Search results for: hand gesture classification

Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Mahesh Balan U, Rohith Srinivaas Mohanakrishnan, Venkat Subramanian

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Positive and unlabeled learning (PU) has gained more attention in both academic and industry research literature recently because of its relevance to existing business problems today. Yet, there still seems to be some existing challenges in terms of validating the performance of PU learning, as the actual truth of unlabeled data points is still unknown in contrast to a binary classification where we know the truth. In this study, we propose a novel PU learning technique based on the Pseudo-F measure, where we address this research gap. In this approach, we train the PU model to discriminate the probability distribution of the positive and unlabeled in the validation and spy data. The predicted probabilities of the PU model have a two-fold validation – (a) the predicted probabilities of reliable positives and predicted positives should be from the same distribution; (b) the predicted probabilities of predicted positives and predicted unlabeled should be from a different distribution. We experimented with this approach on a credit marketing case study in one of the world’s biggest fintech platforms and found evidence for benchmarking performance and backtested using historical data. This study contributes to the existing literature on semi-supervised learning.

Keywords: PU learning, semi-supervised learning, pseudo f measure, classification

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Authors: S. P. Raut, S. K. Soni, A. W. Kolhatkar

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Denim is widely used by every age of people all over the world. As the use of denim is increasing progressively, till now the handle properties of denim fabric not reported at significant level. In the present study, five commercial denim fabric samples were used. Denim samples, weighing from 8.5oz/sq yds to 14.5 oz/sq yds, were processed as per standard commercial procedure for denim finishing. These finished denim samples were tested on Kawabata Evaluation System(KES) for low stress mechanical properties. The results of KES values are used for calculation of Total Hand value(THV) using equation for summer suit. The obtained result for THV using equation for summer suit for denim samples is in the range from 1.62 to 3.30. These values of low stress mechanical properties values given by KES, can be used to engineer the denim fabric for bottom wear.

Keywords: denim, handle value, Kawabata evaluation system, objective evaluation

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Authors: Morteza Esfandiari, Shabnam Jabari, Heather MacGrath, David Coleman

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Flood is a severe issue in different places in the world as well as the city of Fredericton, New Brunswick, Canada. The downtown area of Fredericton is close to the Saint John River, which is susceptible to flood around May every year. Recently, the frequency of flooding seems to be increased, especially after the fact that the downtown area and surrounding urban/agricultural lands got flooded in two consecutive years in 2018 and 2019. In order to have an explicit vision of flood span and damage to affected areas, it is necessary to use either flood inundation modelling or satellite data. Due to contingent availability and weather dependency of optical satellites, and limited existing data for the high cost of hydrodynamic models, it is not always feasible to rely on these sources of data to generate quality flood maps after or during the catastrophe. Height Above the Nearest Drainage (HAND), a state-of-the-art topo-hydrological index, normalizes the height of a basin based on the relative elevation along with the stream network and specifies the gravitational or the relative drainage potential of an area. HAND is a relative height difference between the stream network and each cell on a Digital Terrain Model (DTM). The stream layer is provided through a multi-step, time-consuming process which does not always result in an optimal representation of the river centerline depending on the topographic complexity of that region. HAND is used in numerous case studies with quite acceptable and sometimes unexpected results because of natural and human-made features on the surface of the earth. Some of these features might cause a disturbance in the generated model, and consequently, the model might not be able to predict the flow simulation accurately. We propose to include a previously existing stream layer generated by the province of New Brunswick and benefit from culvert maps to improve the water flow simulation and accordingly the accuracy of HAND model. By considering these parameters in our processing, we were able to increase the accuracy of the model from nearly 74% to almost 92%. The improved model can be used for generating highly accurate flood maps, which is necessary for future urban planning and flood damage estimation without any need for satellite imagery or hydrodynamic computations.

