Search results for: collecting speech emotion dataset

Authors: Sherri Franklin-Guy

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The emergence of language in young children has been well-documented and play-based activities that support its continued development have been utilized in the clinic-based setting. Speech-language pathologists have long used such activities to stimulate the production of language in children with speech and language disorders via modeling and elicitation tasks. This presentation will examine the importance of play in the development of language in young children, including social and pragmatic communication. Implications for clinicians and educators will be discussed.

Keywords: language development, language stimulation, play-based activities, symbolic play

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Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

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Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

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Authors: Govind Ji Pandey

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The divergent opinions in the society are important for its development but with reasonable restrictions. The world recently witnessed one of the most violent protests by a group against the editor and publisher of the magazine ‘Charlie Hebdo’ for publishing cartoon of their religious leader. The supporter of freedom of speech and expression around the world were in shock and termed it the strongest attack against the free speech. People all around the world condemned the killing of the journalists but many soft voices from several corners were also coming for reasonable restrictions on the freedom of speech and expression. Of late, Indian society has witnessed many protests and supports of films with controversial content. It is the beauty of the Indian democracy which gives an opportunity to all for discussion and debate on any issue that challenges established social norms. However, many organizations as well as individuals misuse it for their personal benefits. There have been many film directors who faced protest from several quarters for their controversial themes. This research aims at analyzing the controversial contents published in print media and shown in films. To understand the nature and frequency of such media reports, content analysis technique is used. The research also highlights the perception of the public regarding the controversies. For getting the popular opinion on the coverage of controversial content in cinema and print media, five hundred people from Lucknow, UP, India were randomly selected. The findings of this research are important to understand the response of media and society towards the controversial content presented in cinema and print media. The research highlights that how a handful of people curb free speech in a democratic country like India.

Keywords: cinema, censor board, free speech, liberty, social-religious beliefs

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: Essam Al Daoud

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Gradient boosting methods have been proven to be a very important strategy. Many successful machine learning solutions were developed using the XGBoost and its derivatives. The aim of this study is to investigate and compare the efficiency of three gradient methods. Home credit dataset is used in this work which contains 219 features and 356251 records. However, new features are generated and several techniques are used to rank and select the best features. The implementation indicates that the LightGBM is faster and more accurate than CatBoost and XGBoost using variant number of features and records.

Keywords: gradient boosting, XGBoost, LightGBM, CatBoost, home credit

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Authors: Sachin, Shantanu Arya, Gunjan Mehta, Md. Shamim Ansari

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The cross-modal influence of visual information on speech perception can be illustrated by the McGurk effect which is an illusion of hearing of syllable /ta/ when a listener listens one syllable, e.g.: /pa/ while watching a synchronized video recording of syllable, /ka/. The McGurk effect is an excellent tool to investigate multisensory integration in speech perception in both normal hearing and hearing impaired populations. As the visual cue is unaffected by noise, individuals with hearing impairment rely more than normal listeners on the visual cues.However, when non congruent visual and auditory cues are processed together, audiovisual interaction seems to occur differently in normal and persons with hearing impairment. Therefore, this study aims to observe the audiovisual interaction in speech perception in Cochlear Implant users compares the same with normal hearing children. Auditory stimuli was routed through calibrated Clinical audiometer in sound field condition, and visual stimuli were presented on laptop screen placed at a distance of 1m at 0 degree azimuth. Out of 4 presentations, if 3 responses were a fusion, then McGurk effect was considered to be present. The congruent audiovisual stimuli /pa/ /pa/ and /ka/ /ka/ were perceived correctly as ‘‘pa’’ and ‘‘ka,’’ respectively by both the groups. For the non- congruent stimuli /da/ /pa/, 23 children out of 35 with normal hearing and 9 children out of 35 with cochlear implant had a fusion of sounds i.e. McGurk effect was present. For the non-congruent stimulus /pa/ /ka/, 25 children out of 35 with normal hearing and 8 children out of 35 with cochlear implant had fusion of sounds.The children who used cochlear implants for less than three years did not exhibit fusion of sound i.e. McGurk effect was absent in this group of children. To conclude, the results demonstrate that consistent fusion of visual with auditory information for speech perception is shaped by experience with bimodal spoken language during early life. When auditory experience with speech is mediated by cochlear implant, the likelihood of acquiring bimodal fusion is increased and it greatly depends on the age of implantation. All the above results strongly support the need for screening children for hearing capabilities and providing cochlear implants and aural rehabilitation as early as possible.

