Miracle-dz/UniFAR-FTUs huggingface dataset:
BLAST on UPMEM

This paper presents the implementation of the BLAST software on the UPMEM architecture. BLAST is a well-known molecular biology software to rapidly scan DNA or protein genomic banks. It is daily used by thousands of biologists. Given a query sequence, BLAST reports all the sequences of the bank where similarities are found. The result is a list of alignments that detail the similarities. UPMEM’s Processing-In-Memory (PIM) solution consist of adding processing units into the DRAM, to minimize data access time and maximize bandwidth, in order to drastically accelerate data-consuming algorithms. A 16 GBytes UPMEM-DIMM module comes then with 256 UPMEM DRAM Processing Units (named DPU). To find similarities, BLAST proceeds in 3 steps: (1) search of common words between the query sequence and the bank sequences; (2) evaluation of local similarity on the neighbourhood of these words; and (3) computation of the final alignment. As the 2 first steps are limited by memory bandwidth, and represent the majority of time of the overall computation, they have been massively parallelized on UPMEM DPUs. The 3rd step is performed on the host processor and is overlapped with the UPMEM processing. Experimentation on real datasets shows a speed-up of 25 when using UPMEM configuration, versus a standard server running 20 Intel cores.
A Unit Touch Gesture Model of Performance Time Prediction for Mobile Devices

Objective: The aim of this study is to propose a unit touch gesture model, which would be useful to predict the performance time on mobile devices. Background: When estimating usability based on Model-based Evaluation (MBE) in interfaces, the GOMS model measured ‘operators’ to predict the execution time in the desktop environment. Therefore, this study used the concept of operator in GOMS for touch gestures. Since the touch gestures are comprised of possible unit touch gestures, these unit touch gestures can predict to performance time with unit touch gestures on mobile devices. Method: In order to extract unit touch gestures, manual movements of subjects were recorded in the 120 fps with pixel coordinates. Touch gestures are classified with ‘out of range’, ‘registration’, ‘continuation’ and ‘termination’ of gesture. Results: As a results, six unit touch gestures were extracted, which are hold down (H), Release (R), Slip (S), Curved-stroke (Cs), Path-stroke (Ps) and Out of range (Or). The movement time predicted by the unit touch gesture model is not significantly different from the participants’ execution time. The measured six unit touch gestures can predict movement time of undefined touch gestures like user-defined gestures. Conclusion: In conclusion, touch gestures could be subdivided into six unit touch gestures. Six unit touch gestures can explain almost all the current touch gestures including user-defined gestures. So, this model provided in this study has a high predictive power. The model presented in the study could be utilized to predict the performance time of touch gestures. Application: The unit touch gestures could be simply added up to predict the performance time without measuring the performance time of a new gesture.
International Journal for Research and Development in Engineering

Search Engines are tremendous force multipliers for end hosts trying to discover content on the Web. As the amount of content online grows, so does dependence on web crawlers to discover relevant content. The motive is to develop an efficient Web Crawler that will give results more relevant to search keyword and faster, which will support Semantics extraction, multithreading and distributed computing. Full Text: PDF DOI: http://dx.doi. org/10.11591/ijset.v1i2. 4571
TOTAL QUALITY MANAGEMENT AND CHANGES IN MANAGEMENT ACCOUNTING SYSTEMS IN A MANUFACTURING FIRM: A CASE STUDY

This paper explores the emergence of Total Quality Management (TQM), its continuation as well as the management accounting implications in a leading solid tyre manufacturing firm in Sri Lanka, where TQM has been practiced for over fifteen years. The paper adopts the qualitative methodology and case study approach, while the rational-choice perspective and new institutional sociology have been utilized as the theoretical lens. Our findings suggests that both institutional as well as technical efficiency reasons have influenced the implementation of TQM, and that the management accounting system of the firm has been changed to keep up with the TQM system. How management accounting systems get implicated in a TQM environment, although important has received limited research interest; thus this research is a useful addition to the existing literature. This paper also has theoretical merits; it depicts how coming together of a rational-choice perspective and new institutional sociology provides a better understanding on internal and external reasons for implementing management tools (such as TQM). Using a TQM example, our paper provides learning points to practicing managers on how management accounting systems could be shaped to facilitate implementation of management tools.
An Optimal Factor Analysis Approach to Improve the Wavelet-based Image Resolution Enhancement Techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods.
A Collaborative Filtering Recommendation Algorithm with Time Context for Learning Interest Mining

A time context based collaborative filtering recommendation( TCCF-LI) algorithm this paper was proposed,and students' learning interest mining from university library borrow record was implemented. The time context information was imported into the traditional collaborative filtering recommendation algorithm,in which,both interest homoplasy of large scale user groups and short-term correlation of small scale user groups was considered. Good recommendation performance was gained. According to the experiments on real dataset,TCCF-LI algorithm presents higher precision and recall rate compared with traditional recommendation algorithm.
“The 2-Dimensional Approach”: a novel tool to help learners organize their knowledge and improve their clinical reasoning skills.

