Miracle-dz/UniFAR-FTUs huggingface dataset:
Subjective Duration Assessment: An Implicit Probe for Software Usability

This paper explores a new approach to gauging users’ difficulties with tasks, interfaces, and situations we refer to as subjective duration assessment. The approach, adapted from results described in the interruption literature in psychology, centers on the use of time estimation to characterize performance. We introduce a metric, named relative subjective duration (RSD) that provides a means for probing the difficulty that users have with performing tasks—without directly asking users about the difficulty We do this in order to avoid the inherent bias toward the positive end of the scale typically seen in user satisfaction ratings after usability studies. It has been shown previously that when engaging tasks are interrupted, participants tend to overestimate how long those tasks take when compared to actual task times. Conversely, tasks that are completed tend to be underestimated in terms of the overall task times. We have explored the value of time estimation as a metric for evaluating task performance in HCI. Our hypothesis was that the duration of activity on tasks that are halted before completion would be overestimated, because participants were not able to complete them on their own, while the duration of activity on tasks completed successfully would be underestimated. A user study of a well-known Internet browser explored the efficacy of the metric. Our results show that within deployment constraints, RSD can be a valuable tool for HCI research.
Experimental Study on Effect of Information Behavior by Media Representation Patterns of E-commerce System

The article made study on effect of different media representation pattern of the hierarchy in shopping-on-line system. The result shows that the effect of virtual reality representation pattern is better than that of Text in higher levels of the hierarchy, but more levels in virtual reality pattern increases the errors of user’s activity. It also shows that the media representation pattern of first level have greatest impact on improving performance efficiency.
Validating a short term financial risk model

This thesis project considers validation methods for an existing solvency model for pension funds. The solvency model produces forecasts about the development of financial markets, fund investments, liabilities and, most important, the solvency of the fund. Since the model is a stochastic model, statistical inference is used to compare model outcomes with realized quantities. Several known methods are studied and described in this thesis to execute this model validation. These methods are applied on the solvency model. A testing procedure of risk driver forecasts is implemented and evaluated. Since a lot of data is needed to get a reliable outcome of the validation process, more data from inside th e model must be used and combined to get a better risk model.
Study of Pseudo-Parallel Genetic Algorithm with Ant Colony Optimization to Solve the TSP

Summary The traveling salesman problem (TSP) has attracted many researchers’ attention in the past few decades, and amounts of algorithms based on heuristic algorithms, genetic algorithms, particle swarm optimization, tabu search and memetic algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. This paper is devoted to the presentation of a novel hybrid pseudo-parallel genetic algorithm with ant colony optimization (PPGA-ACO). The experimental results on small and large size TSP instances in TSPLIB (traveling salesman problem library) show that PPGAACO is more robust and efficient than the traditional algorithms.
Supporting smart system applications in scientific gateway environment

Smart system application are gaining significant attention especially concerning health monitoring researches. Data captured from sensors are arriving continuously and for an efficient handling of this large volume of data stream processing would give the ideal solution [6]. Stream processing means that without intermediate storage the data should be processed as they are coming from the sensors. Storing and downloading data is a time-consuming mechanism, and during our investigation we have proved that data downloading time has significant impact on the overall execution of our health monitoring smart application. We have investigated the most common science gateway solution to examine the path that data takes from the outer storage to the executor node or the infrastructure and we have defined an execution model for smart system applications where the data movement is minimal.
Approaches to Modeling the Effects of Fatigue on Cognitive Performance

Behavior representations in simulation environments have become increasingly sophisticated as computational power and understanding of human cognitive processes have increased. Despite improvements in the breadth and depth of models of human behavior, comparatively little research has explored how human cognition is moderated by external and internal factors. In this review paper, we focus on fatigue, a pervasive cognitive moderator that has dramatic and sometimes tragic effects on human cognitive functioning. We discuss prior and current research investigating models of how fatigue impacts human cognition and behavior, sometimes leading to dramatic degradations. The paper ends with a description of our on-going efforts to identify mechanisms for fatigue within the Adaptive Control of Thought - Rational (ACT-R) cognitive architecture. Our approach involves integrating diverse research areas, especially biomathematical modeling of the effects of fatigue on neurobehavioral functioning, and computational cognitive modeling of human cognition and behavior.
Decision support system for investing in stock market by using OAA-Neural Network