Keywords: cochlear implant, congruent stimuli, mcgurk effect, non-congruent stimuli

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Authors: Festa Shabani

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This paper aims at investigating bilingual speech in the International Schools of Prishtina. More particularly, it seeks to analyze bilingual phenomena among adolescent students highly exposed to English with the latter as the language of instruction at school in naturally-occurring conversations within school environment. Adolescence was deliberately chosen since it is regarded as an age when peer influence on language choice is the greatest. Driven by daily unsystematic observation and prior research already undertaken, the hypothesis stated is that Albanian continues to be the dominant language among Prishtina international schools’ students with a lot of code-switched items from the English. Furthermore, they will also use lexical borrowings - words already adapted in the receiving language, from the language they have been in contact with, in their speech often in the lack of existing equivalents in Albanian or for other reasons. This is done owing to the fact that the language of instruction at school is English, and any topic related to the language they have been exposed to will trigger them to use English. Therefore, this needs special attention in an attempt to identify patterns of their speech; in this way, linguistic and socio-pragmatic factors will be considered when analyzing the motivations behind their language choice. Methodology for collecting data include participant systematic observation and tape-recording. While observing them in their natural conversations, the fieldworker also took notes, which helped transcribe details better. The paper starts by raising the question of whether code-switching is occurring among Prishtina International Schools’ students highly exposed to English. The data gathered from students in informal settings suggests that there are well-founded grounds for an affirmative answer. The participants in this study are observed to be code-switching, although showing differences in degree. However, a generalization cannot be made on the basis of the findings except in so far it appears that English has, in turn, became a language to which they turn when identifying with the group when discussing about particular school topics. Particularly, participants seemed to use intra-sentential CS in cases when they seem to find an English expression rather easier than an Albanian one when repeating or emphasizing a point when urged to talk about educational issues with English being their language of instruction, and inter-sentential code-switching, particularly when quoting others. Concerning the grammatical aspect of code-switching, the intrasentential CS is used more than the intersentetial one. Speaking of gender, the results show that there were really no significant differences in regards quantity between male and female participants. However, the slight tendency for men to code switch intrasententially more than women was manifested. Similarly, a slight tendency again for a difference to emerge is on intersentential switching, which contributes 21% to the total number of switches for women, but 11% to the total number of switches for men.

Keywords: Albanian, code-switching contact linguistics, bilingual phenomena, lexical borrowing, English

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Authors: Methawee Yuttapongtada

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Intensifier is the linguistic term and device that is generally found in different languages in order to enhance and give additional quantity, quality or emotion to the words of each language. In fact, each language in the world has both of the similar and dissimilar intensifying device. More specially, the wide variety of intensifying device is used for Thai language and one of those is usage of the impolite word or the word that used to mean something negative as intensifier. The data collection in this study was done throughout the spoken language style by collecting from intensifiers regarded as impolite words because these words as employed in the other contexts will be held as the rude, swear words or the words with negative meaning. Then, backward study to the past was done in order to consider the historical change. Explanation of the original meaning and the contexts of words use from the past till the present time were done by use of both textual documents and dictionaries available in different periods. It was found that regarding the semantics and pragmatic aspects, subjectification also is the significant motivation that changed the impolite words to intensifiers. At last, it can explain pathway of the semantic change of these very words undoubtedly. Moreover, it is found that use tendency in the impolite word or the word that used to mean something negative will more be increased and this phenomenon is commonly found in many languages in the world and results of this research may support to the belief that human language in the world is universal and the same still reflected that human has the fundamental thought as the same to each other basically.

Keywords: impolite word, intensifier, Thai, semantic change

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Authors: Samaneh Abedini, Diana Coholic

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The transition from adolescence into adulthood involves many changes that result in increased vulnerability to psychological challenges. This developmental stage can be especially stressful for female youth living in underserviced regions. If mental health problems are left untreated in socially marginalized youth, these challenges can extend into adulthood. We know that a lack of access to mental health services and supports can influence adolescents’ psycho-social development and well-being, while resilience and emotion regulation can help them cope with these challenges. Feasible therapeutic programs can play a significant role in assisting youth in developing these characteristics and skills. Mindfulness-Based Cognitive Therapy for Children (MBCT-C) and Holistic Art-Based Program (HAP) are two examples of mindfulness-based interventions (MBIs) that address emotion regulation, coping strategies, and resilience in marginalized youth. While each program’s beneficial effects have been documented, there is a lack of research comparing MBIs with youth, within underserviced geographical locations, and across different cultures. In this study, the sample was 42 female youth between the ages of 12 and 17 years from Iran. 42 female youth from the Elm o Honar High School, located in rural parts of Iran, Isfahan province, have been enrolled in the study. The participants were assigned to one of the MBIs (three MBCT-C experimental groups (n=20) and three HAP experimental groups (n=22)). All participants completed measures including the Child and Youth Resilience Measure-28 (CYRM-28), Child and Adolescent Mindfulness Measure (CAMM), and Difficulties in Emotion Regulation Scale (DERS) at baseline and post-intervention. At the end of intervention, the MBCT-C and HAP experimental groups showed significant changes in resilience and emotion regulation. However, the changes in resilience in HAP groups were not significant; the participants in MBCT-C experimental groups showed significant improvement in resilience. The study provided initial evidence that mindfulness-based intervention can be potentially beneficial for improving mental health status in marginalized Iranian female youth living in the middle east culture.

