Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Users Request for Attentive Deskwork Support System

Since the late 1990s, several studies have been conducted on intelligent systems that support daily life in the home or office environments (Sato et al., 1996; Pentland, 1996; Brooks, 1997). In daily life, people spend a significant amount of time at desks to operate computers, read and write documents and books, eat, and assemble objects, among other activities. Therefore it can be said that supporting deskwork by intelligent systems is of extreme importance. Many kinds of intelligent systems have been proposed to provide desktop support. In particular, augmented desk interface systems have been eagerly studied. DigitalDesk is one of the earliest augmented desk interface systems (Wellner, 1993). It requires a CCD camera and a video projector to integrate physical paper documents and electronic documents. Koike et al. proposed EnhancedDesk, which uses an infrared camera instead of a CCD camera to improve sensitivity to changes in lighting conditions and a complex background (Koike et al., 2001). In addition, Leibe et al. proposed one called Perceptive Workbench, which requires both a CCD and an infrared camera (Leibe et al., 2000), and Rekimoto proposed SmartSkin, which is based on capacitive sensing without cameras (Rekimoto, 2002). Raghavan et al. proposed a system that requires a head-mounted display to show how to assemble products (Raghavan et al., 1999). These systems have been limited to show some information to the user. Ishii & Ullmer proposed an idea referred to as "tangible bits (Ishii & Ullmer, 1997)," which seeks to realize a seamless interface among humans, digital information, and the physical environment by using manipulable objects. Based on this idea, they proposed metaDESK (Ullmer & Ishii, 1997). Pangaro et al. proposed a system called Actuated Workbench (Pangaro et al., 2002), and Noma et al. proposed one called Proactive Desk (Noma et al., 2004). Both systems convey only information to the user through movement of physical objects. They do not support the user from physical aspects. On the other hand, especially in rehabilitation robotics, several studies have been conducted on supporting humans working at desks from a physical aspect (Harwin et al., 1995; Dallaway et al., 1995). Dallaway & Jackson proposed RAID (Robot for Assisting the Integration of Disabled people) workstation (Dallaway & Jackson, 1994). In RAID, a user selects an object through a GUI, and a manipulator carries it to the user. Ishii et al. proposed a meal-assistance robot for disabled individuals (Ishii et al., 1995). The system user points a laser attached to his head to operate a manipulator. Topping proposed a system, Handy 1, 16
Assessment of Student Achievement in Introductory Physical Geology: A three-year study on delivery method and term length

Four strong hurricanes impacted a large portion of Florida coast within a month during the summer of 2004. Hurricane Ivan impacted nearly the entire Florida panhandle coast causing tremendous overwash deposits. Hurricanes Frances and Jeanne, impacting nearly the same location along south Florida Atlantic coast, also resulted in substantial overwash deposits. Overwash from Hurricane Charley is limited. This study compares the overwash deposits from Hurricanes Frances-Jeanne and Ivan. The overwash deposits from Frances and Jeanne are relatively restricted in aerial extent, being localized north of the eye and mostly within 30 km. In contrast, overwash deposits from Ivan are far more extensive, stretching over 120 km from the eye. Overwash from France-Jeanne typically transgressed into the backbarrier mangrove swamp and displays a tongue shape. The shape and extent of the overwash fans/tongues are influenced by the vegetation density. The extensive overwash from Hurricane Ivan typically does not display an overall fan shape. The different overwash extents and sedimentological characteristics are influenced by both hurricane intensity and pre-storm morphology, particularly the latter for this case.
Dynamic location area management and performance analysis

A dynamic location area scheme is proposed for cellular networks, in which the size of location areas of a user is dynamically determined according to its current incoming call arrival rate and mobility as the signaling traffic in radio channels reaches the minimum. The protocols and algorithms of the proposed scheme are presented, and its performance is compared with that of the conventional system.
Memristor-CNTFET based Ternary Comparator unit

This paper proposes a new design for ternary logic comparator unit based on memristive threshold logic concept. To provide high-performance design, integrating memristor and Carbon Nano-Tube Field-Effect Transistor, CNTFET, is used. A comparison with other related work is presented to discuss performance aspects. It shows that performance has been improved by 75% compared with the other related work. Therefore, the proposed design is very promising to build high-performance full ternary ALU memristor-based unit.
Techniques for cloth animation

