43 research outputs found
D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments
Finding the optimum path for a robot for moving from start to the goal
position through obstacles is still a challenging issue. This paper presents a
novel path planning method, named D-point trigonometric, based on Q-learning
algorithm for dynamic and uncertain environments, in which all the obstacles
and the target are moving. We define a new state, action and reward functions
for the Q-learning by which the agent can find the best action in every state
to reach the goal in the most appropriate path. The D-point approach minimizes
the possible number of states. Moreover, the experiments in Unity3D confirmed
the high convergence speed, the high hit rate, as well as the low dependency on
environmental parameters of the proposed method compared with an opponent
approach
On the Efficiency of the Neuro-Fuzzy Classifier for User Knowledge Modeling Systems
User knowledge modeling systems are used as the most effective technology for
grabbing new user's attention. Moreover, the quality of service (QOS) is
increased by these intelligent services. This paper proposes two user knowledge
classifiers based on artificial neural networks used as one of the influential
parts of knowledge modeling systems. We employed multi-layer perceptron (MLP)
and adaptive neural fuzzy inference system (ANFIS) as the classifiers.
Moreover, we used real data contains the user's degree of study time,
repetition number, their performance in exam, as well as the learning
percentage, as our classifier's inputs. Compared with well-known methods like
KNN and Bayesian classifiers used in other research with the same data sets,
our experiments present better performance. Although, the number of samples in
the train set is not large enough, the performance of the neuro-fuzzy
classifier in the test set is 98.6% which is the best result in comparison with
others. However, the comparison of MLP toward the ANFIS results presents
performance reduction, although the MLP performance is more efficient than
other methods like Bayesian and KNN. As our goal is evaluating and reporting
the efficiency of a neuro-fuzzy classifier for user knowledge modeling systems,
we utilized many different evaluation metrics such as Receiver Operating
Characteristic and the Area Under its Curve, Total Accuracy, and Kappa
statistics
A Fully Time-domain Neural Model for Subband-based Speech Synthesizer
This paper introduces a deep neural network model for subband-based speech
synthesizer. The model benefits from the short bandwidth of the subband signals
to reduce the complexity of the time-domain speech generator. We employed the
multi-level wavelet analysis/synthesis to decompose/reconstruct the signal into
subbands in time domain. Inspired from the WaveNet, a convolutional neural
network (CNN) model predicts subband speech signals fully in time domain. Due
to the short bandwidth of the subbands, a simple network architecture is enough
to train the simple patterns of the subbands accurately. In the ground truth
experiments with teacher-forcing, the subband synthesizer outperforms the
fullband model significantly in terms of both subjective and objective
measures. In addition, by conditioning the model on the phoneme sequence using
a pronunciation dictionary, we have achieved the fully time-domain neural model
for subband-based text-to-speech (TTS) synthesizer, which is nearly end-to-end.
The generated speech of the subband TTS shows comparable quality as the
fullband one with a slighter network architecture for each subband.Comment: 5 pages, 3 figur
Question-type Identification for Academic Questions in Online Learning Platform
Online learning platforms provide learning materials and answers to students'
academic questions by experts, peers, or systems. This paper explores
question-type identification as a step in content understanding for an online
learning platform. The aim of the question-type identifier is to categorize
question types based on their structure and complexity, using the question
text, subject, and structural features. We have defined twelve question-type
classes, including Multiple-Choice Question (MCQ), essay, and others. We have
compiled an internal dataset of students' questions and used a combination of
weak-supervision techniques and manual annotation. We then trained a BERT-based
ensemble model on this dataset and evaluated this model on a separate
human-labeled test set. Our experiments yielded an F1-score of 0.94 for MCQ
binary classification and promising results for 12-class multilabel
classification. We deployed the model in our online learning platform as a
crucial enabler for content understanding to enhance the student learning
experience.Comment: 18 pages, 6 figures, 4th International Conference on Semantic &
Natural Language Processing (SNLP 2023
Adjusting Pleasure-Arousal-Dominance for Continuous Emotional Text-to-speech Synthesizer
Emotion is not limited to discrete categories of happy, sad, angry, fear,
disgust, surprise, and so on. Instead, each emotion category is projected into
a set of nearly independent dimensions, named pleasure (or valence), arousal,
and dominance, known as PAD. The value of each dimension varies from -1 to 1,
such that the neutral emotion is in the center with all-zero values. Training
an emotional continuous text-to-speech (TTS) synthesizer on the independent
dimensions provides the possibility of emotional speech synthesis with
unlimited emotion categories. Our end-to-end neural speech synthesizer is based
on the well-known Tacotron. Empirically, we have found the optimum network
architecture for injecting the 3D PADs. Moreover, the PAD values are adjusted
for the speech synthesis purpose.Comment: Interspeech2019, Show and Tell demonstration
https://www.youtube.com/watch?v=MAOk_ZxuA0I&feature=youtu.b
Infection Curve Flattening via Targeted Interventions and Self-Isolation
Understanding the impact of network clustering and small-world properties on
epidemic spread can be crucial in developing effective strategies for managing
and controlling infectious diseases. Particularly in this work, we study the
impact of these network features on targeted intervention (e.g., self-isolation
and quarantine). The targeted individuals for self-isolation are based on
centrality measures and node influence metrics. Compared to our previous works
on scale-free networks, small-world networks are considered in this paper.