Keywords: benefits, female, marginalized, mindfulness, youth

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Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

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Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

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Authors: Sanja Seljan, Ivan Dunđer

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The paper presents combined automatic speech recognition (ASR) for English and machine translation (MT) for English and Croatian in the domain of business correspondence. The first part presents results of training the ASR commercial system on two English data sets, enriched by error analysis. The second part presents results of machine translation performed by online tool Google Translate for English and Croatian and Croatian-English language pairs. Human evaluation in terms of usability is conducted and internal consistency calculated by Cronbach's alpha coefficient, enriched by error analysis. Automatic evaluation is performed by WER (Word Error Rate) and PER (Position-independent word Error Rate) metrics, followed by investigation of Pearson’s correlation with human evaluation.

Keywords: automatic machine translation, integrated language technologies, quality evaluation, speech recognition

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Authors: Heeba A. Gurku

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Introduction: Cone Beam CT(CBCT) images play an integral part in proper patient positioning in cancer patients undergoing radiation therapy treatment. But these images are low in quality. The purpose of this study is to generate high-quality synthetic CT images from CBCT using generative models. Material and Methods: This study utilized two datasets from The Cancer Imaging Archive (TCIA) 1) Lung cancer dataset of 20 patients (with full view CBCT images) and 2) Pancreatic cancer dataset of 40 patients (only 27 patients having limited view images were included in the study). Cycle Generative Adversarial Networks (GAN) and its variant Attention Guided Generative Adversarial Networks (AGGAN) models were used to generate the synthetic CTs. Models were evaluated by visual evaluation and on four metrics, Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR) Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), to compare the synthetic CT and original CT images. Results: For pancreatic dataset with limited view CBCT images, our study showed that in Cycle GAN model, MAE, RMSE, PSNR improved from 12.57to 8.49, 20.94 to 15.29 and 21.85 to 24.63, respectively but structural similarity only marginally increased from 0.78 to 0.79. Similar, results were achieved with AGGAN with no improvement over Cycle GAN. However, for lung dataset with full view CBCT images Cycle GAN was able to reduce MAE significantly from 89.44 to 15.11 and AGGAN was able to reduce it to 19.77. Similarly, RMSE was also decreased from 92.68 to 23.50 in Cycle GAN and to 29.02 in AGGAN. SSIM and PSNR also improved significantly from 0.17 to 0.59 and from 8.81 to 21.06 in Cycle GAN respectively while in AGGAN SSIM increased to 0.52 and PSNR increased to 19.31. In both datasets, GAN models were able to reduce artifacts, reduce noise, have better resolution, and better contrast enhancement. Conclusion and Recommendation: Both Cycle GAN and AGGAN were significantly able to reduce MAE, RMSE and PSNR in both datasets. However, full view lung dataset showed more improvement in SSIM and image quality than limited view pancreatic dataset.

Keywords: CT images, CBCT images, cycle GAN, AGGAN

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Authors: Naouel Zoghlami

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Spoken-word recognition involves the simultaneous activation of potential word candidates which compete with each other for final correct recognition. In continuous speech, the activation-competition process gets more complicated due to speech reductions existing at word boundaries. Lexical processing is more difficult in L2 than in L1 because L2 listeners often lack phonetic, lexico-semantic, syntactic, and prosodic knowledge in the target language. In this study, we investigate the on-line lexical segmentation hypotheses that French listeners of L2 English form and then revise as subsequent perceptual evidence is revealed. Our purpose is to shed further light on the processes of L2 spoken-word recognition in context and better understand L2 listening difficulties through a comparison of skilled and unskilled reactions at the point where their working hypothesis is rejected. We use a variant of the gating experiment in which subjects transcribe an English sentence presented in increments of progressively greater duration. The spoken sentence was “And this amazing athlete has just broken another world record”, chosen mainly because it included common reductions and phonetic features in English, such as elision and assimilation. Our preliminary results show that there is an important difference in the manner in which proficient and less-proficient L2 listeners handle connected speech. Less-proficient listeners delay recognition of words as they wait for lexical and syntactic evidence to appear in the gates. Further statistical results are currently being undertaken.