High speed cutters are attached adjacent the outlet of a wire cage making machine to sever individual cages as they issue from the machine. Upon actuation, a positioning piston swings the jaws of each cutter into a cutting position around each longitudinal cage wire and simultaneously opens a control spool to admit hydraulic fluid to a power cutting piston to sever the wire. The pistons and jaws then quickly retract to avoid interference with the advancing wires of the next cage. A hoist neutralizes the weight of the cut cage while the wrap wires are cut, and a second hoist automatically transports and deposits the cage at a preselected location.
A New Training Algorithm for RBF Neural Network based on Dynamic Fuzzy Clustering

A new algorithm for training radial basis function neural network (RBFNN) is presented in this paper. This algorithm is based on the dynamic fuzzy clustering method (DFCM). The algorithm has a number of advantages compared to the traditional method based on k-means. For example, it does not need to know the number of the hidden nodes and to predicts more accurately. Due to these advantages, this method proves to be suitable for developing models for complex nonlinear systems.
Effects of pharmacist-led patient education on diabetes-related knowledge and medication adherence: A home-based study

Objective: Patient education is key to the management of acute and chronic conditions. However, the majority of such educational interventions have been reported from health-care settings. In contrast, this study aims to evaluate whether a home-based intervention can result in better understanding about type 2 diabetes mellitus and can increase adherence to prescribed medications. Method: A non-clinical randomised controlled trial was conducted whereby participants received a home-based educational intervention through a registered pharmacist. A total of 150 patients were randomly assigned to two groups (75 patients in each arm). Diabetes knowledge and medication adherence were measured by means of the 14-item Michigan Diabetes Knowledge Test and the 8-item Morisky Medication Adherence Scale, respectively. Results: No significant differences were observed in either group for demographic variables. There was, however, a significant increase in the participants’ levels of knowledge about type 2 diabetes mellitus and medication adherence in the home-based intervention group at the completion of the intervention (p < .001). Significantly lower HbA1c levels were also observed among the home-based intervention group following the intervention (p < .001). Conclusion: A pharmacist-led home-based intervention can significantly increase disease-related knowledge and medication adherence in patients with type 2 diabetes mellitus. Home-based interventional programmes offer a promising method of patient education and counselling.
Proposed Efficient and Enhanced Algorithm in Cloud Computing

Cloud computing, as of today has boomed and enhanced the use of internet as well as network services where the capability of one node can be used by other nodes. Gigantic-scale heterogeneous distributed computing environments (like Clouds and Computational Grids) can easily access the vast amount of computing resources at very economical cost. This has implications for many technical issues in Service Oriented Architectures and Internet of Services (IoS)-type applications; including high interoperability, fault tolerance, high availability and scalability. Central to these issues is the establishment of effective load balancing techniques. It is clear that the scale and complexity of these systems makes centralized assignment of jobs to specific servers infeasible; requiring an effective distributed solution. To properly manage the resources of the service provider, we require balancing the load of the jobs that are submitted to the service provider.
Quadriceps and hamstrings strength in team sports: Basketball, football and volleyball

Isokinetic strength evaluation of the quadriceps (Q) and hamstrings (H), in both concentric (con) and eccentric (ecc) mode of contraction, is an important part of the comprehensive evaluation and rehabilitation of athletes. Systematic preseason isokinetic evaluation of the knee is very useful in highlighting the athletes at risk for those injuries as well as for the optimization of the training process. The main purpose of this short review was to present the major findings of studies relating to this issue.
Statistical Learning with Sparsity: The Lasso and Generalizations

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of 1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.
A beam switching slot array with a 4-way Butler matrix installed in a single layer post-wall waveguide

The authors propose a switched beam slot array antenna with the 4-way Butler matrix. All are integrated in one substrate. Experiments in the 26 GHz hand confirm the key operation of this structure of the antenna; four almost-identical beams are switched to cover the total horizontal 90-degree sector with equal angular spacing. A gain of 21.3 dB was observed though two beams have a lower gain by about 1.8 dB due to the grating lobes. A narrower waveguide could eliminate the defects.
Estimating transition rates for multistate models from panel data and repeated cross-sections

