Abstract: The introduction of haptic elements in a graphic user interfaces are becoming more widespread. Since haptics are being introduced rapidly into computational tools, investigating how these models affect Human-Computer Interaction would help define how to integrate and model new modes of interaction. The interest of this paper is to discuss and investigate the issues surrounding Haptic and Graphic User Interface designs (GUI) as separate systems, as well as understand how these work in tandem. The development of these systems is explored from a psychological perspective, based on how usability is addressed through learning and affordances, defined by J.J. Gibson. Haptic design can be a powerful tool, aiding in intuitive learning. The problems discussed within the text is how can haptic interfaces be integrated within a GUI without the sense of frivolity. Juxtaposing haptics and Graphic user interfaces has issues of motivation; GUI tends to have a performatory process, while Haptic Interfaces use affordances to learn tool use. In a deeper view, it is noted that two modes of perception, foveal and ambient, dictate perception. These two modes were once thought to work in tandem, however it has been discovered that these processes work independently from each other. Foveal modes interpret orientation is space which provide for posture, locomotion, and motor skills with variations of the sensory information, which instructs perceptions of object-task performance. It is contended, here, that object-task performance is a key element in the use of Haptic Interfaces because exploratory learning uses affordances in order to use an object, without meditating an experience cognitively. It is a direct experience that, through iteration, can lead to skill-sets. It is also indicated that object-task performance will not work as efficiently without the use of exploratory or kinesthetic learning practices. Therefore, object-task performance is not as congruently explored in GUI than it is practiced in Haptic interfaces.
Abstract: We present a label-free biosensor based on
electrochemical impedance spectroscopy for the detection of proinflammatory
cytokine Tumor Necrosis Factor (TNF-α). Secretion of
TNF-α has been correlated to the onset of various diseases including
rheumatoid arthritis, Crohn-s disease etc. Gold electrodes were
patterned on a silicon substrate and self assembled monolayer of
dithiobis-succinimidyl propionate was used to develop the biosensor
which achieved a detection limit of ~57fM. A linear relationship was
also observed between increasing TNF-α concentrations and chargetransfer
resistance within a dynamic range of 1pg/ml – 1ng/ml.
Abstract: This paper describes a new approach of classification
using genetic programming. The proposed technique consists of
genetically coevolving a population of non-linear transformations on
the input data to be classified, and map them to a new space with a
reduced dimension, in order to get a maximum inter-classes
discrimination. The classification of new samples is then performed
on the transformed data, and so become much easier. Contrary to the
existing GP-classification techniques, the proposed one use a
dynamic repartition of the transformed data in separated intervals, the
efficacy of a given intervals repartition is handled by the fitness
criterion, with a maximum classes discrimination. Experiments were
first performed using the Fisher-s Iris dataset, and then, the KDD-99
Cup dataset was used to study the intrusion detection and
classification problem. Obtained results demonstrate that the
proposed genetic approach outperform the existing GP-classification
methods [1],[2] and [3], and give a very accepted results compared to
other existing techniques proposed in [4],[5],[6],[7] and [8].
Abstract: This study analyzes the effect of discretization on
classification of datasets including continuous valued features. Six
datasets from UCI which containing continuous valued features are
discretized with entropy-based discretization method. The
performance improvement between the dataset with original features
and the dataset with discretized features is compared with k-nearest
neighbors, Naive Bayes, C4.5 and CN2 data mining classification
algorithms. As the result the classification accuracies of the six
datasets are improved averagely by 1.71% to 12.31%.
Abstract: On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.
Abstract: High performance Resistive Random Access Memory
(RRAM) based on HfOx has been prepared and its temperature
instability has been investigated in this work. With increasing
temperature, it is found that: leakage current at high resistance state
increases, which can be explained by the higher density of traps
inside dielectrics (related to trap-assistant tunneling), leading to a
smaller On/Off ratio; set and reset voltages decrease, which may be
attributed to the higher oxygen ion mobility, in addition to the
reduced potential barrier to create / recover oxygen ions (or oxygen
vacancies); temperature impact on the RRAM retention degradation
is more serious than electrical bias.
Abstract: An on-line condition monitoring method for transmission line is proposed using electrical circuit theory and IT technology in this paper. It is reasonable that the circuit parameters such as resistance (R), inductance (L), conductance (g) and capacitance (C) of a transmission line expose the electrical conditions and physical state of the line. Those parameters can be calculated from the linear equation composed of voltages and currents measured by synchro-phasor measurement technique at both end of the line. A set of linear voltage drop equations containing four terminal constants (A, B ,C ,D ) are mathematical models of the transmission line circuits. At least two sets of those linear equations are established from different operation condition of the line, they may mathematically yield those circuit parameters of the line. The conditions of line connectivity including state of connecting parts or contacting parts of the switching device may be monitored by resistance variations during operation. The insulation conditions of the line can be monitored by conductance (g) and capacitance(C) measurements. Together with other condition monitoring devices such as partial discharge, sensors and visual sensing device etc.,they may give useful information to monitor out any incipient symptoms of faults. The prototype of hardware system has been developed and tested through laboratory level simulated transmission lines. The test has shown enough evident to put the proposed method to practical uses.