In stock market, successful investors can earn maximum profits depended on a stock selection and a suitable time on trading. Generally, investors use two statistical techniques for making a decision, which are the fundamental analysis and the technical analysis. Recently, machine learning models which are a part of artificial intelligence, has been applied to enhance investors for investment. A number of machine learning models have been investigated for stock prediction such as Genetic Algorithms (GAs), Support Vector Machines (SVMs) and Neural Network (NN). In this paper, several multiclass classification techniques using neural networks are investigated. The multi-binary classification experiments using One-Against-One (OAO) and One-Against-All (OAA) techniques are tested and they are compared with the traditional neural network. Furthermore, an alternative data preparation and a data selection process are proposed. The experimental results show that the multi-binary classification using OAA technique outperforms other techniques. It can provide the return on investment greater than the traditional analysis techniques.
International Trends in Park Tourism : The Emerging Role of Finance

sufficiently large that submarkets are apparent. Eagles (1995a) suggests, using a motive-based methodology for segmentation, that the nature-tourism market contains at least four recognizable niche markets: ecotourism, wilderness use, adventure travel, and car camping. Ecotourism involves travel for the discovery of and learning about wild, natural environments. Wilderness travel involves personal re-creation through primitive travel in natural environments that are devoid of human disturbance. Adventure travel is personal accomplishment through the thrills of dominating dangerous environments. Car camping is safe, family travel in the interface between the wild and the civilized (Eagles 1995a). This classification utilizes unique sets of social motives to identify the market segments. Each of the niche markets is at a different stage in the typical business cycle using Butler’s (1980) tourism life-cycle analysis approach. Ecotourism and adventure tourism have considerable growth potential, according to this analysis. Wilderness travel is reaching capacity in many locales because of the requirement of very low-density use in wilderness destinations. Car camping is probably in decline, or soon will be, largely due to the peak population profile of the developed word passing beyond the ages in which camping is popular. All four of these market segments are visible in park tourism internationally. Given the different travel motivations in each submarket, it is important for planners and managers to be aware of the implications for park visitor management—for example, the levels of social grouping, the level of desired service, the level of environmental quality, and the desired environmental attributes vary amongst the four submarkets. It is important to note that such submarket differences only become visible when the naturebased tourism volume reaches a sufficiently large size. Nature-based tourism is a large and growing segment of international tourism. In several countries where the most important export industry is
Complete Sequence of pEC012, a Multidrug-Resistant IncI1 ST71 Plasmid Carrying blaCTX-M-65, rmtB, fosA3, floR, and oqxAB in an Avian Escherichia coli ST117 Strain

A 139,622-bp IncI1 ST71 conjugative plasmid pEC012 from an avian Escherichia coli D-ST117 strain was sequenced, which carried five IS26-bracketed resistance modules: IS26-fosA3-orf1-orf2-Δorf3-IS26, IS26-fip-ΔISEcp1-blaCTX-M-65-IS903D-iroN-IS26, IS26-ΔtnpR-blaTEM-1-rmtB-IS26, IS26-oqxAB-IS26, and IS26-floR-aac(3)-IV-IS26. The backbone of pEC012 was similar to that of several other IncI1 ST71 plasmids: pV408, pM105, and pC271, but these plasmids had different arrangements of multidrug resistance region. In addition, the novel ISEc57 element was identified, which is in the IS21 family. The stepwise emergence of multi-resistance regions demonstrated the accumulation of different resistance determinants through homologous recombination. To the best of our knowledge, this is the first study to identify a multidrug-resistant IncI1 ST71 plasmid carrying blaCTX-M-65, rmtB, fosA3, floR, and oqxAB in an avian E. coli ST117 strain.
Testing the homogeneity of SMEs: The impact of size on managerial and organisational processes

The European Commission (EC) is updating the definition of small and medium sized enterprises (SMEs) following widespread consultation. The EC is focusing on a redefinition of the financial ceiling criterion rather than the ceilings in relation to the number of persons employed in categorising SMEs as micro, small or medium. The retention of a ceiling of 250 employees as the overall employment criterion for SMEs has a major drawback in that it assumes that all firms from micro to medium have broadly similar organisational and managerial processes. Some researchers contend that organisational size based on employee numbers is an important underpinning factor in the way these processes are emphasised. The analysis presented indicates that while size is important, it does not have a significant impact on the managerial and organisational processes examined. These findings lend some support for the retention of the existing size criterion in the definition of SMEs.
Gain Adapted Optimum Mixture Estimator for Single Channel Speech Separation

