Abstract: One of the main aims of current social robotic research
is to improve the robots’ abilities to interact with humans. In order
to achieve an interaction similar to that among humans, robots
should be able to communicate in an intuitive and natural way
and appropriately interpret human affects during social interactions.
Similarly to how humans are able to recognize emotions in other
humans, machines are capable of extracting information from the
various ways humans convey emotions—including facial expression,
speech, gesture or text—and using this information for improved
human computer interaction. This can be described as Affective
Computing, an interdisciplinary field that expands into otherwise
unrelated fields like psychology and cognitive science and involves
the research and development of systems that can recognize and
interpret human affects. To leverage these emotional capabilities
by embedding them in humanoid robots is the foundation of
the concept Affective Robots, which has the objective of making
robots capable of sensing the user’s current mood and personality
traits and adapt their behavior in the most appropriate manner
based on that. In this paper, the emotion recognition capabilities
of the humanoid robot Pepper are experimentally explored, based
on the facial expressions for the so-called basic emotions, as
well as how it performs in contrast to other state-of-the-art
approaches with both expression databases compiled in academic
environments and real subjects showing posed expressions as well
as spontaneous emotional reactions. The experiments’ results show
that the detection accuracy amongst the evaluated approaches differs
substantially. The introduced experiments offer a general structure
and approach for conducting such experimental evaluations. The
paper further suggests that the most meaningful results are obtained
by conducting experiments with real subjects expressing the emotions
as spontaneous reactions.
Abstract: Image retrieval is the most interesting technique which is being used today in our digital world. CBIR, commonly expanded as Content Based Image Retrieval is an image processing technique which identifies the relevant images and retrieves them based on the patterns that are extracted from the digital images. In this paper, two research works have been presented using CBIR. The first work provides an automated and interactive approach to the analysis of CBIR techniques. CBIR works on the principle of supervised machine learning which involves feature selection followed by training and testing phase applied on a classifier in order to perform prediction. By using feature extraction, the image transforms such as Contourlet, Ridgelet and Shearlet could be utilized to retrieve the texture features from the images. The features extracted are used to train and build a classifier using the classification algorithms such as Naïve Bayes, K-Nearest Neighbour and Multi-class Support Vector Machine. Further the testing phase involves prediction which predicts the new input image using the trained classifier and label them from one of the four classes namely 1- Normal brain, 2- Benign tumour, 3- Malignant tumour and 4- Severe tumour. The second research work includes developing a tool which is used for tumour stage identification using the best feature extraction and classifier identified from the first work. Finally, the tool will be used to predict tumour stage and provide suggestions based on the stage of tumour identified by the system. This paper presents these two approaches which is a contribution to the medical field for giving better retrieval performance and for tumour stages identification.
Abstract: This work approaches the automatic planning of paths
for Unmanned Aerial Vehicles (UAVs) through the application of the
Rapidly Exploring Random Tree Star-Smart (RRT*-Smart) algorithm.
RRT*-Smart is a sampling process of positions of a navigation
environment through a tree-type graph. The algorithm consists of
randomly expanding a tree from an initial position (root node) until
one of its branches reaches the final position of the path to be
planned. The algorithm ensures the planning of the shortest path,
considering the number of iterations tending to infinity. When a
new node is inserted into the tree, each neighbor node of the
new node is connected to it, if and only if the extension of the
path between the root node and that neighbor node, with this new
connection, is less than the current extension of the path between
those two nodes. RRT*-smart uses an intelligent sampling strategy
to plan less extensive routes by spending a smaller number of
iterations. This strategy is based on the creation of samples/nodes
near to the convex vertices of the navigation environment obstacles.
The planned paths are smoothed through the application of the
method called quintic pythagorean hodograph curves. The smoothing
process converts a route into a dynamically-viable one based on the
kinematic constraints of the vehicle. This smoothing method models
the hodograph components of a curve with polynomials that obey
the Pythagorean Theorem. Its advantage is that the obtained structure
allows computation of the curve length in an exact way, without the
need for quadratural techniques for the resolution of integrals.