Keywords: HAND, DTM, rapid floodplain, simplified conceptual models

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Rose Mini Agoes Salim, Shahnaz Safitri

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The aim of this study is to test the effectiveness of teaching kindness in preschool children by using several techniques. Kindness is a physical act or emotional support aimed to build or maintain relationships with others. Kindness is known to be essential in the development of moral reasoning to distinguish between the good and bad things. In this study, kindness is operationalized as several acts including helping friends, comforting sad friends, inviting friends to play, protecting others, sharing, saying hello, saying thank you, encouraging others, and apologizing. It is mentioned that kindness is crucial to be developed in preschool children because this is the time the children begin to interact with their social environment through play. Furthermore, preschool children's cognitive development makes them begin to represent the world with words, which then allows them to interact with others. On the other hand, preschool children egocentric thinking makes them still need to learn to consider another person's perspective. In relation to social interaction, preschool children need to be stimulated and assisted by adult to be able to pay attention to other and act with kindness toward them. On teaching kindness to children, the quality of interaction between children and their significant others is the key factor. It is known that preschool children learn about kindness by imitating adults on their two way interaction. Specifically, this study examines two types of teaching techniques that can be done by parents as a way to teach kindness, namely the picture-storybook reading and hand-puppet storytelling. These techniques were examined because both activities are easy to do and both also provide a model of behavior for the child based on the character in the story. To specifically examine those techniques effectiveness in teaching kindness, two studies were conducted. Study I involves 31 children aged 5-6 years old with picture-storybook reading technique, where the intervention is done by reading 8 picture books for 8 days. In study II, hand-puppet storytelling technique is examined to 32 children aged 3-5 years old. The treatments effectiveness are measured using an instrument in the form of nine colored cards that describe the behavior of kindness. Data analysis using Wilcoxon Signed-rank test shows a significant difference on the average score of kindness (p < 0.05) before and after the intervention has been held. For daily observation, a ‘kindness tree’ and observation sheets are used which are filled out by the teacher. Two weeks after interventions, an improvement on all kindness behaviors measured is intact. The same result is also gained from both ‘kindness tree’ and observational sheets.

Keywords: kindness, moral teaching, storytelling, hand puppet

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Authors: G. C. Tikkiwal, Mukesh Upadhyay

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During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving the army, moving convoys etc. The radar operator selects one of the promising targets into single target tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper, we present a technique using mathematical and statistical methods like fast fourier transformation (FFT) and principal component analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, FFT, principal component analysis, eigenvector, octave-notes, DSP

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Authors: Khawlah Al-Khalidi

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Prevalence of lower third molar impactions and angulations among Yemeni population The purpose of this study was to look into the prevalence of lower third molars in a sample of patients from Ibb University Affiliated Hospital, as well as to study and categorise their position by using Pell and Gregory classification, and to look into a possible correlation between their position and the indication for extraction. Materials and methods: This is a retrospective, observational study in which a sample of 200 patients from Ibb University Affiliated Hospital were studied, including patient record validation and orthopantomography performed in screening appointments in people aged 16 to 21. Results and discussion: Males make up 63% of the sample, while people aged 19 to 20 make up 41.2%. Lower third molars were found in 365 of the 365 instances examined, accounting for 91% of the sample under study. According to Pell and Gregory's categorisation, the most common position is IIB, with 37%, followed by IIA with 21%; less common classes are IIIA, IC, and IIIC, with 1%, 3%, and 3%, respectively. It was feasible to determine that 56% of the lower third molars in the sample were recommended for extraction during the screening consultation. Finally, there are differences in third molar location and angulation. There was, however, a link between the available space for third molar eruption and the need for tooth extraction.

Keywords: lower third molar, extraction, Pell and Gregory classification, lower third molar impaction

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

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The aim of the present work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. Based on feature selection in different phases, in this research, we design a neural network system that has optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each ROI, 6 distinct set of texture features are extracted such as first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. We show that with the injection of liquid and the analysis of more phases the high relevant features in each region changed. Our results show that for detecting HCC tumor phase3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between these two classes according to our method, relates to first order histogram parameters with the accuracy of 85% in phase 1, 95% phase 2, and 95% in phase 3.

