for the existence of the evolution science since the seventeenth century is how to once again understand the evolution science and its origin. Explains, on the basis of this new understanding, how some of the problems dating from the seventeenth century can now be resolved. Finally, briefly describes the method of infrastructural analysis of the second stir.
Characterization of charge-coupled device line and area-array imaging at low light levels

A cell design affording compacting of an active CCD sensor, interline shift sensor, transfer gate and stopper diffusion into 2-mil centers with 5-m aluminum lines and spacings in a 75 × 100 element array will be described. Coherent readout technique removes Nyquist noise and suppresses clock feedthroughs.
Research on model and scheduling algorithm for double-mode VoD servers

In this paper, we design double-mode VoD (video on demand) server (DM-VS). DM-VS provide online on-demand service and offline on-demand service. For offline on-demand service, DM-VS merges several requests accessing the same video clip into a batching multicast that will improve the server's scalability and resource utilization, and decrease the servicing cost. In this paper, we design the model and scheduling algorithm for DM-VS. We use exclusive resource allocation and different resource scheduling algorithms for the two on-demand services. Besides this, we present the cost functions for online on-demand service and offline on-demand service, and analyze the relation between the two functions.
Cooccurrence smoothing for stochastic language modeling

Training corpora for stochastic language models are virtually always too small for maximum-likelihood estimation, so smoothing the models is of great importance. The authors derive the cooccurrence smoothing technique for stochastic language modeling and give experimental evidence for its validity. Using word-bigram language models, cooccurrence smoothing improved the test-set perplexity by 14% on a German 100000-word text corpus and by 10% on an English 1-million word corpus. <<ETX>>
The Development of a Speech Recognition Software Application Using HMM, ANN and LPC Models to Minimize Error

The purpose of this application is to develop speech recognition software applications and test it. The developed speech recognition system for human language solves for processing the voice input and answering the queries of the user. This system begins with an introduction to Speech Recognition Technology then it explains how it works, and the level of accuracy that can be expected. The system is considered for the possible applications of speech technology, including every single task related with the action of the human voice. In this sense, the application fields can vary from speech production, storage, transmission and recognition processes. This research pertains to discuss the theories and models on speech communication and more importantly the use of technology for speech recognition. Several authors’ views are discussed but a great emphasis is given to the Hidden Markov Models (HMM), Artificial Neural Network (ANN) and Liner Predictive Coding (LPC). The aim of the research is to test these models and outline voice recognition algorithms which are important in improving the voice recognition performance. The application is tested with speakers used to represent different tones, emotions and lengths in metres as a sample of relationships methodology, the reports the system permits are used in the results analysis.
Hybrid Algorithm for Face Recognition Using an Evolutionary Software Engineering

The principal aim was the construction of a face recognition system in order to be implemented in the service robot Donaxi, delimited by the Who is who test which is part of the RoboCups tests set, using an evolutionary development strategy of triple iterations. A two phase hybrid algorithm was developed, the first phase aim was the face detection using the Haar classifiers for face search in an image and the second phase is based on a decision tree whereby the faces characteristics were evaluated by the comparison techniques of phase correlation and histogram comparison. The needed characteristics were identified in order to develop this work as a software engineering project which allowed the algorithm construction and implementation through an evolutionary approach and a personal development process. The evolutionary strategy allowed the prototyping development with functionality and the tracking of the final system construction. A three iterations total was realized during which the needed metrics were registered (time, defects and sizes). The final analysis of results (algorithm and methods) allowed concluding and visualizing the employment advantages of a software engineering formal technique for research and robotics projects realization when improving estimations and software production quality.
Design issues in decision feedback MMSE multiuser receivers for high data rate indoor wireless CDMA networks

We consider multiple-access interference (MAI) and intersymbol interference (ISI) suppressing receivers for asynchronous direct-sequence spread-spectrum (DS/SS) code-division multiple-access (CDMA) networks operating over wireless multipath channels with background Gaussian noise. By varying the degree of connectivity in the feedforward filter (FFF), which linearly processes the samples at the output of a chip-matched filter, and feedback filter (FBF), which linearly processes previous decisions, we define a group of decision feedback (DF) minimum-mean-square-error (MMSE) receivers. A performance evaluation for coherent binary 10 Mb/s transmission with processing gains from 7-10 elucidates some of the performance/complexity tradeoffs present in the combined suppression of MAI, ISI, and noise.
A visual sensitivity based low-bit-rate image compression algorithm

In this paper, we present a visual sensitivity based low-bit-rate image compression algorithm. Using the idea that different image regions have different perceptual significance relative to quality, the input image is divided into edges, textures and smooth regions. For the edges, the standard JPEG algorithm with an appropriate quantitative step is applied so that the details can be preserved. For the textures, the JPEG algorithm is applied on the down-scale version. For the smooth regions, a skipping scheme is employed in the compression process so as to save bits. Experimental results show the superior performance of our method in terms of both compression efficiency and visual quality.
Performance Analysis of a Neuro-PID Controller Applied to a Robot Manipulator