Abstract: Crude oil market is an immensely complex and dynamic environment and thus the task of predicting changes in such an environment becomes challenging with regards to its accuracy. A number of approaches have been adopted to take on that challenge and machine learning has been at the core in many of them. There are plenty of examples of algorithms based on machine learning yielding satisfactory results for such type of prediction. In this paper, we have tried to predict crude oil prices using Long Short-Term Memory (LSTM) based recurrent neural networks. We have tried to experiment with different types of models using different epochs, lookbacks and other tuning methods. The results obtained are promising and presented a reasonably accurate prediction for the price of crude oil in near future.
Abstract: It is of great importance for an architectural student to know the parameters through which he/she can conduct his/her design and makes his/her design effective in architectural education. Therefore; an empirical application study was carried out through the designing activity using the emulation model to support the design and design approaches of architectural students. During the investigation period, studies were done on the basic design elements and principles of the fall semester, and the emulation model, one of the designing methods that constitute the subject of the study, was fictionalized as three phased “recognition-interpretation-application”. As a result of the study, it was observed that when students were given a key method during the design process, their awareness increased and their aspects improved as well.
Abstract: In this paper, we study the performance of the strong
stability method of the univariate classical risk model. We interest to
the stability bounds established using two approaches. The first based
on the strong stability method developed for a general Markov chains.
The second approach based on the regenerative processes theory . By
adopting an algorithmic procedure, we study the performance of the
stability method in the case of exponential distribution claim amounts.
After presenting numerically and graphically the stability bounds, an
interpretation and comparison of the results have been done.
Abstract: The earliest theories of sloshing waves and solitary waves based on potential theory idealisations and irrotational flow have been extended to be applicable to more realistic domains. To this end, the computational fluid dynamics (CFD) methods are widely used. Three-dimensional CFD methods such as Navier-Stokes solvers with volume of fluid treatment of the free surface and Navier-Stokes solvers with mappings of the free surface inherently impose high computational expense; therefore, considerable effort has gone into developing depth-averaged approaches. Examples of such approaches include Green–Naghdi (GN) equations. In Cartesian system, GN velocity profile depends on horizontal directions, x-direction and y-direction. The effect of vertical direction (z-direction) is also taken into consideration by applying weighting function in approximation. GN theory considers the effect of vertical acceleration and the consequent non-hydrostatic pressure. Moreover, in GN theory, the flow is rotational. The present study illustrates the application of GN equations to propagation of sloshing waves and solitary waves. For this purpose, GN equations solver is verified for the benchmark tests of Gaussian hump sloshing and solitary wave propagation in shallow basins. Analysis of the free surface sloshing of even harmonic components of an initial Gaussian hump demonstrates that the GN model gives predictions in satisfactory agreement with the linear analytical solutions. Discrepancies between the GN predictions and the linear analytical solutions arise from the effect of wave nonlinearities arising from the wave amplitude itself and wave-wave interactions. Numerically predicted solitary wave propagation indicates that the GN model produces simulations in good agreement with the analytical solution of the linearised wave theory. Comparison between the GN model numerical prediction and the result from perturbation analysis confirms that nonlinear interaction between solitary wave and a solid wall is satisfactorilly modelled. Moreover, solitary wave propagation at an angle to the x-axis and the interaction of solitary waves with each other are conducted to validate the developed model.
Abstract: This paper presents a Monte Carlo (MC) method-based dose distributions on lung tumor for 6 MV photon beam to improve the dosimetric accuracy for cancer treatment. The polystyrene which is tissue equivalent material to the lung tumor density is used in this research. In the empirical calculations, TRS-398 formalism of IAEA has been used, and the setup was made according to the ICRU recommendations. The research outcomes were compared with the state-of-the-art experimental results. From the experimental results, it is observed that the proposed based approach provides more accurate results and improves the accuracy than the existing approaches. The average %variation between measured and TPS simulated values was obtained 1.337±0.531, which shows a substantial improvement comparing with the state-of-the-art technology.
Abstract: Crop yield prediction is a paramount issue in
agriculture. The main idea of this paper is to find out efficient
way to predict the yield of corn based meteorological records.