Keywords: gating paradigm, spoken word recognition, online lexical segmentation, L2 listening

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Authors: Arianna Vedaschi

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This paper addresses the relationship between freedom of expression, national security and radicalization. Is it still possible to talk about a balance between the first two elements? Or, due to the intrusion of the third, is it more appropriate to consider freedom of expression as “permanently disfigured” by securitarian concerns? In this study, both the legislative and the judicial level are taken into account and the comparative method is employed in order to provide the reader with a complete framework of relevant issues and a workable set of solutions. The analysis moves from the finding according to which the tension between free speech and national security has become a major issue in democratic countries, whose very essence is continuously endangered by the ever-changing and multi-faceted threat of international terrorism. In particular, a change in terrorist groups’ recruiting pattern, attracting more and more people by way of a cutting-edge communicative strategy, often employing sophisticated technology as a radicalization tool, has called on law-makers to modify their approach to dangerous speech. While traditional constitutional and criminal law used to punish speech only if it explicitly and directly incited the commission of a criminal action (“cause-effect” model), so-called glorification offences – punishing mere ideological support for terrorism, often on the web – are becoming commonplace in the comparative scenario. Although this is direct, and even somehow understandable, consequence of the impending terrorist menace, this research shows many problematic issues connected to such a preventive approach. First, from a predominantly theoretical point of view, this trend negatively impacts on the already blurred line between permissible and prohibited speech. Second, from a pragmatic point of view, such legislative tools are not always suitable to keep up with ongoing developments of both terrorist groups and their use of technology. In other words, there is a risk that such measures become outdated even before their application. Indeed, it seems hard to still talk about a proper balance: what was previously clearly perceived as a balancing of values (freedom of speech v. public security) has turned, in many cases, into a hierarchy with security at its apex. In light of these findings, this paper concludes that such a complex issue would perhaps be better dealt with through a combination of policies: not only criminalizing ‘terrorist speech,’ which should be relegated to a last resort tool, but acting at an even earlier stage, i.e., trying to prevent dangerous speech itself. This might be done by promoting social cohesion and the inclusion of minorities, so as to reduce the probability of people considering terrorist groups as a “viable option” to deal with the lack of identification within their social contexts.

Keywords: radicalization, free speech, international terrorism, national security

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Authors: Pushpavathi M., Kavya V., Akshatha V.

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Introduction: Cleft lip and palate (CLP) is a congenital condition which hinders effectual communication due to associated speech and language difficulties. Expressive language delay (ELD) is a feature seen in this population which is influenced by factors such as type and severity of CLP, age at surgical and linguistic intervention and also the type and intensity of speech and language therapy (SLT). Since CLP is the most common congenital abnormality seen in Indian children, early intervention is a necessity which plays a critical role in enhancing their speech and language skills. The interaction between the timing of intervention and factors which contribute to effective intervention by caregivers is an area which needs to be explored. Objectives: The present study attempts to determine the effect of timing of intervention on the contributing maternal factors for effective linguistic intervention in toddlers with repaired CLP with respect to the awareness, home training patterns, speech and non-speech behaviors of the mothers. Participants: Thirty six toddlers in the age range of 1 to 4 years diagnosed as ELD secondary to repaired CLP, along with their mothers served as participants. Group I (Early Intervention Group, EIG) included 19 mother-child pairs who came to seek SLT soon after corrective surgery and group II (Delayed Intervention Group, DIG) included 16 mother-child pairs who received SLT after the age of 3 years. Further, the groups were divided into group A, and group B. Group ‘A’ received SLT for 60 sessions by Speech Language Pathologist (SLP), while Group B received SLT for 30 sessions by SLP and 30 sessions only by mother without supervision of SLP. Method: The mothers were enrolled for the Early Language Intervention Program and following this, their awareness about CLP was assessed through the Parental awareness questionnaire. The quality of home training was assessed through Mohite’s Inventory. Subsequently, the speech and non-speech behaviors of the mothers were assessed using a Mother’s behavioral checklist. Detailed counseling and orientation was done to the mothers, and SLT was initiated for toddlers. After 60 sessions of intensive SLT, the questionnaire and checklists were re-administered to find out the changes in scores between the pre- and posttest measurements. Results: The scores obtained under different domains in the awareness questionnaire, Mohite’s inventory and Mothers behavior checklist were tabulated and subjected to statistical analysis. Since the data did not follow normal distribution (i.e. p > 0.05), Mann-Whitney U test was conducted which revealed that there was no significant difference between groups I and II as well as groups A and B. Further, Wilcoxon Signed Rank test revealed that mothers had better awareness regarding issues related to CLP and improved home-training abilities post-orientation (p ≤ 0.05). A statistically significant difference was also noted for speech and non-speech behaviors of the mothers (p ≤ 0.05). Conclusions: Extensive orientation and counseling helped mothers of both EI and DI groups to improve their knowledge about CLP. Intensive SLT using focused stimulation and a parent-implemented approach enabled them to carry out the intervention in an effectual manner.