Niet zelden worden `onvolledige' data bronnen, zoals panel data en herhaalde dwarsdoorsneden, gebruikt voor schatting van de parameters van multistate modellen in continue tijd. Er is echter nog weinig bekend over de betrouwbaarheid van die schattingen. In dit proefschrift worden methoden besproken voor de schatting van Markov modellen in continue tijd op basis van panel data en herhaalde dwarsdoorsneden. De betrouwbaarheid van de methoden wordt onderzocht met behulp van simulatie. Het simulatie-onderzoek van panel data toont aan het EM algoritme en de methode van maximum likelihood schatting op basis van `volledige' data een vertekend beeld geven. De vertekening hangt af van het gebruikte model en van de steekproef. Andere methoden die gebruik maken van de maximum likelihood methode leiden niet tot een systematisch verschil tussen verwachte waarden van de schatters van de transitiecijfers en de werkelijke waarden. Resultaten van het onderzoek met herhaalde dwarsdoorsneden tonen aan dat de onderzochte methoden toegepast mogen worden op Markov modellen met twee of drie toestanden en transitiecijfers die veranderen met leeftijd. De twee beste schattingsmethoden voor panel data worden toegepast op gegevens van de US Health and Retirement Study om inzicht te krijgen in het verloop van beperkingen op hogere leeftijd. De twee methoden geven vergelijkbare resultaten. Het gladstrijken van transitiecijfers naar leeftijd leidt tot betere resultaten dan transitiecijfers die leeftijdsspecifiek zijn. Het onderzoek bevestigt de resultaten van gepubliceerd onderzoek.
Generalized Exponential Poisson Model for Software Reliability Growth

Software reliability modeling is challenging since no single Software Reliability Growth Model (SRGM) is considered suitable in all situations owing to poor goodness of fit, lack of predictive validity of the models and their sensitivity to fluctuations in the number of failures in the data sets. In this paper, we propose a NonHomogenous Poisson Process Model whose failure intensity function has the same Mathematical form as that of the probability density function (pdf) of a generalized exponential distribution. The performance of the proposed model was verified and also compared with six chosen SRGMs using failure data from 18 software systems and the model is found to be adequate in terms of goodness of fit statistic and predictive validity. It is also less sensitive to fluctuations in data.
AN ANALYSIS OF ACCOUNT ON MARRIAGE IN ISIXHOSA

This study deals with the analysis of accounts of marriages in isiXhosa. Gergen (1994) proposes the following aspects that such narratives should cover: structure of narrative account; narrative form; self-narrative: process; pragmatics of self-narrative, interknitting of narratives, and emotion. Each of the listed aspects has its own variants. The variants will be dealt with in Chapter four, where Gergen’s theory is being summarised. The proposals Gergen (1994) made are central and significant in this research, since the narratives that have been given by five Xhosa-speaking married persons will be analysed according to his theory on self-narratives. This research seeks to verify the validity of Gergen’s (1994) theory of narratives in the Xhosa context. The research has been conducted by involving five Xhosaspeaking married persons. Each person had to give his or her account of marriage in line with the topic of this research, namely, the analysis of accounts of marriage in Xhosa. After narratives had been collected, an analysis of each narrative has been done in Chapter five of this research. The analysis reveals how each narrative reflects the following: the structure of the narrative account, narrative form, selfnarrative: process, pragmatics of self-narrative, practices of self-narratives, and emotions, as suggested by Gergen (1994). Analyses vary from one narrative to another because an account of married life varies from one person to another. Bakhtin (1981) suggest that the words that narrators use are inter-individual. The understanding of social morals, values, norms, justice, and the history of the community by the narrators enables them to be intelligible in their narratives. It is therefore crucial that this study be pursued in the Xhosa language because narratives are socially embedded. The narratives in this study could assist people to think correctly about the marriages in the Xhosa context. The issue of marriage affects all people, despite language diversity, and it is therefore proper that this study be conducted in all languages. If this is not done, others may think that the findings from this research is applicable to the Xhosa-speaking community only. The analytic part of this research would help communication practitioners and language practitioners to analyse narratives in their languages in the same manner
Forensic Enhancement of Digital Audio Recordings

A protocol is described to improve the voice intelligibility of investigative and other forensic audio recordings collected via digital recording systems, whether audio only or audio/video units. Sections are included on the differences between analog and digital recordings used in the forensic field, appropriate laboratory space, applicable equipment and software, enhancement examination procedures, enhancement examples, evidence handling, and expert testimony.
Forecasting daily foreign exchange rates using genetically optimized neural networks

Forecasting currency exchange rates is an important financial problem that has received much attention especially because of its intrinsic difficulty and practical applications. The statistical distribution of foreign exchange rates and their linear unpredictability are recurrent themes in the literature of international finance. Failure of various structural econometric models and models based on linear time series techniques to deliver superior forecasts to the simplest of all models, the simple random walk model, have prompted researchers to use various non-linear techniques. A number of non-linear time series models have been proposed in the recent past for obtaining accurate prediction results, in an attempt to ameliorate the performance of simple random walk models. In this paper, we use a hybrid artificial intelligence method, based on neural network and genetic algorithm for modelling daily foreign exchange rates. A detailed comparison of the proposed method with non-linear statistical models is also performed. The results indicate superior performance of the proposed method as compared to the traditional non-linear time series techniques and also fixed-geometry neural network models. Copyright © 2002 by John Wiley & Sons, Ltd.
In-Depth Understanding: A Computer Model of Integrated Processing for Narrative Comprehension