The prediction models used in this paper can be classified into
model-driven approaches and data-driven approaches, according to
the different modeling methodologies. The model-driven approaches are based on crop mechanistic
modeling. They describe crop growth in interaction with their
environment as dynamical systems. But the calibration process of
the dynamic system comes up with much difficulty, because it
turns out to be a multidimensional non-convex optimization problem.
An original contribution of this paper is to propose a statistical
methodology, Multi-Scenarios Parameters Estimation (MSPE), for the
parametrization of potentially complex mechanistic models from a
new type of datasets (climatic data, final yield in many situations).
It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction
is free of the complex biophysical process. But it has some strict
requirements about the dataset.
A second contribution of the paper is the comparison of these
model-driven methods with classical data-driven methods. For this
purpose, we consider two classes of regression methods, methods
derived from linear regression (Ridge and Lasso Regression, Principal
Components Regression or Partial Least Squares Regression) and
machine learning methods (Random Forest, k-Nearest Neighbor,
Artificial Neural Network and SVM regression).
The dataset consists of 720 records of corn yield at county scale
provided by the United States Department of Agriculture (USDA) and
the associated climatic data. A 5-folds cross-validation process and
two accuracy metrics: root mean square error of prediction(RMSEP),
mean absolute error of prediction(MAEP) were used to evaluate the
crop prediction capacity.
The results show that among the data-driven approaches, Random
Forest is the most robust and generally achieves the best prediction
error (MAEP 4.27%). It also outperforms our model-driven approach
(MAEP 6.11%). However, the method to calibrate the mechanistic
model from dataset easy to access offers several side-perspectives.
The mechanistic model can potentially help to underline the stresses
suffered by the crop or to identify the biological parameters of interest
for breeding purposes. For this reason, an interesting perspective is
to combine these two types of approaches.
Abstract: The purpose of the present research is to equate two
test forms as part of a study to evaluate the educational effectiveness
of the ARTé: Mecenas art history learning game. The researcher
applied Item Response Theory (IRT) procedures to calculate item,
test, and mean-sigma equating parameters. With the sample size
n=134, test parameters indicated “good” model fit but low Test
Information Functions and more acute than expected equating
parameters. Therefore, the researcher applied equipercentile equating
and linear equating to raw scores and compared the equated form
parameters and effect sizes from each method. Item scaling in IRT
enables the researcher to select a subset of well-discriminating items.
The mean-sigma step produces a mean-slope adjustment from the
anchor items, which was used to scale the score on the new form
(Form R) to the reference form (Form Q) scale. In equipercentile
equating, scores are adjusted to align the proportion of scores in each
quintile segment. Linear equating produces a mean-slope adjustment,
which was applied to all core items on the new form. The study
followed a quasi-experimental design with purposeful sampling of
students enrolled in a college level art history course (n=134) and
counterbalancing design to distribute both forms on the pre- and posttests.
The Experimental Group (n=82) was asked to play ARTé:
Mecenas online and complete Level 4 of the game within a two-week
period; 37 participants completed Level 4. Over the same period, the
Control Group (n=52) did not play the game. The researcher
examined between group differences from post-test scores on test
Form Q and Form R by full-factorial Two-Way ANOVA. The raw
score analysis indicated a 1.29% direct effect of form, which was
statistically non-significant but may be practically significant. The
researcher repeated the between group differences analysis with all
three equating methods. For the IRT mean-sigma adjusted scores,
form had a direct effect of 8.39%. Mean-sigma equating with a small
sample may have resulted in inaccurate equating parameters.
Equipercentile equating aligned test means and standard deviations,
but resultant skewness and kurtosis worsened compared to raw score
parameters. Form had a 3.18% direct effect. Linear equating
produced the lowest Form effect, approaching 0%. Using linearly
equated scores, the researcher conducted an ANCOVA to examine
the effect size in terms of prior knowledge. The between group effect
size for the Control Group versus Experimental Group participants
who completed the game was 14.39% with a 4.77% effect size
attributed to pre-test score. Playing and completing the game
increased art history knowledge, and individuals with low prior
knowledge tended to gain more from pre- to post test. Ultimately,
researchers should approach test equating based on their theoretical
stance on Classical Test Theory and IRT and the respective assumptions. Regardless of the approach or method, test equating
requires a representative sample of sufficient size. With small sample
sizes, the application of a range of equating approaches can expose
item and test features for review, inform interpretation, and identify
paths for improving instruments for future study.