The performance of robot manipulators with nonadaptive controllers might degrade significantly due to the open loop unstable system and the effect of some uncertainties on the robot model or environment. A novel Neural Network PID controller (NNP) is proposed in order to improve the system performance and its robustness. The Neural Network (NN) technique is applied to compensate for the effect of the uncertainties of the robot model. With the NN compensator introduced, the system errors and the NN weights with large dispersion are guaranteed to be bounded in the Lyapunov sense. The weights of the NN compensator are adaptively tuned. The simulation results show the effectiveness of the model validation approach and its efficiency to guarantee a stable and accurate trajectory tracking process in the presence of uncertainties.
The Application of Multiple Classifier System for Environmental Audio Classification

Multiple classifier system trains different classifiers and combines their predictions to improve the accuracy of classification. This paper explains the popular algorithms and strategies in multiple classifier system, and points out the key factors to affect the performance of the application of multiple classifier system. The experiments are carried out on given environmental audio data in order to compare the singular classifier methods with multiple classifier system such as Random Forest and MCS, as well as Bagging and AdaBoost. The experimental results show that the multiple classifiers technology outperforms the singular classifier and obtains better performance in environmental audio data classification. It provides an effective way to guarantee the performance and generalization of classification.
Staff Similarity Computation in Technology Innovation Team

The main objective of this investigation is to explore new similarity algorithms of staff similarity in technology innovation team. First, this paper proposes the knowledge representation model of technology staff based on network, and the cliques after clustering according to network feature expresses the sub-fields. Second, from the view of knowledge contained in technology staff, this paper proposes the similarity algorithm based on VSM and the similarity algorithm based on sub-field. Finally, we use the staff classification of one technology innovation team as case study. The experiment results reveal that the similarity of the new methods is accurate than that of the old method, and the information obtained by the new methods is more than that obtained by the old method.
Public and Private Employers’ Perceptions of the Competence of Botswana College of Agriculture Graduates

A large sample size technique was used to select 30 employers each from the private and public sector to examine their perception of the competence of agricultural graduates. Data were collected with a structured questionnaire and analyzed using means, standard deviation, and t test. The results show that, for 16 crop production tasks, public agroemployers were favorably disposed to competence in 4 tasks, while private employers were not favorably disposed to competence in any tasks. Both public and private employers were not favorably disposed to competence in farm engineering tasks. The Botswana College of Agriculture curriculum should be overhauled to better prepare graduates for employment.
Computing Fast Fourier Transforms On Boolean Cubes And Related Networks

High performance architectures are using an ever increasing number of processors. The Boolean cube network has many independent paths between any pair of processors. It provides both a high communications bandwidth as well as the ability to emulate many other networks without contention for communication channels. Of particular interest for the Fast Fourier Transform (FFT) is the ability to emulate butterfly networks, which defines the communication pattern of the FFT. Each node of a Boolean cube network of N nodes has a degree of log2N . For a large number of nodes the number of channels required at the chip boundary may be unfeasibly large with several nodes to a chip, and a network with slightly lower connectivity, such as Cube Connected Cycles networks, may be preferable. The communication system is the most critical resource in many high performance architectures, and its effective use imperative. We describe FFT algorithms that use both the storage bandwidth and the communication sys-tem optimally for an architecture such as the Connection Machine that has 65536 processors interconnected in a Boolean cube related network. We also describe the necessary data allocation, and the allocation and use of the twiddle factors.
Multimodal Analysis: An Integrative Approach for Scientific Visualizing on the Web

The Multimodal approach offers technical communicators and science writers an analytical tool to synthesize the meaning made in the connections across communicative modes. This multimodal synthesis can help technical communicators better exploit the meaning-making potential of multimodal combinations and understand the needs of future generations shaped by their increasingly developed multimodal literacy.
Estimating User’s Preferred Response Bias in an Automated Diagnostic Aid

Operators often perform a target detection task better when assisted by a diagnostic aid of conservative response bias than when assisted by an aid of liberal bias. Little is known, however, about the factors that might influence the preferred level of automation response bias across task contexts. The present study used a psychophysical staircase technique to assess the influence of signal frequency and automation sensitivity on users’ preferences. Subjects performed a signal detection task with the assistance of an automated aid, and were allowed to adjust the aid’s response criterion across trials. A staircase procedure gauged the subjects’ preferred level of automation response bias across factorially-manipulated levels of signal rate (25% vs. 75%) and aid sensitivity (d’ = 1.5 vs. 3.0). The preferred level of bias was generally conservative, but varied as an interaction of signal rate and aid’s sensitivity.