Keywords: multi-phasic liver images, texture analysis, neural network, hidden layer

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Authors: Na Li, Yunwei Zhang, Yuhong Niu

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The study discussed the patent protections of health system and enterprises in Shanghai. The comparisons of technical distribution and scopes of patent protections between Shanghai health system and enterprises were used by the methods of IPC classification, co-words analysis and visual social network. Results reflected a decreasing order within IPC A61 area, namely A61B, A61K, A61M, and A61F. A61B required to be further investigated. The highest authorized patents A61B17 of A61B of IPC A61 area was found. Within A61B17, fracture fixation, ligament reconstruction, cardiac surgery, and biopsy detection were regarded as common concerned fields by Shanghai health system and enterprises. However, compared with cardiac closure which Shanghai enterprises paid attention to, Shanghai health system was more inclined to blockages and hemostatic tools. The results also revealed that the scopes of patent protections of Shanghai enterprises were relatively centralized. Shanghai enterprises had a series of comprehensive strategies for protecting core patents. In contrast, Shanghai health system was considered to be lack of strategic patent protections for core patents.

Keywords: co-words analysis, IPC classification, patent protection, technical distribution

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Authors: Asier Romero Andonegi, Irati De Pablo Delgado

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The objective of this study is to explore the different methodological procedures that are used to obtain information on the early linguistic development of children. To this end, two different methodological procedures were carried out on the same sample: on the one hand, the MacArthur-Bates Communicative Development Inventories, in its adaptations in Spanish and Basque; and on the other hand, longitudinal observation through professional software: ELAN and CHAT. The sample consists of 8 Basque children/ages 16 to 30 months with different mother tongue (L1). The results show the usefulness of inventories in obtaining information on the development of early communication and language skills, but also their limitations mostly focused on the interpretive overvaluation of their children’s lexical development.

Keywords: early language development, language evaluation, lexicon, MacArthur-Bates communicative development inventories

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Authors: Ayse Pekrioglu Balkis, Sawash Mecid

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Due to continued growth and rapid development of road construction in worldwide, road sub-layers consist of soil layers, therefore, identification and recognition of type of soil and soil behavior in different condition help to us to select soil according to specification and engineering characteristic, also if necessary sometimes stabilize the soil and treat undesirable properties of soils by adding materials such as bitumen, lime, cement, etc. If the soil beneath the road is not done according to the standards and construction will need more construction time. In this case, a large part of soil should be removed, transported and sometimes deposited. Then purchased sand and gravel is transported to the site and full depth filled and compacted. Stabilization by cement or other treats gives an opportunity to use the existing soil as a base material instead of removing it and purchasing and transporting better fill materials. Classification of soil according to AASHTOO system and USCS help engineers to anticipate soil behavior and select best treatment method. In this study soil classification and the relation between soil classification and stabilization method is discussed, cement stabilization with different percentages have been selected for soil treatment based on NCHRP. There are different parameters to define the strength of soil. In this study, CBR will be used to define the strength of soil. Cement by percentages, 0%, 3%, 7% and 10% added to soil for evaluation effect of added cement to CBR of treated soil. Implementation of stabilization process by different cement content help engineers to select an economic cement amount for the stabilization process according to project specification and characteristics. Stabilization process in optimum moisture content (OMC) and mixing rate effect on the strength of soil in the laboratory and field construction operation have been performed to see the improvement rate in strength and plasticity. Cement stabilization is quicker than a universal method such as removing and changing field soils. Cement addition increases CBR values of different soil types by the range of 22-69%.

Keywords: California Bearing Ratio, cement stabilization, clayey soil, mechanical properties

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Authors: Naman Solanki, Souraj Mondal

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With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

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Authors: Ermias A. Tegegn, Million Meshesha