Abstract: The world-wide population of people over 60 years
of age is growing rapidly. The explosion is placing increasingly
onerous demands on individual families, multiple industries and
entire countries. Current, human-intensive approaches to eldercare
are not sustainable, but IoT and AI technologies can help. The
Knowledge Reactor (KR) is a contextual, data fusion engine built to
address this and other similar problems. It fuses and centralizes IoT
and System of Record/Engagement data into a reactive knowledge
graph. Cognitive applications and services are constructed with its
multiagent architecture. The KR can scale-up and scaledown, because
it exploits container-based, horizontally scalable services for graph
store (JanusGraph) and pub-sub (Kafka) technologies. While the KR
can be applied to many domains that require IoT and AI technologies,
this paper describes how the KR specifically supports the challenging
domain of cognitive eldercare. Rule- and machine learning-based
analytics infer activities of daily living from IoT sensor readings. KR
scalability, adaptability, flexibility and usability are demonstrated.
Abstract: Butterfly valves are widely used industrial piping components as on-off and flow controlling devices. The main challenge in the design process of this type of valves is the correct dimensioning to ensure proper mechanical performance as well as to minimise flow losses that affect the efficiency of the system. Butterfly valves are typically dimensioned in a closed position based on mechanical approaches considering uniform hydrostatic pressure, whereas the flow losses are analysed by means of CFD simulations. The main limitation of these approaches is that they do not consider either the influence of the dynamics of the manoeuvring stage or coupled phenomena. Recent works have included the influence of the flow on the mechanical behaviour for different opening angles by means of one-way FSI approach. However, these works consider steady-state flow for the selected angles, not capturing the effect of the transient flow evolution during the manoeuvring stage. Two-way FSI modelling approach could allow overcoming such limitations providing more accurate results. Nevertheless, the use of this technique is limited due to the increase in the computational cost. In the present work, the applicability of FSI one-way and two-way approaches is evaluated for the analysis of butterfly valves, showing that not considering fluid-structure coupling involves not capturing the most critical situation for the valve disc.
Abstract: Emotion dysregulation has been linked to psychopathology in general and, in particular, to substance abuse and other addiction-related disorders, such as eating disorders, impulsive disorder, and gambling. It has been proposed that a lessening of the difficulties in emotion regulation can have a significant positive impact on the treatment of these disorders. The present study explores the association between the progress in the Change & Grow® therapeutic model (5 stages of treatment), and the decrease in the difficulties related to emotion regulation. The Change & Grow® model has five stages of treatment according to the model’s five principles (Truth, Acceptance, Gratitude, Love and Responsibility) and incorporates different therapeutic approaches such as positive psychology, cognitive and behavioral therapy and third generation therapies. The main objective is to understand the impact of the presented therapeutic model on difficulties in emotion regulation in patients with addiction-related disorders. The exploratory study has a cross-sectional design. Participants were 44 (15 women and 29 men) Portuguese patients in the residential Villa Ramadas International Treatment Centre. The instrument used was the Portuguese version of the Difficulties in Emotion Regulation Scale (DERS), which measures six dimensions of emotion regulation (Strategies, Non-acceptance, Awareness, Impulse, Goals, and Clarity). The mean rank scores for both the DERS total score and the Impulse subscale showed statistically significant differences according to Stage of Treatment/Principles. Furthermore, Stage of Treatment/Principles held a negative correlation with the scores of the Non-acceptance and Impulse subscales, as well as the DERS total score. The results indicate that the Change & Grow® model seems to have an impact in lessening the patient’s difficulties in emotion regulation. The Impulse dimension suffered the greater impact, which supports the well-known relevance of impulse control, or related difficulties, in addiction-related disorders.