This article was migrated. The article was marked as recommended. Background: Clinical reasoning is an essential competence of a physician. Particularly, pathophysiological understanding is the key for novices to organize their knowledge and improve clinical reasoning. To this end, we propose a “2-Dimensional (2D) Approach” that visualizes pathophysiology by using a matrix of organs and systems. Methods: This study was a prospective observational study of 100 residents (PGY-1), who attended a lecture on the 2D Approach. They underwent a pre- and post-test that assessed their ability to list differentials for various symptoms and to choose the most likely diagnosis for a case vignette. We stratified the participants according to their baseline knowledge level on the pretest into 2 groups (high-knowledge and low-knowledge). In each of the 2 groups, we compared the change in the pre- and post-test scores between participants who used the 2D Approach during the posttest and those who did not. Results: In the high-knowledge group (n=59), the change in the pre- and post-test scores was significantly larger in the 2D Approach users than non-users (p=0.046), while in the low-knowledge group (n=41), there was no significant difference in the change of the scores between the 2D Approach users and non-users. Conclusions: On condition that residents have sufficient knowledge, the 2D Approach helps them organize their knowledge and improve their clinical reasoning skills, through visualization of pathophysiology on a matrix of organs and systems.
An Object-Tracking System Using Particle Filter

Based on TMS320C5416 and FPGA, a practical system for tracking of target using particle filter is presented. The original image is firstly sampled by SAA7111A, and then transmitted to DSP by Ping-Pang Method via FPGA. The gray feature is selected as the object characteristic and particle filter is used for object tracking. Fixed-point operation is used instead of floating-point operation for increasing the speed of computation. Experimental results show that the system operates well in real-time and is robust.
Multi-Scale Color Image Enhancement Algorithm Based on Color Space and Human Visual System (HVS)

Based on the analysis of human visual system characteristics to brightness and color, we present a novel algorithm with considering about wavelet decomposition and color space for color image enhancement.The uniform color space CIE La b is used and a hybrid contrast enhancement algorithm for color image is proposed. Color image enhancement exists two goals: image looking naturally and looking vividly. Experimental results confirm that improved color image enhancement is achieved.
The Classification and Recognition of Neurons Form

In understanding neurons characteristics,basic problem is classification for neurons. Neurons forms are complicated,neurons recognition and classification are difficult.This paper provides statistical analysis for reference datas,translates datas into index parameters corresponding to each neuron space form,then determines key indexes for recognizing various types of neurons by using qualitative analysis and contrast analysis.Analytic Hierarchy Process(AHP),graphics section,and the geometrical similarity of analysis method are used to establish the classification model.This model can classify neuron types provided in appendix accurately.Data analysis is finished by using open-source software Scilab,running environment is Scilab5.2,part of codes are developed based on VS2008.
Schooling, wages and the role of unobserved ability in the Philippines

Yaraslau Zayats: Schooling, Wages, and the Role of Unobserved Ability in the Philippines (Under the direction of Thomas Mroz) The dissertation analyzes the impacts of an individual’s unobserved ability on schooling and wages in the context of a developing country using rich data from the Cebu (Philippines) Longitudinal Health and Nutrition Survey. Unlike any previous study, my model allows for grade repetition and school reentry after dropping out of school. Both phenomena are common in developing countries in general, and in the Philippines in particular. Semiparametric approach is used to control for an individual’s unobserved ability. The results indicate that children with lower innate ability enter school at a later age and complete fewer years of school. They are also more likely to drop out of school at all levels of education, but the effect of lower ability diminishes at higher levels of education. A standard Mincer-type regression appears to be misspecified. Results strongly suggest presence of heterogeneity in the returns to education by an individual’s ability. Rates of return to education appear to be nonlinear differing across three educational levels.
Indexes of Drivers' Visual Interesting Region and Influencing Factors

In order to quantitative analysis the charactersitic of drivers' visual search,the concept of Viusal Interesting Region(VIR),which includes area per unit time and barycentric coordinate,was proposed.On this basis,the eye movement indexes were obtained by tests,the area per unit time and the barycentric coordinate of VIR were cacluated using Matlab.Visual information can be classified as information of highway alignment,driving state and traffic flow,whose influencing factors are horizontal alignment,operating speed and frontage vehicle numbers.Finally,based on the eye movement data obtained by the tests,the model of the relation of VIR indexs with influencing factors was established by qualitative analysis and regression.
Coding and signal processing for high density granular magnetic recording systems