Keywords: awareness, cleft lip and palate, early language intervention program, home training, orientation, timing of intervention

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Swapna N., Deepthy Ann Joy

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One of the major causes of communication disorders in pediatric population is Motor speech disorders. These disorders which affect the motor aspects of speech articulators can have an adverse effect on the communication abilities of children in their developmental period. The motor aspects are dependent on the sensory abilities of children with motor speech disorders. Hence, oral sensorimotor evaluation is an important component in the assessment of children with motor speech disorders. To our knowledge, the importance of oral motor examination has been well established, yet the sensory assessment of the oral structures has received less focus. One of the most common motor speech disorders seen in children is developmental dysarthria. The present study aimed to assess the orosensory aspects in children with developmental dysarthria (CDD). The control group consisted of 240 children in the age range of four and eight years which was divided into four subgroups (4-4.11, 5-5.11, 6-6.11 and 7-7.11 years). The experimental group consisted of 15 children who were diagnosed with developmental dysarthria secondary to cerebral palsy who belonged in the age range of four and eight years. The oro-sensory aspects such as response to touch, temperature, taste, texture, and orofacial sensitivity were evaluated and profiled. For this purpose, the authors used the ‘Oral Sensorimotor Evaluation Protocol- Children’ which was developed by the authors. The oro-sensory section of the protocol was administered and the clinical profile of oro-sensory abilities of typically developing children and CDD was obtained for each of the sensory abilities. The oro-sensory abilities of speech articulators such as lips, tongue, palate, jaw, and cheeks were assessed in detail and scored. The results indicated that experimental group had poorer scores on oro-sensory aspects such as light static touch, kinetic touch, deep pressure, vibration and double simultaneous touch. However, it was also found that the experimental group performed similar to control group on few aspects like temperature, taste, texture and orofacial sensitivity. Apart from the oro-motor abilities which has received utmost interest, the variation in the oro-sensory abilities of experimental and control group is highlighted and discussed in the present study. This emphasizes the need for assessing the oro-sensory abilities in children with developmental dysarthria in addition to oro-motor abilities.

Keywords: cerebral palsy, developmental dysarthria, orosensory assessment, touch

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Authors: Vivian Franco

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There has been a great deal of research about vowel production of second language learners of Spanish, vowel variation across Spanish dialects, and more recently, research related to Spanish heritage speakers’ vowel production based on speech style. However, there is little investigation reported on Spanish heritage speakers’ vowel production in regard to task modality by incorporating own comparison groups of monolinguals and late bilinguals. Thus, the present study investigates the influence of speech style on Spanish heritage speakers’ vowel production by comparing Spanish-English early and late bilinguals and Spanish monolinguals. The study was guided by the following research question: How do early bilinguals (heritage speakers) differ/relate to advanced L2 speakers of Spanish (late bilinguals) and Spanish monolinguals in their vowel quality (acoustic distribution) and quantity (duration) based on speech style? The participants were a total of 11 speakers of Spanish: 7 early Spanish-English bilinguals with a similar linguistic background (simultaneous bilinguals of the second generation); 2 advanced L2 speakers of Spanish; and 2 Spanish monolinguals from Mexico. The study consisted of two tasks. The first one adopted a semi-spontaneous style by a solicited narration of life experiences and a description of a favorite movie with the purpose to collect spontaneous speech. The second task was a reading activity in which the participants read two paragraphs of a Mexican literary essay 'La nuez.' This task aimed to obtain a more controlled speech style. From this study, it can be concluded that early bilinguals and monolinguals show a smaller formant vowel space overall compared to the late bilinguals in both speech styles. In terms of formant values by stress, the early bilinguals and the late bilinguals resembled in the semi-spontaneous speech style as their unstressed vowel space overlapped with that of the unstressed vowels different from the monolinguals who displayed a slightly reduced unstressed vowel space. For the controlled data, the early bilinguals were similar to the monolinguals as their stressed and unstressed vowel spaces overlapped in comparison to the late bilinguals who showed a more clear reduction of unstressed vowel space. In regard to stress, the monolinguals revealed longer vowel duration overall. However, findings of duration by stress showed that the early bilinguals and the monolinguals remained stable with shorter values of unstressed vowels in the semi-spontaneous data and longer duration in the controlled data when compared to the late bilinguals who displayed opposite results. These findings suggest an implication for Spanish heritage speakers and L2 Spanish vowels research as it has been frequently argued that Spanish bilinguals differ from the Spanish monolinguals by their vowel reduction and centralized vowel space influenced by English. However, some Spanish varieties are characterized by vowel reduction especially in certain phonetic contexts so that some vowels present more weakening than others. Consequently, it would not be conclusive to affirm an English influence on the Spanish of these bilinguals.