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Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major cause of death for most African countries. Ethiopia is one of the seriously affected countries in sub Saharan Africa. Previously in Ethiopia, having HIV/AIDS was almost equivalent to a death sentence. With the introduction of Antiretroviral Therapy (ART), HIV/AIDS has become chronic, but manageable disease. The study focused on a data mining technique to predict future living status of HIV/AIDS patients at the time of drug regimen change when the patients become toxic to the currently taking ART drug combination. The data is taken from University of Gondar Hospital ART program database. Hybrid methodology is followed to explore the application of data mining on ART program dataset. Data cleaning, handling missing values and data transformation were used for preprocessing the data. WEKA 3.7.9 data mining tools, classification algorithms, and expertise are utilized as means to address the research problem. By using four different classification algorithms, (i.e., J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their parameters thirty-two models were built on the pre-processed University of Gondar ART program dataset. The performances of the models were evaluated using the standard metrics of accuracy, precision, recall, and F-measure. The most effective model to predict the status of HIV patients with drug regimen substitution is pruned J48 decision tree with a classification accuracy of 98.01%. This study extracts interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4 count, Age, Weight, and Gender so as to predict the status of drug regimen substitution. The outcome of this study can be used as an assistant tool for the clinician to help them make more appropriate drug regimen substitution. Future research directions are forwarded to come up with an applicable system in the area of the study.

Keywords: HIV drug regimen, data mining, hybrid methodology, predictive model

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Authors: Adriana Aparecida Rodrigues Leite

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Linguistics and grammar conceptions are necessary to comprehend the structure of a language. On one hand, grammar depicts structural rules and instructions. On the other hand, linguistcs is a science which intends to understand the changes that occur in a language. Ferdinand de Saussure throughout his book called: Cours de linguistique générale (Course of general Linguistics) developed theories that portray explanations which defines linguistics as a real object of study of a language. It differs from grammar which is seen by Saussure as an element without scientific pattern. By that means, this research plans to decipher whether Ferdinand de Saussure would be against these conceptions and rules proposed by grammar. Besides that, this paper was based on the exploratory approach to pose concrete principles and facts to provide a response for the problem. This research is divided in the following sections: Introduction, Ferdinand de Saussure, Linguistics Conceptions, Linguistics for Saussure, Grammar Conceptions, and Grammar for Saussure. The result obtained from the analysis of the problem is highlighted in the section: Final Considerations.

Keywords: linguistics, grammar, ferdinand de saussure, language

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Aliyu Maje Bello, Yaowaluck Maprang Roshorm

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Hand, foot, and mouth disease (HFMD) is a highly contagious viral infection affecting mostly infants and children. Although the Enterovirus A71 (EV71) is usually the major causative agent of HFMD, other enteroviruses such as coxsackievirus A16, A10, and A6 are also found in some of the recent outbreaks. The commercially available vaccines have demonstrated their effectiveness against only EV71 infection but no protection against other enteroviruses. To address the limitation of the monovalent EV71 vaccine, the present study thus designed a tetravalent vaccine against the four major enteroviruses causing HFMD and primarily evaluated the designed vaccine using an immunoinformatics approach. The immunogen was designed to contain the EV71 VP1 protein and multiple reported epitopes from all four distinct enteroviruses and thus designated a tetravalent vaccine. The 3D structure of the designed tetravalent vaccine was modeled, refined, and validated. Epitope screening showed the presence of B-cell, CTL, CD4 T cell, and IFN epitopes with vast application among the Asian population. Docking analysis confirmed the stable and strong binding interactions between the immunogen and immune receptor B-cell receptor (BCR). In silico cloning and immune simulation analyses guaranteed high efficiency and sufficient expression of the vaccine candidate in humans. Overall, the promising results obtained from the in-silico studies of the proposed tetravalent vaccine make it a potential candidate worth further experimental validation.

Keywords: enteroviruses, coxsackieviruses, hand foot and mouth disease, immunoinformatics, tetravalent vaccine

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Authors: Lali Khurtsia, Vano Tsertsvadze

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The purpose of the study was to find out incentives and expectations, methods and ways, which are influential to students during working with their thesis. Research findings shows that the use of plagiarism has cultural links deep in the history - on the one hand, the tradition of sharing knowledge in the oral manner, with its different interpretations, and on the other hand the lack of fair and honest methods in the academic process. Research results allow us to determine general ideas about preventive policy to reduce the use of plagiarism. We conducted surveys in three different groups – we interviewed so-called diploma writers, students on bachelors and masters level and the focus group of lecturers. We found that the problem with plagiarism in Georgia has cultural-mental character. We think that nearest years’ main task should be breaking of barriers existed between lecturers and students and acknowledgement of honest principals of study process among students and pupils.