iii TABLE OF CO TE TS . v LIST OF FIGURES . vii LIST OF ACRO YMS . ix CHAPTER 1: I TRODUCTIO 1.1 Motivation . 1 1.2 Why is Single-Channel Speech Separation Important? 2 1.3 Why is Single-Channel Speech Separation Challenging? 3 CHAPTER 2: BACKGROU D & LITERATURE REVIEW 2.1 Cocktail Party Effect . 4 2.1.1 Binaural Processing . 5 2.1.2 Monaural Processing . 5 2.2 Single Channel Speech Separation . 6 2.2.1 Speech Separation Systems . 8 2.3 Review of Techniques Used For SCSS . 9 2.3.1 Blind Source Separation (BSS) . 9 2.3. 2 Computational Auditory Scene Analysis (CASA) . 13 Biological Foundation of Auditory Scene Analysis . 14 Procedure of CASA . 15 2.3. 3 Model Based SCSS . 17 CHAPTER 3: BLI D SOURCE SEPARATIO 3.1 Mathematical Description of Source Mixing. 19
Integrating Global Regulatory Input Into the Salmonella Pathogenicity Island 1 Type III Secretion System

Salmonella enterica serovar Typhimurium uses the Salmonella pathogenicity island 1 (SPI1) type III secretion system to induce inflammatory diarrhea and bacterial uptake into intestinal epithelial cells. The expression of hilA, encoding the transcriptional activator of the SPI1 structural genes, is directly controlled by three AraC-like regulators, HilD, HilC, and RtsA, each of which can activate the hilD, hilC, rtsA, and hilA genes, forming a complex feed-forward regulatory loop. A large number of factors and environmental signals have been implicated in SPI1 regulation. We have developed a series of genetic tests that allows us to determine where these factors feed into the SPI1 regulatory circuit. Using this approach, we have grouped 21 of the known SPI1 regulators and environmental signals into distinct classes on the basis of observed regulatory patterns, anchored by those few systems where the mechanism of regulation is best understood. Many of these factors are shown to work post-transcriptionally at the level of HilD, while others act at the hilA promoter or affect all SPI1 promoters. Analysis of the published transcriptomic data reveals apparent coregulation of the SPI1 and flagellar genes in various conditions. However, we show that in most cases, the factors that affect both systems control SPI1 independently of the flagellar protein FliZ, despite its role as an important SPI1 regulator and coordinator of the two systems. These results provide a comprehensive model for SPI1 regulation that serves as a framework for future molecular analyses of this complex regulatory network.
Machine vision industrialising the textile-based FRP production

Producing textile preforms for fibre-reinforced plastics (FRP) still involves many manually performed steps. In consequence, the build-up process is characterised by low reproducibility and high costs without quality traceability. This paper illustrates how inline metrology enables the automation of the preform production through machine vision, in spite of the difficult optical properties of the fibre structures. The main focus is on the algorithms for an accurate and robust detection of the fibre orientation under industrial conditions. A structure tensor method and a spectral analysis method are presented in detail. Thus, a control variable is provided to automate the preform build-up.
Randomized Switched Antenna Array FMCW Radar for Automotive Applications

Frequency-modulated continuous-wave (FMCW) methods and switched antenna arrays (SAAs) are very attractive for application in automotive radar because of their low hardware complexity. In traditional SAA radar, the receiving antenna array elements are successively switched according to their natural spatial order [referred to as sequential SAAs (SSAAs)]. For radars using both SSAA and FMCW methods, the phase difference caused by the target azimuth and time delay will be difficult to distinguish, thus resulting in delay-space coupling, which may lead to degradation of the road target observation. In this paper, randomized SAA (RSAA) FMCW radar is proposed. The new radar scheme has high delay/space resolution for target location detection and character extraction, and can also solve the coupling problem. The signal model of RSAA FMCW radar is derived and compared with that of SSAA radar. The influence of the antenna-array switching mode on the radar performance is also analyzed. A conventional zero-padding algorithm and a new clustering FOCUSS algorithm are both introduced for the signal processing of the new radar scheme. The numerical results illustrate the performance of RSAA FMCW radar in high-resolution target imaging and delay-space decoupling.
Robot path simulation: a low cost solution based on CAD