Keywords: Spanish-English bilinguals, Spanish monolinguals, spontaneous and controlled speech, vowel production.

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Authors: Zaralyn Bernardo, Dante Boac, Annabelle Del Rosario

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Professional individuals who are in a primary role to impart learning with the new generation are alarmingly tend to have a vast decrease in their workforce due to stress at work. Thus, the study used mixed method research design to explore the ideal and actual coping patterns of college school teachers, both private and public, using Coping Response Inventory-Adult (CRI-Adult). It was suggested that in order for coping to be effective there must be a congruence or good match between coping efforts and preferred coping style. Results basically provided the same information on sources of teacher stress. However, workload and low salary were more likely heightened, for public and private school, respectively. There is also a significant difference between the ideal and actual coping style of college school teachers. Though the public school teachers leaned towards problem-focused as their ideal way of coping, both public and private teachers are somewhat inclined to use emotion-focused coping in actual situation. Results of FGD identified the factors that contribute to the incongruence or mismatch in their preferred style of coping and actual efforts to cope. Identified factors based on thematic analysis (TA) are clustered into themes such as affectivity and rehearsal of the preferred coping responses, sensitivity to pressure impairs coping efficacy, seeking for social acceptance and approval, indefinite appraisal of perceived stress, emotional dysregulation, and impulsivity, immediate desire to terminate negative emotion and adversity. Most of the factors somewhat provide partial elucidation on the engagement of the respondents on emotion-focused coping.

Keywords: coping responses subtypes, appraisal, teacher stress, ideal and actual coping

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

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Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Saulutė Juzelėnienė, Skirmantė Šarkauskienė

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In this paper, it is aimed to analyse the multimodal representations of the concepts of LOVE and PASSION in Lithuanian graphic novel “Gertrūda”, by Gerda Jord. The research is based on the earlier findings by Forceville (2005), Eerden (2009) as well as insights made by Shihara and Matsunaka (2009) and Kövecses (2000). The domains of target and source of LOVE and PASSION metaphors in comics are expressed by verbal and non-verbal cues. The analysis of non-verbal cues adopts the concepts of rune and indexes. A pictorial rune is a graphic representation of an object that does not exist in reality in comics, such as lines, dashes, text "balloons", and pictorial index – a graphically represented object of reality, a real symptom expressing a certain emotion, such as a wide smile, furrowed eyebrows, etc. Indexes are often hyperbolized in comics. The research revealed that most frequent source domains are CLOSINESS/UNITY, NATURAL/ PHYSICAL FORCE, VALUABLE OBJECT, PRESSURE. The target is the emotion of LOVE/PASSION which belongs to a more abstract domain of psychological experience. In this kind of metaphor, the picture can be interpreted as representing the emotion of happiness. Data are taken from Lithuanian comic books and Internet sites, where comics have been presented. The data and the analysis we are providing in this article aims to reveal that there are pictorial metaphors that manifest conceptual metaphors that are also expressed verbally and that methodological framework constructed for the analysis in the papers by Forceville at all is applicable to other emotions and culture specific pictorial manifestations.