Keywords: education, Georgia, plagiarism, study process, school, university

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Authors: Hee-Jung Seo, Yunjung Lee, Areum Han, Heeyoung Park, Se-Hyuk Park

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The purpose of this study was to analyze the relationship between happiness and depression among people with disabilities and to analyze the social phenomenon of leisure activities among them to promote physical and leisure activities for people with disabilities. The research methods included analyzing differences in happiness according to depression classification. A total of 281 people with disabilities were analyzed using SPSS WIN Ver. 29.0. In addition, the SumTrend platform was used to analyze terms related to 'leisure activities for the disabled.' The findings can be summarized into two main points: First, there were significant differences in happiness according to depression classification. Second, there were 20 mentions before COVID-19, 34 mentions after COVID-19, and currently 43 mentions, with high positive rates observed in each period. Based on these results, the following conclusions were drawn: First, measures for people with disabilities include strengthening online resources and services, social distancing response policies, improving accessibility, and providing support and financial assistance. Second, measures for non-disabled individuals emphasize the need for education and information provision, promoting dialogue and interaction, ensuring accessibility, and promoting inclusive cultural awareness and attitude change.

Keywords: leisure activities, individuals with disabilities, COVID-19 pandemic, depression

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Authors: N. Othman, J. Ujang, M. N. Ismail, R. Noordin, B. H. Lim

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Amoebiasis is caused by the Entamoeba histolytica and still endemic in many parts of the tropical region, worldwide. Currently, there is no available vaccine against amoebiasis. Hence, there is an urgent need to develop a vaccine. The excretory secretory antigen (ESA) of E. histolytica is a suitable biomarker for the vaccine candidate since it can modulate the host immune response. Hence, the objective of this study is to identify the proteome of the ESA towards finding suitable biomarker for the vaccine candidate. The non-gel based and gel-based proteomics analyses were performed to identify proteins. Two kinds of mass spectrometry with different ionization systems were utilized i.e. LC-MS/MS (ESI) and MALDI-TOF/TOF. Then, the functional proteins classification analysis was performed using PANTHER software. Combination of the LC -MS/MS for the non-gel based and MALDI-TOF/TOF for the gel-based approaches identified a total of 273 proteins from the ESA. Both systems identified 29 similar proteins whereby 239 and 5 more proteins were identified by LC-MS/MS and MALDI-TOF/TOF, respectively. Functional classification analysis showed the majority of proteins involved in the metabolic process (24%), primary metabolic process (19%) and protein metabolic process (10%). Thus, this study has revealed the proteome the E. histolytica ESA and the identified proteins merit further investigations as a vaccine candidate.

Keywords: E. histolytica, ESA, proteomics, biomarker

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Authors: Maria Vinca Novenia

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The number of school-aged children with autism in Indonesia has been increasing each year. Autism is a developmental disorder which can be diagnosed in childhood. One of the symptoms is the lack of communication skills. Music therapy is known as an effective treatment for children with autism. Music elements and structures create a good space for children with autism to express their feelings and communicate their thoughts. School-aged children are expected to be able to communicate non-verbally very well, but children with autism experience the difficulties of communicating non-verbally. The aim of this research is to analyze the significance of music therapy treatment to improve non-verbal communication tools for children with autism. This research informs teachers and parents on how music can be used as a media to communicate with children with autism. The qualitative method is used to analyze this research, while the result is described with the microanalysis technique. The result is measured specifically from the whole experiment, hours of every week, minutes of every session, and second of every moment. The samples taken are four school-aged children with autism in the age range of six to 11 years old. This research is conducted within four months started with observation, interview, literature research, and direct experiment. The result demonstrates that music therapy could be effectively used as a non-verbal communication tool for children with autism, such as changes of body gesture, eye contact, and facial expression.