The Hard Disk Drive (HDD) industry has, in recent years, struggled with attempting to attain continual growth in storage capacity to meet the needs of a rapidly growing storage demand in the information age. Conventional Perpendicular Magnetic Recording (PMR) is already hitting its limit with areal density growth slowing down considerably in recent years. In moving towards higher densities, PMR faces the problem of simultaneously shrinking the size of bits and ensuring the media remains writable without compromising the thermal stability of its magnetic grains. To overcome this, various novel recording architectures have been proposed, namely Heat Assisted Magnetic Recording (HAMR), Bit Patterned Magnetic Recording (BPMR), Shingled Magnetic Recording (SMR) and Two-Dimensional Magnetic Recording (TDMR). HAMR overcomes the trilemma by injecting thermal energy into a high coercivity and thermally stable media (needed for high density) that would otherwise require an excessively strong head field to write. BPMR organizes magnetic grains into a defined lattice and stores bit on single grains known as islands. SMR targets for a higher track density by writing data tracks using a shingling technique. TDMR uses the same writing mechanism as in SMR. In TDMR however, information is read simultaneously from multiple tracks through the use multiple reader heads. To attain massive storage improvements in the near future, the industry needs to invent new ideas across the entire HDD platform. Development of new recording architectures, concurrent with the development of new trends in signal processing are essential in paving the way to new and intriguing prospects of significant performance gains. As such, the first part of the thesis focuses on a system level study of the recently proposed SMR and HAMR channel. The formulation and evaluation of theoretical models for these emerging recording technologies is essential in enabling an accurate prediction of its feasibility. Magnetic researchers often employ
A Visual Tracking Deep Convolutional Neural Network Accelerator

Convolutional Neural Network (CNN) has been widely used in computer vision problems including image classification, object detection and tracking and semantic segmentation which has obtained state-of-art results. However, CNN is both computation and bandwidth intensive. At present, we usually train and use CNN on CPU, GPU and even GPU clusters which are impractical for embedded platforms. Therefore, we need design CNN accelerator for low power, high computation and high bandwidth processing. There are already some accelerator which is mainly accelerate 2-D convolution, but not high energy-efficiency for 3-D convolution. So we propose an architecture which is effective for large depth 3-D convolution and we support three different scales kernel size. We use a visual tracking CNN algorithm-MDNet to verify our architecture and make no accuracy loss. We evaluate our system using 65nm library which has a small footprint of 2.58 mm and 224 mW.
Rice Monitoring Using Sentinel-1 Data in the Google Earth Engine Platform

Rice is the most essential and nutritional staple food crop worldwide. There is a need for accurate and timely rice mapping and monitoring, which is a pre-requisite for crop management and food security. Recent studies have utilized Sentinel-1 data for mapping and monitoring rice-growing areas. The present study was carried out in the Google Earth Engine (GEE), where the Sentinel-1data were used for monitoring the rice-growing area over Kulithalai taluk of Karur district, located along the Cauvery delta region. Normally, the production of rice in the study area starts in the late Samba Season where the long duration variety Cr1009 (130 days) is extensively grown. The results exhibit low backscattering values during the transplanting stage of VV and VH polarization (−15.19 db and −24 db), whereas maximum backscattering is experienced at the peak vegetation stage of VV and VH polarization (−7.42 and −16.9 db) and there is a decrease in the backscattering values after attaining the maturity stage. Amongst VH and VV polarization, VH polarization provides a consistently increasing trend in backscatter coefficients from the panicle initiation phase to the early milking phase, after which the crop attains its maturity phase, whereas in VV polarization, an early peak of backscatter coefficients is seen much earlier during the flowering phase itself. Thus, in this study, VV polarization gives better interpretation than VH polarization in the selected rice crop fields. The obtained results were cross-validated by collecting the ground truth values during the satellite data acquisition time, throughout the crop growing period from the selected rice fields.
User profiling on the basis of interactions with a computer game