Keywords: multimodal metaphor, conceptual metaphor, comics, graphic novel, concept of love/passion

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Authors: Sakda Loetpiparwanich, Preecha Vichitthamaros

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Nowadays, poor data quality is considered one of the majority costs for a data project. The data project with data quality awareness almost as much time to data quality processes while data project without data quality awareness negatively impacts financial resources, efficiency, productivity, and credibility. One of the processes that take a long time is defining the expectations and measurements of data quality because the expectation is different up to the purpose of each data project. Especially, big data project that maybe involves with many datasets and stakeholders, that take a long time to discuss and define quality expectations and measurements. Therefore, this study aimed at developing meaningful indicators to describe overall data quality for each dataset to quick comparison and priority. The objectives of this study were to: (1) Develop a practical data quality indicators and measurements, (2) Develop data quality dimensions based on statistical characteristics and (3) Develop Composite Indicator that can describe overall data quality for each dataset. The sample consisted of more than 500 datasets from public sources obtained by random sampling. After datasets were collected, there are five steps to develop the Dataset Quality Index (SDQI). First, we define standard data quality expectations. Second, we find any indicators that can measure directly to data within datasets. Thirdly, each indicator aggregates to dimension using factor analysis. Next, the indicators and dimensions were weighted by an effort for data preparing process and usability. Finally, the dimensions aggregate to Composite Indicator. The results of these analyses showed that: (1) The developed useful indicators and measurements contained ten indicators. (2) the developed data quality dimension based on statistical characteristics, we found that ten indicators can be reduced to 4 dimensions. (3) The developed Composite Indicator, we found that the SDQI can describe overall datasets quality of each dataset and can separate into 3 Level as Good Quality, Acceptable Quality, and Poor Quality. The conclusion, the SDQI provide an overall description of data quality within datasets and meaningful composition. We can use SQDI to assess for all data in the data project, effort estimation, and priority. The SDQI also work well with Agile Method by using SDQI to assessment in the first sprint. After passing the initial evaluation, we can add more specific data quality indicators into the next sprint.

Keywords: data quality, dataset quality, data quality management, composite indicator, factor analysis, principal component analysis

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Authors: Sheng Fu, Yinbo Gao, Hao Lin

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In the current mine air compressors monitoring system, there are some difficulties in the installation and maintenance because of the wired connection. To solve the problem, this paper introduces a new air compressors monitoring system based on ZigBee in which the monitoring parameters are transmitted wirelessly. The collecting devices are designed to form a cluster network to collect vibration, temperature, and pressure of air cylinders and other parameters. All these devices are battery-powered. Besides, the monitoring software in PC is developed using MFC. Experiments show that the designed wireless sensor network works well in the site environmental condition and the system is very convenient to be installed since the wireless connection. This monitoring system will have a wide application prospect in the upgrade of the old monitoring system of the air compressors.

Keywords: condition monitoring, wireless sensor network, air compressor, zigbee, data collecting

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Authors: Zahra Fotovatnia, Jeffery A. Jones, Alexandra Gottardo

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Speech communication often occurs in environments where noise conceals part of a message. Listeners should compensate for the lack of auditory information by picking up distinct acoustic cues and using semantic and sentential context to recreate the speaker’s intended message. This situation seems to be more challenging in a nonnative than native language. On the other hand, early bilinguals are expected to show an advantage over the late bilingual and monolingual speakers of a language due to their better executive functioning components. In this study, English monolingual speakers were compared with early and late nonnative speakers of English to understand speech in noise processing (SIN) and the underlying neurobiological features of this phenomenon. Auditory mismatch negativities (MMNs) were recorded using a double-oddball paradigm in response to a minimal pair that differed in their middle vowel (beat/bit) at Wilfrid Laurier University in Ontario, Canada. The results did not show any significant structural and electroneural differences across groups. However, vocabulary knowledge correlated positively with performance on tests that measured SIN processing in participants who learned English after age 6. Moreover, their performance on the test negatively correlated with the integral area amplitudes in the left superior temporal gyrus (STG). In addition, the STG was engaged before the inferior frontal gyrus (IFG) in noise-free and low-noise test conditions in all groups. We infer that the pre-attentive processing of words engages temporal lobes earlier than the fronto-central areas and that vocabulary knowledge helps the nonnative perception of degraded speech.

Keywords: degraded speech perception, event-related brain potentials, mismatch negativities, brain regions

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Authors: Ghazal Faraj, Andras Micsik