Keywords: autism, improvisation, microanalysis, music therapy, nonverbal communication, school-aged

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Authors: Elena Jiménez Lidueña, Noelia Santos Muriel, Patricia López Resa, Noelia Pulido García, Esther Moraleda Sepúlveda

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In recent years, there has been a notable increase in the number of investigations that establish that Down Syndrome and Autism Spectrum Disorder are diagnoses that can coexist together. However, there are also many studies that consider that both diagnoses present neuropsychological, linguistic and adaptive characteristics with a totally different profiles. The objective of this research is to question whether there really can be a profile that encompasses both disorders or if they can be incompatible with each other. To this end, a review of the scientific literature of recent years has been carried out. The results indicate that the two lines collect opposite approaches. On the one hand, there is research that supports the increase in comorbidity between Down Syndrome and Autism Spectrum Disorder and, on the other hand, shows a totally different general development profile between the two. The discussion focuses on discussing both lines of work and on proposing future lines of research in this regard.

Keywords: Down Syndrome, Autism, comorbidity, linguistic

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Authors: Kuei-Ling Sun, Emily Chia-Yu Su

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Allergy is a hypersensitive overreaction of the immune system to environmental stimuli, and a major health problem. These overreactions include rashes, sneezing, fever, food allergies, anaphylaxis, asthmatic, shock, or other abnormal conditions. Allergies can be caused by food, insect stings, pollen, animal wool, and other allergens. Their development of allergies is due to both genetic and environmental factors. Allergies involve immunoglobulin E antibodies, a part of the body’s immune system. Immunoglobulin E antibodies will bind to an allergen and then transfer to a receptor on mast cells or basophils triggering the release of inflammatory chemicals such as histamine. Based on the increasingly serious problem of environmental change, changes in lifestyle, air pollution problem, and other factors, in this study, we both collect allergens and non-allergens from several databases and use several machine learning methods for classification, including logistic regression (LR), stepwise regression, decision tree (DT) and neural networks (NN) to do the model comparison and determine the permutations of allergen amino acid’s sequence.

Keywords: allergy, classification, decision tree, logistic regression, machine learning

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Authors: Jaishree Ranganathan, MuthuPriya Shanmugakani Velsamy, Shamika Kulkarni, Angelina Tzacheva

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Emotions play an important role in everyday life. An-alyzing these emotions or feelings from social media platforms like Twitter, Facebook, blogs, and forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to benefit the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to benefit the end user. In this paper, we propose automatic classification of emotions in Twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion that helps to provide actionable suggestion.

Keywords: emotion mining, twitter, recurrent neural network, gated recurrent unit, actionable pattern mining

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Authors: Sigal Portnoy, Jason Friedman, Eitan Raveh

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Introduction: Sensory substitution is possible due to the capacity of our brain to adapt to information transmitted by a synthetic receptor via an alternative sensory system. Practical sensory substitution systems are being developed in order to increase the functionality of individuals with sensory loss, e.g. amputees. For upper limb prosthetic-users the loss of tactile feedback compels them to allocate visual attention to their prosthesis. The effect of adding vibrotactile feedback (VTF) to the applied force has been studied, however its effect on the allocation if visual attention during dual-tasking and the response during misleading visual feedback have not been studied. We hypothesized that VTF will improve the performance and reduce visual attention during dual-task assignments in healthy individuals using a robotic hand and improve the performance in a standardized functional test, despite the presence of misleading visual feedback. Methods: For the dual-task paradigm, twenty healthy subjects were instructed to toggle two keyboard arrow keys with the left hand to retain a moving virtual car on a road on a screen. During the game, instructions for various activities, e.g. mix the sugar in the glass with a spoon, appeared on the screen. The subject performed these tasks with a robotic hand, attached to the right hand. The robotic hand was controlled by the activity of the flexors and extensors of the right wrist, recorded using surface EMG electrodes. Pressure sensors were attached at the tips of the robotic hand and induced VTF using vibrotactile actuators attached to the right arm of the subject. An eye-tracking system tracked to visual attention of the subject during the trials. The trials were repeated twice, with and without the VTF. Additionally, the subjects performed the modified box and blocks, hidden from eyesight, in a motion laboratory. A virtual presentation of a misleading visual feedback was be presented on a screen so that twice during the trial, the virtual block fell while the physical block was still held by the subject. Results: This is an ongoing study, which current results are detailed below. We are continuing these trials with transradial myoelectric prosthesis-users. In the healthy group, the VTF did not reduce the visual attention or improve performance during dual-tasking for the tasks that were typed transfer-to-target, e.g. place the eraser on the shelf. An improvement was observed for other tasks. For example, the average±standard deviation of time to complete the sugar-mixing task was 13.7±17.2s and 19.3±9.1s with and without the VTF, respectively. Also, the number of gaze shifts from the screen to the hand during this task were 15.5±23.7 and 20.0±11.6, with and without the VTF, respectively. The response of the subjects to the misleading visual feedback did not differ between the two conditions, i.e. with and without VTF. Conclusions: Our interim results suggest that the performance of certain activities of daily living may be improved by VTF. The substitution of visual sensory input by tactile feedback might require a long training period so that brain plasticity can occur and allow adaptation to the new condition.