Adapting computer software to the individual user during run-time could offer substantial advantages over the current practice of tailoring software to groups of users during the development process (Stewart 2007; Charles et al. 2005; Charles & Black 2004; Houlette 2004). In order to achieve this, the computer requires information about the user, yet its ability to perceive them is severely limited (Suchman 2006 p.167; Fisher 2001). In an effort to address this shortcoming, this dissertation examines the potential for determining an individual’s personality through analysis of their interactions with commercial computer games – which, in common with cinema and literature, work on an underlying model of reality – as well as their performance in game elements using an underlying general intelligence factor, and their emotional state from visual and physiological cues. Through a program of original primary research, it demonstrates that data pertaining to several of the big five personality factors can be captured from interactions with a commercial computer game, and explores methods for predicting these personality traits using regression analysis and clustering techniques. It also employs a series of factor analyses to investigate the latent variables present in interactions with a computer role-playing game, as a foundation for further work.
score vragenlijst poliklinieken 20140404 A

No Yes Under development (choose the best possible answer) 0.000 0.167 0.083 1 Is with respect to the protocol recorded in writing x 0.17 0.17 2 Its contents is based on evidence-based standards and guidelines x 0.17 0.17 3 Is with regard to the agreements on organisation and implementation recorded in writing x 0.17 0.17 4 Includes a description of the functions of all the different care providers x 0.17 0.17 5 Is available in writing for all care providers involved x *) 0.17 0.17 6 Is structurally maintained and updated x *) 7 Contains a description of all the steps followed by the patient follows (care pathway) x 0.17 0.17 *) The maximum score of sub question 5 and 6 counts 1.00 1.00
Digital Media and Technologies for Virtual Artistic Spaces

Dew Harrison is a Professor of Digital Media Art and works as the Associate Dean for Research and Postgraduate Study at the University of Wolverhampton, School of Art and Design, where she is also the Director of CADRE, Centre for Art, Design, Research and Experimentation. She is a researcher and practitioner with a BA in Fine Art, an MA in Contemporary Art Theory, an MSc in Computer Science and a PhD from the Planetary Collegium, CAiiA, in Interactive Art. Her practice undertakes a critical exploration of Conceptual Art, semantic media and intuitive interfaces where she often works collaboratively and considers virtual curation a form of art practice. She continues to show her work internationally, most recently two of her Digital Action Painting series were exhibited at the Centre for Contemporary Art in Poland, and has over 50 publications to date spanning digital art, consciousness studies, interactive games, art history and museology. Dew Harrison (University of Wolverhampton, UK)
Optimal Reservoir Operation Using GeneticAlgorithm: A Case Study of Ukai ReservoirProject

Application of optimization techniques for determining the optimal operating policy of reservoirs is a major issue in water resources planning and management. Genetic Algorithm is an optimization technique, based on the principle of natural selection, derived from the theory of evolution, are popular for solving optimization problems. The main aim of the present study is to develop a policy for optimizing the release of water for the purpose of irrigation. The fitness function used is minimizing the squared deviation of monthly irrigation demand along with the squared deviation in mass balance equation. The months considered are from July to October for three years from year 2007 to 2009. The decision variables are monthly releases for irrigation from the reservoir and initial storages in reservoir at beginning of the month. The constraints considered for this optimization are the bounds for the releases and reservoir capacity. Results show that in the year 2007, for months of July and August, 625 and 1573.86 MCM of water is saved respectively. In the year 2008, for July 65.67 MCM, August 27.15 MCM, September 35.32 MCM and October 62.91 MCM of water is saved. In the year 2009, for July 49.18 MCM, August 35.48 MCM, and in October 43.51 MCM of water is saved. Hence, GA model, if applied to the Ukai reservoir project in Gujarat State, India, can completely satisfy downstream irrigation demands and releases are minimized which leads to considerable amount of saving in water.
Application of Simulation Software in the Course of Numerical Controlled Technique

This article introduced the application of simulation software in the course of numerical controlled technique.Using this for three dimensional simulation is equivalent to the real operation on the CNC machine.It helps to avoid the puzzledom of low efficiency and poor result due to the shortage of capital or the high cost on equipment during the course of numerical controlled technology.It has also improved students' capability to programme numerical control,operate the numerical controlled machine tool and maintain the system satisfactorily.
Relevance of General Education: An Assessment of Undergraduate Business Students

In this study, the relevance of general education among undergraduate business students is assessed. Zaichkowsky’s (1985) personal involvement inventory (PII) is used to assess perceived relevance. Data are collected via a survey administered to undergraduate business students at a Midwest, regional state university. The cognitive dimension of the involvement construct was evaluated with 7-point semantic differential-type scales measuring five items: important, relevant, means a lot to me, valuable, and needed. The affective dimension of the involvement construct was evaluated with exciting, appealing, and fascinating items also measured on a 7-point semantic differential-type scale. The results suggest strong support for the reduced set of eight-items from Zaichkowsky’s (1994) scale modification. The proposed scale reduction appears to capture both affective and cognitive dimensions within the context of higher education.