Abstract: Most real world systems express themselves formally
as a set of nonlinear algebraic equations. As applications grow, the
size and complexity of these equations also increase. In this work, we
highlight the key concepts in using the homotopy analysis method
as a methodology used to construct efficient iteration formulas for
nonlinear equations solving. The proposed method is experimentally
characterized according to a set of determined parameters which
affect the systems. The experimental results show the potential and
limitations of the new method and imply directions for future work.
Abstract: Nowadays predicting political risk level of country
has become a critical issue for investors who intend to achieve
accurate information concerning stability of the business
environments. Since, most of the times investors are layman and
nonprofessional IT personnel; this paper aims to propose a
framework named GECR in order to help nonexpert persons to
discover political risk stability across time based on the political
news and events.
To achieve this goal, the Bayesian Networks approach was
utilized for 186 political news of Pakistan as sample dataset.
Bayesian Networks as an artificial intelligence approach has been
employed in presented framework, since this is a powerful technique
that can be applied to model uncertain domains. The results showed
that our framework along with Bayesian Networks as decision
support tool, predicted the political risk level with a high degree of
accuracy.
Abstract: Electronic commerce is growing rapidly with on-line
sales already heading for hundreds of billion dollars per year. Due to
the huge amount of money transferred everyday, an increased
security level is required. In this work we present the architecture of
an intelligent speaker verification system, which is able to accurately
verify the registered users of an e-commerce service using only their
voices as an input. According to the proposed architecture, a
transaction-based e-commerce application should be complemented
by a biometric server where customer-s unique set of speech models
(voiceprint) is stored. The verification procedure requests from the
user to pronounce a personalized sequence of digits and after
capturing speech and extracting voice features at the client side are
sent back to the biometric server. The biometric server uses pattern
recognition to decide whether the received features match the stored
voiceprint of the customer who claims to be, and accordingly grants
verification. The proposed architecture can provide e-commerce
applications with a higher degree of certainty regarding the identity
of a customer, and prevent impostors to execute fraudulent
transactions.
Abstract: Intuitionistic fuzzy sets as proposed by Atanassov,
have gained much attention from past and latter researchers for
applications in various fields. Similarity measures between
intuitionistic fuzzy sets were developed afterwards. However, it does
not cater the conflicting behavior of each element evaluated. We
therefore made some modification to the similarity measure of IFS
by considering conflicting concept to the model. In this paper, we
concentrate on Zhang and Fu-s similarity measures for IFSs and
some examples are given to validate these similarity measures. A
simple modification to Zhang and Fu-s similarity measures of IFSs
was proposed to find the best result according to the use of degree of
indeterminacy. Finally, we mark up with the application to real
decision making problems.
Abstract: Analytical investigation of the sedimentation
processes in the river engineering and hydraulic structures is of vital
importance as this can affect water supply for the cultivating lands in
the command area. The reason being that gradual sediment formation
behind the reservoir can reduce the nominal capacity of these dams.
The aim of the present paper is to analytically investigate
sedimentation process along the river course and behind the storage
reservoirs in general and the Eastern Intake of the Dez Diversion weir
in particular using the SHARC software. Results of the model
indicated the water level at 115.97m whereas the real time
measurement from the river cross section was 115.98 m which
suggests a significantly close relation between them. The average
transported sediment load in the river was measured at 0.25mm ,
from which it can be concluded that nearly 100% of the suspended
loads in river are moving which suggests no sediment settling but
indicates that almost all sediment loads enters into the intake. It was
further showed the average sediment diameter entering the intake to
be 0.293 mm which in turn suggests that about 85% of suspended
sediments in the river entre the intake. Comparison of the results
from the SHARC model with those obtained form the SSIIM
software suggests quite similar outputs but distinguishing the
SHARC model as more appropriate for the analysis of simpler
problems than other model.
Abstract: The Tropical Data Hub (TDH) is a virtual research environment that provides researchers with an e-research infrastructure to congregate significant tropical data sets for data reuse, integration, searching, and correlation. However, researchers often require data and metadata synthesis across disciplines for crossdomain analyses and knowledge discovery. A triplestore offers a semantic layer to achieve a more intelligent method of search to support the synthesis requirements by automating latent linkages in the data and metadata. Presently, the benchmarks to aid the decision of which triplestore is best suited for use in an application environment like the TDH are limited to performance. This paper describes a new evaluation tool developed to analyze both features and performance. The tool comprises a weighted decision matrix to evaluate the interoperability, functionality, performance, and support availability of a range of integrated and native triplestores to rank them according to requirements of the TDH.
Abstract: Operating rooms are important assets for hospitals as
they generate the largest revenue and, at the same time, produce the
largest cost for hospitals. The model presented in this paper helps
make capacity planning decisions on the combination of open
operating rooms (ORs) and estimated overtime to satisfy the
allocated OR time to each specialty. The model combines both
decisions on determining the amount of OR time to open and to
allocate to different surgical specialties. The decisions made are
based on OR costs, overutilization and underutilization costs, and
contribution margins from allocating OR time. The results show the
importance of having a good estimate of specialty usage of OR time
to determine the amount of needed capacity and highlighted the
tradeoff that the OR manager faces between opening more ORs
versus extending the working time of the ORs already in use.