Keywords: prosthetics, rehabilitation, sensory substitution, upper limb amputation

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Authors: Artur Matuck, Guilherme F. Nobre

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Machines can be either tool, media, or social agents. Advances in technology have been delivering machines capable of autonomous expression, both through communication and art. This paper deals with models (theoretical approach) and experiments (applied approach) related to artificial agents. On one hand it traces how social sciences' scholars have worked with topics such as text automatization, man-machine writing cooperation, and communication. On the other hand it covers how computer sciences' scholars have built communicative and artistic machines, including the programming of creativity. The aim is to present a brief survey on artificially intelligent communicators and artificially creative writers, and provide the basis to understand the meta-authorship and also to new and further man-machine co-authorship.

Keywords: artificial communication, artificial creativity, artificial writers, meta-authorship, robotic art

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Authors: Li Niu, Jihong Liang, Lichao Liu, Huidi Chen

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With the drawing of ancient palace painter, the layout of Beijing famous architect and the lens under photographers, a series of pictures which described whether emperors or ordinary people, whether gardens or Hutongs, whether historical events or life scenarios has emerged into our society. These precious resources are scattered around and preserved in different places Such as organizations like archives and libraries, along with individuals. The research combined decentralized photographic resources with Geographic Information System (GIS), focusing on the figure, event, time and location of the pictures to map them with geographic information in webpage and to display them productively. In order to meet the demand of reality, we designed a metadata description proposal, which is referred to DC and VRA standards. Another essential procedure is to formulate a four-tier classification system to correspond with the metadata proposals. As for visualization, we used Photo Waterfall and Time Line to display our resources in front end. Last but not the least, leading the Web 2.0 trend, the research developed an artistic, friendly, expandable, universal and user involvement platform to show the historical and culture precipitation of Beijing.

Keywords: historical picture, geographic information system, display platform, four-tier classification system

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Authors: Tawfik Thelaidjia, Salah Chenikher

Abstract:

Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach

Keywords: condition monitoring, discrete wavelet transform, fault diagnosis, kurtosis, machine learning, particle swarm optimization, roller bearing, rotating machines, support vector machine, vibration measurement

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Authors: Dangut Maren David, Skaf Zakwan

Abstract:

Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

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Authors: Elizabeta Eduard Baltadzhyan

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This work presents a stage of an investigation about the metaphorical conceptualization of translation in Bulgarian language. The material is a linguistic corpus consisting of 38 interviews with several generations Bulgarian translators and interpreters. The aim of this presentation is to inform about the results of the organization of the source concepts in scenarios that dominate the discursive manifestations of the source domains. The data show that, on the one hand, translators from different generations share some basic assignments of source and target domains, e. g. translation is a journey or translation is an artistic presentation. On the other hand, there are some specific scenarios motivated by significant changes in the socio-economic structure of the country and the valuation of the translator´s mission and work, e. g., the scenario of pleasure and addictive activity marks the generation that enjoy great support and stimulation from the socialist government, whereas the war scenario marks the generation during the Perestroika time.

Keywords: Bulgarian language, metaphor, scenario, translation

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