Abstract: Adolescents’ biased perceptions about their social competence (SC), whether negatively or positively, serve to influence their socioemotional adjustment such as early feelings of social phobia (nowadays referred to as Social Anxiety Disorder-SAD). Despite the importance of biased self-perceptions in adolescents’ psychosocial adjustment, the extent to which discrepancies between self- and others’ evaluations of one’s SC are linked to social phobic symptoms remains unclear in the literature. This study examined the perceptual discrepancy profiles between self- and peers’ as well as between self- and teachers’ evaluations of adolescents’ SC and the interrelations of these profiles with self-reported social phobic symptoms. The participants were 390 3rd graders (15 years old) of Finnish lower secondary school (50.8% boys, 49.2% girls). In contrast with variable-centered approaches that have mainly been used by previous studies when focusing on this subject, this study used latent profile analysis (LPA), a person-centered approach which can provide information regarding risk profiles by capturing the heterogeneity within a population and classifying individuals into groups. LPA revealed the following five classes of discrepancy profiles: i) extremely negatively biased perceptions of SC, ii) negatively biased perceptions of SC, iii) quite realistic perceptions of SC, iv) positively biased perceptions of SC, and v) extremely positively biased perceptions of SC. Adolescents with extremely negatively biased perceptions and negatively biased perceptions of their own SC reported the highest number of social phobic symptoms. Adolescents with quite realistic, positively biased and extremely positively biased perceptions reported the lowest number of socio-phobic symptoms. The results point out the negatively and the extremely negatively biased perceptions as possible contributors to social phobic symptoms. Moreover, the association of quite realistic perceptions with low number of social phobic symptoms indicates its potential protective power against social phobia. Finally, positively and extremely positively biased perceptions of SC are negatively associated with social phobic symptoms in this study. However, the profile of extremely positively biased perceptions might be linked as well with the existence of externalizing problems such as antisocial behavior (e.g. disruptive impulsivity). The current findings highlight the importance of considering discrepancies between self- and others’ perceptions of one’s SC in clinical and research efforts. Interventions designed to prevent or moderate social phobic symptoms need to take into account individual needs rather than aiming for uniform treatment. Implications and future directions are discussed.
Abstract: In the past, the quay crane operations have been well studied. There were a certain number of scheduling algorithms for quay crane operations, but without considering some nuisance factors that might disrupt the quay crane operations. For example, bad grapples make a crane unable to load or unload containers or a sudden strong breeze stops operations temporarily. Although these disruptive conditions randomly occur, they influence the efficiency of quay crane operations. The disruption is not considered in the operational procedures nor is evaluated in advance for its impacts. This study applies simulation and optimization approaches to develop structures of pre-analysis and post-analysis for the Quay Crane Scheduling Problem to deal with disruptive scenarios for quay crane operation. Numerical experiments are used for demonstrations for the validity of the developed approaches.
Abstract: Empirical deterministic models have been developed to predict roughness progression of heavy duty spray sealed pavements for a dataset representing rural arterial roads. The dataset provides a good representation of the relevant network and covers a wide range of operating and environmental conditions. A sample with a large size of historical time series data for many pavement sections has been collected and prepared for use in multilevel regression analysis. The modelling parameters include road roughness as performance parameter and traffic loading, time, initial pavement strength, reactivity level of subgrade soil, climate condition, and condition of drainage system as predictor parameters. The purpose of this paper is to report the approaches adopted for models development and validation. The study presents multilevel models that can account for the correlation among time series data of the same section and to capture the effect of unobserved variables. Study results show that the models fit the data very well. The contribution and significance of relevant influencing factors in predicting roughness progression are presented and explained. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data.