Small-world networks resemble real-world social and human networks. In this
type of network, most nodes are not directly connected but can be reached
through a few intermediaries (known as the small-worldness property). Real
social networks, such as friendship networks, also exhibit this small-worldness
property, where most people are connected through a relatively small number of
intermediaries. We particularly study the epidemic curve flattening by
centrality-based interventions/isolation over small-world networks. Our results
show that high clustering while having low small-worldness (higher shortest
path characteristics) implies flatter infection curves. In reality, a flatter
infection curve implies that the number of new cases of a disease is spread out
over a longer period of time, rather than a sharp and sudden increase in cases
(a peak in epidemic). In turn, this reduces the strain on healthcare resources
and helps to relieve the healthcare services
Epidemic modeling and flattening the infection curve in social networks
The main goal of this paper is to model the epidemic and flattening the
infection curve of the social networks. Flattening the infection curve implies
slowing down the spread of the disease and reducing the infection rate via
social-distancing, isolation (quarantine) and vaccination. The
nan-pharmaceutical methods are a much simpler and efficient way to control the
spread of epidemic and infection rate. By specifying a target group with high
centrality for isolation and quarantine one can reach a much flatter infection
curve (related to Corona for example) without adding extra costs to health
services. The aim of this research is, first, modeling the epidemic and, then,
giving strategies and structural algorithms for targeted vaccination or
targeted non-pharmaceutical methods for reducing the peak of the viral disease
and flattening the infection curve. These methods are more efficient for
nan-pharmaceutical interventions as finding the target quarantine group
flattens the infection curve much easier. For this purpose, a few number of
particular nodes with high centrality are isolated and the infection curve is
analyzed. Our research shows meaningful results for flattening the infection
curve only by isolating a few number of targeted nodes in the social network.
The proposed methods are independent of the type of the disease and are
effective for any viral disease, e.g., Covid-19.Comment: in Persian language. Journal of Modelling in Engineering 202
A population-based prospective study on obesity-related non-communicable diseases in northern Iran: rationale, study design, and baseline analysis
BackgroundIran is facing an epidemiological transition with the increasing burden of non-communicable diseases, such as obesity-related disorders and cardiovascular diseases (CVDs). We conducted a population-based prospective study to assess the prevalence and incidence rates of CVDs and obesity-related metabolic disorders and to evaluate the predictive ability of various CVD risk assessment tools in an Iranian population.MethodWe enrolled 5,799 participants in Amol, a city in northern Iran, in 2009–2010 and carried out the first repeated measurement (RM) after seven years (2016–2017). For all participants, demographic, anthropometric, laboratory, hepatobiliary imaging, and electrocardiography data have been collected in the enrollment and the RM. After enrollment, all participants have been and will be followed up annually for 20 years, both actively and passively.ResultsWe adopted a multidisciplinary approach to overcome barriers to participation and achieved a 7-year follow-up success rate of 93.0% with an active follow-up of 5,394 participants aged 18–90 years. In the RM, about 64.0% of men and 81.2% of women were obese or overweight. In 2017, about 16.2% and 5.2% of men had moderate or severe non-alcoholic fatty liver disease, while women had a significantly higher prevalence of metabolic syndrome (35.9%), and type 2 diabetes mellitus (20.9%) than men. Of 160 deceased participants, 69 cases (43.1%) died due to CVDs over seven years.ConclusionThe most prevalent obesity-related chronic disease in the study was metabolic syndrome. Across the enrollment and RM phases, women exhibited a higher prevalence of obesity-related metabolic disorders. Focusing on obesity-related metabolic disorders in a population not represented previously and a multidisciplinary approach for enrolling and following up were the strengths of this study. The study outcomes offer an evidence base for future research and inform policies regarding non-communicable diseases in northern Iran