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The CIDOC Conceptual Reference Model (CRM) is an extensible ontology that provides integrated access to heterogeneous and digital datasets. The CIDOC-CRM offers a “semantic glue” intended to promote accessibility to several diverse and dispersed sources of cultural heritage data. That is achieved by providing a formal structure for the implicit and explicit concepts and their relationships in the cultural heritage field. The COURAGE (“Cultural Opposition – Understanding the CultuRal HeritAGE of Dissent in the Former Socialist Countries”) project aimed to explore methods about socialist-era cultural resistance during 1950-1990 and planned to serve as a basis for further narratives and digital humanities (DH) research. This project highlights the diversity of flourished alternative cultural scenes in Eastern Europe before 1989. Moreover, the dataset of COURAGE is an online RDF-based registry that consists of historical people, organizations, collections, and featured items. For increasing the inter-links between different datasets and retrieving more relevant data from various data silos, a shared federated ontology for reconciled data is needed. As a first step towards these goals, a full understanding of the CIDOC CRM ontology (target ontology), as well as the COURAGE dataset, was required to start the work. Subsequently, the queries toward the ontology were determined, and a table of equivalent properties from COURAGE and CIDOC CRM was created. The structural diagrams that clarify the mapping process and construct queries are on progress to map person, organization, and collection entities to the ontology. Through mapping the COURAGE dataset to CIDOC-CRM ontology, the dataset will have a common ontological foundation with several other datasets. Therefore, the expected results are: 1) retrieving more detailed data about existing entities, 2) retrieving new entities’ data, 3) aligning COURAGE dataset to a standard vocabulary, 4) running distributed SPARQL queries over several CIDOC-CRM datasets and testing the potentials of distributed query answering using SPARQL. The next plan is to map CIDOC-CRM to other upper-level ontologies or large datasets (e.g., DBpedia, Wikidata), and address similar questions on a wide variety of knowledge bases.

Keywords: CIDOC CRM, cultural heritage data, COURAGE dataset, ontology alignment

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Authors: Mara J. Richman, Zsolt Unoka, Robert Dudas, Zsolt Demetrovics

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Borderline personality disorder (BPD) is characterized by deficits in emotion regulation and effective liability. Of this domain, ruminative behaviors have been considered a core feature of emotion dysregulation difficulties. Taking this into consideration, a meta-analysis was performed to assess how BPD symptoms correlate with rumination, while also considering clinical moderator variables such as comorbidity, GAF score, and type of BPD symptom and demographic moderator variables such as age, gender, and education level. Analysis of correlation across rumination domains for the entire sample revealed a medium overall correlation. When assessing types of rumination, the largest correlation was among pain rumination followed by anger, depressive, and anxious rumination. Furthermore, affective instability had the strongest correlation with increased rumination, followed by unstable relationships, identity disturbance, and self-harm/ impulsivity, respectively. Demographic variables showed no significance. Clinical implications are considered and further therapeutic interventions are discussed in the context of rumination.

Keywords: borderline personality disorder, meta-analysis, rumination, symptoms

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Authors: Joy Aworo-Okoroh

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Literary texts are mirrors of a real-life situation. Thus, authors choose the linguistic items that would best encode their intended meanings and messages. However, words mean more than they seem. The meaning of words is not static rather, it is dynamic as they constantly enter into relationships within a context. Literary texts can only be meaningful if all pragmatic cues are identified and interpreted. Drawing upon Teun Van Djik's theory of local pragmatic coherence, it is established that words enter into relations in a text and these relations account for sequential speech acts in the texts. Comprehension of the text is dependent on the interpretation of these relations.To show the relevance of pragmatic coherence in literary text analysis, ten conversations were selected in Americanah in order to give a clear idea of the pragmatic relations used. The conversations were analysed, identifying the speech act and epistemic relations inherent in them. A subtle analysis of the structure of the conversations was also carried out. It was discovered that justification is the most commonly used relation and the meaning of the text is dependent on the interpretation of these instances' pragmatic coherence. The study concludes that to effectively teach literature in English, pragmatic coherence should be incorporated as words mean more than they say.

Keywords: pragmatic coherence, epistemic coherence, speech act, Americanah

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Authors: Tiago do Carmo Nogueira, Cássio Dener Noronha Vinhal, Gélson da Cruz Júnior, Matheus Rudolfo Diedrich Ullmann

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Generating automatic image descriptions through natural language is a challenging task. Image captioning is a task that consistently describes an image by combining computer vision and natural language processing techniques. To accomplish this task, cutting-edge models use encoder-decoder structures. Thus, Convolutional Neural Networks (CNN) are used to extract the characteristics of the images, and Recurrent Neural Networks (RNN) generate the descriptive sentences of the images. However, cutting-edge approaches still suffer from problems of generating incorrect captions and accumulating errors in the decoders. To solve this problem, we propose a model based on the encoder-decoder structure, introducing a module that generates the weights according to the importance of the word to form the sentence, using the part-of-speech (PoS). Thus, the results demonstrate that our model surpasses state-of-the-art models.

Keywords: gated recurrent units, caption generation, convolutional neural network, part-of-speech

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Authors: Bharatendra Rai

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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

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