The benefits of robot simulation technology have been recognized by scientists and engineers, with applications ranging from simple robot path simulation to complete robotic cell layout simulation. Robot simulation is one of the essential elements of modern and agile manufacturing plants, as it allows to visualize and test a robotic system, even if it does not exist physically. High-growth industries and emerging manufacturing processes will increasingly depend on advanced robot technology such as robotic simulation. Robot path simulation is a very useful process to predict and pre-evaluate performance of robot programs generated off-line. This paper presents a simulation system where a relatively low cost and commercially available 3D CAD package is used as an interface to visualize/simulate pre-programmed robot paths. The developed system is intuitive and friendly, so that in a few minutes any user without knowledge of CAD and robot programming will be able to simulate robot paths and visualize it in a CAD environment. This way, this simulator can be useful for small and medium sized enterprises and for educational purposes. Three different experiments (simulation of robot motion/paths) are presented and discussed: a material handling task; a robot performing work in the footwear industry (shoes soles); and finally, the simulation of robot paths for a robot operating in a bending cell. The experimental results showed that the proposed system is flexible, easy to use and efficient. This paper also covers topics like how simulation makes robot programming easier, advantages and disadvantages of simulation in robotics, and the future trends in this field. Finally, the results of the experiments will be analyzed and discussed. The pros and cons of the system in relation to off-the-shelf robot simulation packages are analyzed.
A voltage-scalable 10-b pipelined ADC with current-mode amplifier

This paper presents an energy-efficient 10-b pipelined ADC with a current-mode amplifier. The proposed amplifier achieves high gain, low static power consumption and supply voltage scalability without any calibration or timing control. The fabricated ADC in 65-nm CMOS process achieves FOMs of 14.3-to-36.9 fJ/c-s with a supply voltage range from 0.6-V to 1.0-V.
Speaker Recognition Based on the Use of Vocal Tract and Residue Signal LPC Parameters

The problem of text-independent speaker recognition based on the use of vocal tract and residue signal LPC parameters is investigated. Pseudostationary segments of voiced sounds are used for feature selection. Parameters of the linear prediction model (LPC) of vocal tract and residue sig- nal or LPC derived cepstral parameters are used as features for speaker recognition. Speaker iden- tification is performed by applying nearest neighbour rule to average distance between speakers. Comparison of distributions of intraindividual and interindividual distortions is used for speaker verification. Speaker recognition performance is investigated. Results of experiments demonstrate speaker recognition performance.
To Professor ART Solarin on his 60th Birthday Celebration SOME NORMAL CONGRUENCES IN QUASIGROUPS DETERMINED BY LINEAR-BIVARIATE POLYNOMIALS OVER THE RING

In this work, two normal congruences are built on two quasigroups with underlining set Zn relative to the linear-bivariate polynomial P (x, y) = a+ bx+ cy that generates a quasigroup over the ring Zn. Four quasigroups are built using the normal congruences and these are shown to be homomorphic to the quasigroups with underlining set Zn. Some subquasigroups of the quasigroups with underlining set Zn are also found. 2000 Mathematics Subject Classification: 20N02, 20N05.
An approach based on TSA-tree for accurate time series classification

In order to improve the performance of time series classification, we introduce a new approach of time series classification. The first step of the approach is to design a feature exaction model based on Trend and Surprise Abstraction tree (TSA-tree). The second step of the approach is to combine the exacted global feature and 1 nearest neighbor to classify time series. The proposed approach is compared with a number of known classifiers by experiments in artificial and real-world data sets. The experimental results show it can reduce the error rates of time series classification, so it is highly competitive with previous approaches.
MONTE-CARLO AND QUASI-MONTE-CARLO METHODS FOR NUMERICAL INTEGRATION

We consider the problem of numerical integration in dimension s, with eventually large s; the usual rules need a very huge number of nodes with increasing dimension to obtain some accuracy, say an error bound less than 10−2; this phenomenon is called ”the curse of dimensionality”; to overcome it, two kind of methods have been developped: the so-called Monte-Carlo and Quasi-Monte-Carlo methods. Very good and up-to-date monographs on the subject exist; our purpose in the present survey is only to present the basic constructions in the two approaches, with a special insight on the second one which performs better for numerical integration and sets the trend with randomized hybridations . To avoid technicalities, we restrict ourselves to the integral domain I = [0, 1] and to the more commonly used sets of nodes which are already implemented in computers routines; we also leave out the so-called Lattice methods to keep an appropriate length to this proceeding paper. The reader interested who should like to go further in the subject should consult the monographs of H.Niederreiter [N], S.Tezuka [T], M.Drmota-R.Tichy [DT], B.L. Fox[F], J.Matoušek [M] (in chronological order) and also of J.E. Gentle [G] for MC Methods and I.H. Sloan-S.Joe [SJ] for Lattice methods; the collective Springer Lecture notes in Statistics 138 [HL] and the proceedings of the three Conferences MCQMC published by Springer (Lectures Notes in Statistics 106 [NSh], 127 [NHLZ], and the book published this year [NSp]) present the main contributions during the five last years; good references on the Number Theory background for Irregularities of Distribution are the books of J.Beck-W.W. L.Chen [BC] and L.Kuipers-H.Niederreiter [KN].