Abstract: In today’s scenario, the complexity of digital signal processing (DSP) applications and various microcontroller architectures have been increasing to such an extent that the traditional approaches to multiplier design in most processors are becoming outdated for being comparatively slow. Modern processing applications require suitable pipelined approaches, and therefore, algorithms that are friendlier with pipelined architectures. Traditional algorithms like Wallace Tree, Radix-4 Booth, Radix-8 Booth, Dadda architectures have been proven to be comparatively slow for pipelined architectures. These architectures, therefore, need to be optimized or combined with other architectures amongst them to enhance its performances and to be made suitable for pipelined hardware/architectures. Recently, Vedic algorithm mathematically has proven to be efficient by appearing to be less complex and with fewer steps for its output establishment and have assumed renewed importance. This paper describes and shows how the Vedic algorithm can be better suited for pipelined architectures and also can be combined with traditional architectures and algorithms for enhancing its ability even further. In this paper, we also established that for complex applications on DSP and other microcontroller architectures, using Vedic approach for multiplication proves to be the best available and efficient option.
Abstract: Social sustainability, as an independent perspective of sustainable development, has gained some acknowledgement, becoming an important aspect in sustainable urban planning internationally. However, limited research aiming at promoting social sustainability within urban areas exists within the South African context. This is mainly due to the different perspectives of sustainable development (e.g., Environmental, Economic, and Social) not being equally prioritized by policy makers and supported by implementation strategies, guidelines, and planning frameworks. The enhancement of social sustainability within urban areas relies on urban dweller satisfaction and the quality of urban life. Inclusive cities with high-quality public spaces are proposed within this research through implementing the third place theory. Third places are introduced as any place other than our homes (first place) and work (second place) and have become an integrated part of sustainable urban planning. As Third Places consist of every place 'in between', the approach has taken on a large role of the everyday life of city residents, and the importance of planning for such places can only be measured through identifying and highlighting the social sustainability benefits thereof. The aim of this research paper is to introduce third place planning within the urban area to ultimately enhance social sustainability. Selected background planning approaches influencing the planning of third places will briefly be touched on, as the focus will be placed on the social sustainability benefits provided through third place planning within an urban setting. The study will commence by defining and introducing the concept of third places within urban areas as well as a discussion on social sustainability, acting as one of the three perspectives of sustainable development. This will gain the researcher an improved understanding on social sustainability in order for the study to flow into an integrated discussion of the benefits Third places provide in terms of social sustainability and the impact it has on improved quality of life within urban areas. Finally, a visual case study comparison of local and international examples of third places identified will be illustrated. These international case studies will contribute towards the conclusion of this study where a local gap analysis will be formulated, based on local third place evidence and international best practices in order to formulate a strategic planning framework on improving social sustainability through third place planning within the local South African context.
Abstract: As a rapid growth of digital videos and data
communications, video summarization that provides a shorter version
of the video for fast video browsing and retrieval is necessary.
Key frame extraction is one of the mechanisms to generate video
summary. In general, the extracted key frames should both represent
the entire video content and contain minimum redundancy. However,
most of the existing approaches heuristically select key frames; hence,
the selected key frames may not be the most different frames and/or
not cover the entire content of a video. In this paper, we propose
a method of video summarization which provides the reasonable
objective functions for selecting key frames. In particular, we apply
a statistical dependency measure called quadratic mutual informaion
as our objective functions for maximizing the coverage of the
entire video content as well as minimizing the redundancy among
selected key frames. The proposed key frame extraction algorithm
finds key frames as an optimization problem. Through experiments,
we demonstrate the success of the proposed video summarization
approach that produces video summary with better coverage of
the entire video content while less redundancy among key frames
comparing to the state-of-the-art approaches.
Abstract: This work presents the various perspectives, dimensions, components and definitions given to quality in the operations management (OM) and healthcare services (HCS) literature in time, highlighting gaps and learning opportunities between the two disciplines through a thorough search into their rich and distinct body of knowledge. Greater and new insights about the general nature of quality are obtained with findings such as in OM, quality has been approached in six fairly distinct paradigms (excellence, value, conformity to specifications, attributes, satisfaction and meeting or exceeding customer expectations), whereas in HCS, two approaches are prominent (Donabedian’s structure, process and outcomes model and Lohr and Schroeder’s circumscribed definition). The two disciplines views on quality seem to have progressed much in parallel with little cross-learning from each other. This work then proposes an encompassing definition of quality as a lever and suggests further research and development avenues for a better use of the concept of quality by academics and practitioners alike toward the goals of greater organizational performance and improved management in healthcare and possibly other service domains.