From the Publisher: This book describes a theory of memory representation, organization, and processing for understanding complex narrative texts. The theory is implemented as a computer program called BORIS which reads and answers questions about divorce, legal disputes, personal favors, and the like. The system is unique in attempting to understand stories involving emotions and in being able to deduce adages and morals, in addition to answering fact and event based questions about the narratives it has read. BORIS also manages the interaction of many different knowledge sources such as goals, plans, scripts, physical objects, settings, interpersonal relationships, social roles, emotional reactions, and empathetic responses. The book makes several original technical contributions as well. In particular, it develops a class of knowledge constructs called Thematic Abstraction Units (TAUs) which share similarities with other representational systems such as Schank's Thematic Organization Packets and Lehnert's Plot Units. TAUs allow BORIS to represent situations which are more abstract than those captured by scripts, plans, and goals. They contain processing knowledge useful in dealing with the kinds of planning and expectation failures that characters often experience in narratives; and, they often serve as episodic memory structures, organizing events which involve similar kinds of planning failures and divergent domains. An appendix contains a detailed description of a demon-based parser, a kernel of the BORIS system, as well as the actual LISP code of a microversion of this parser and a number of exercises for expanding it into a full-fledged story-understander. Michael G. Dyer is anAssistant Professor in the Department of Computer Science at UCLA. His book is included in The MIT Press Artificial Intelligence Series.
Learning and Matching Binary Local Feature Descriptors

Keywords: computer vision ; machine learning ; binary local feature descriptors ; binary approximate nearest neighbor search These Ecole polytechnique federale de Lausanne EPFL, n° 6226 (2014)Programme doctoral Informatique, Communications et InformationFaculte informatique et communicationsInstitut des systemes informatiques et multimediasLaboratoire de vision par ordinateurJury: Prof. M. C. Gastpar (president) ; Prof. P. Fua, Dr V. Lepetit (directeurs) ; Prof. M. Bronstein, Prof. P. Vandergheynst, Prof. A. Vedaldi (rapporteurs) Public defense: 2014-8-25 Reference doi:10.5075/epfl-thesis-6226Print copy in library catalog Record created on 2014-08-18, modified on 2017-05-12
Malachite Green Detection Using Carbon Paste Electrode Based on Voltammetry

The present work reports an easy and low-cost carbon paste electrode (CPE) for detecting malachite green (MG). MG has been extensively applied to control external fungi as well as parasites on fish-egg. However, MG is found to be terribly toxic and hazardous. Hence, in the interest of food security and citizens health, the determination of MG in aquaculture is very much necessary. The electrochemical behavior of the reported CPE electrode has been investigated by recording the corresponding cyclic voltammetry (CV) and differential pulse voltammetry (DPV) responses. These responses show that the presence of MG can be successfully determined using this electrode. The electrode can be utilized for detecting various concentrations of MG. The two different linear ranges of the response of this sensor are found to be 10–75 µM and 75–300 µM and repeatability and reproducibility are obtained with a relative standard deviation (RSD) of 2.28% and 2.38%, respectively. This electrochemical method can thus be chosen for the rapid detection of MG.
Numerical Simulation of Flow and Heat Transfer in a Dimpled Channel with a Fish-Scale Pit

A new dimple configuration (fish-scale pit) is proposed here. Numerical simulations of steady compressible flow were performed to determine the turbulent flow behavior and heat transfer capability in a dimpled channel with the fish-scale pit or spherical pit. The results show that the wall heat transfer coefficient of the surface with the fish-scale pit is higher than that with the spherical pit, and the surface with the fish-scale pit is better in terms of reducing friction drag. This indicates the prospective applications of surfaces with the fish-scale pits, which can enhance wall heat-transfer and reduce skin-friction drag.
The impact of unlisted and no-landline respondents on non-coverage bias. The Italian case

To survey the general population, survey agencies often use sampling frames of landline numbers. However, these frames may exclude a relevant share of the target population. In 2012, 50% of Italian adults are excluded from the sampling frame, as they are unlisted (UN) or do not own a landline telephone (NT). Using a unique national survey with full-coverage of the Italian population, our study describes the changes in the composition of landline telephone coverage over time and investigates differences in demographic and socio-economic characteristics between (i) respondents included in and excluded from the sampling frame and (ii) NT and UN respondents. It also explores whether these differences lead to under-coverage bias and evaluates the impact of current post-adjustment strategies on the total under-coverage bias as well as on its two components separately.