Abstract: In this study, single nozzle method used for
electrospinning technique which composite polymer solution with
cellulose nanowiskers (CNW) was treated by ultrasonic sonificator
have been compared with coaxial (double) nozzle method, in terms of
mechanical, thermal and morphological properties of composite
nanofiber. The effect of water content in composite polymer solution
on properties of nanofiber has also been examined. It has been seen
that single nozzle method which polymer solution does not contain
water has better results than that of coaxial method, in terms of
mechanical, thermal and morphological properties of nanofiber.
However, it is necessary to make an optimization study on setting
condition of ultrasonic treatment to get better dispersion of CNW in
composite nanofiber and to get better mechanical and thermal
properties
Abstract: This study attempts to investigate the relationship
between internal CSR practices and organizational commitment
based on the social exchange theory (SET). Specifically, we examine
the impact of five dimensions of internal CSR practices on
organizational commitment: health and safety, human rights, training
and education, work life balance and workplace diversity. The
proposed model was tested on a sample of 336 frontline employees
within the banking sector in Jordan. Results showed that all internal
CSR dimensions are significantly and positively related to affective
and normative commitment. In addition, the findings of this study
indicate that all internal CSR dimensions did not have a significant
relationship with continuance commitment. Limitations of the study,
directions for future research, and implications of the findings are
discussed.
Abstract: Cost contribution arrangements (CCAs) and Cost
sharing agreements (CCAs) belong to the tools of modern finance
management. Costs spend by associated enterprises on developing
producing or obtaining assets, services or rights (in general -
benefits) are used for tax optimizing too. The main purpose of joint
research and development, producing or obtaining benefits is to
lower these costs as much as possible or to maximize the benefits. In
this article is mentioned the problematic of transfer pricing and arm's
length principle with connection of CCAs, CSAs. Next, there is
mentioned how to settle participation shares of the total cost and
benefits contributions with respect to the OECD Transfer pricing for
MNEs Guidelines and with respect to other significant regulations.
Abstract: Congestion control is one of the fundamental issues in computer networks. Without proper congestion control mechanisms there is the possibility of inefficient utilization of resources, ultimately leading to network collapse. Hence congestion control is an effort to adapt the performance of a network to changes in the traffic load without adversely affecting users perceived utilities. AIMD (Additive Increase Multiplicative Decrease) is the best algorithm among the set of liner algorithms because it reflects good efficiency as well as good fairness. Our control model is based on the assumption of the original AIMD algorithm; we show that both efficiency and fairness of AIMD can be improved. We call our approach is New AIMD. We present experimental results with TCP that match the expectation of our theoretical analysis.
Abstract: The product development process (PDP) in the
Technology group plays a very important role in the launch of any
product. While a manufacturing process encourages the use of certain
measures to reduce health, safety and environmental (HSE) risks on
the shop floor, the PDP concentrates on the use of Geometric
Dimensioning and Tolerancing (GD&T) to develop a flawless design.
Furthermore, PDP distributes and coordinates activities between
different departments such as marketing, purchasing, and
manufacturing. However, it is seldom realized that PDP makes a
significant contribution to developing a product that reduces HSE
risks by encouraging the Technology group to use effective GD&T.
The GD&T is a precise communication tool that uses a set of
symbols, rules, and definitions to mathematically define parts to be
manufactured. It is a quality assurance method widely used in the oil
and gas sector. Traditionally it is used to ensure the
interchangeability of a part without affecting its form, fit, and
function. Parts that do not meet these requirements are rejected
during quality audits.
This paper discusses how the Technology group integrates this
quality assurance tool into the PDP and how the tool plays a major
role in helping the HSE department in its goal towards eliminating
HSE incidents. The PDP involves a thorough risk assessment and
establishes a method to address those risks during the design stage.
An illustration shows how GD&T helped reduce safety risks by
ergonomically improving assembling operations. A brief discussion
explains how tolerances provided on a part help prevent finger injury.
This tool has equipped Technology to produce fixtures, which are
used daily in operations as well as manufacturing. By applying
GD&T to create good fits, HSE risks are mitigated for operating
personnel. Both customers and service providers benefit from
reduced safety risks.
Abstract: In this study, we present an advanced detection
technique for mass type breast cancer based on texture information
of organs. The proposed method detects the cancer areas in three
stages. In the first stage, the midpoints of mass area are determined
based on AHE (Adaptive Histogram Equalization). In the second
stage, we set the threshold coefficient of homogeneity by using
MLE (Maximum Likelihood Estimation) to compute the uniformity
of texture. Finally, mass type cancer tissues are extracted from the
original image. As a result, it was observed that the proposed
method shows an improved detection performance on dense breast
tissues of Korean women compared with the existing methods. It is
expected that the proposed method may provide additional
diagnostic information for detection of mass-type breast cancer.