Search results for: speech emotion classification

Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Jiameng Wang, Yunfei Yin, Xiyu Deng

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Parallel optimization is one of the important research topics of data mining at this stage. Taking Classification and Regression Tree (CART) parallelization as an example, this paper proposes a parallel data mining algorithm based on SSP-OGini-PCCP. Aiming at the problem of choosing the best CART segmentation point, this paper designs an S-SP model without data association; and in order to calculate the Gini index efficiently, a parallel OGini calculation method is designed. In addition, in order to improve the efficiency of the pruning algorithm, a synchronous PCCP pruning strategy is proposed in this paper. In this paper, the optimal segmentation calculation, Gini index calculation, and pruning algorithm are studied in depth. These are important components of parallel data mining. By constructing a distributed cluster simulation system based on SPARK, data mining methods based on SSP-OGini-PCCP are tested. Experimental results show that this method can increase the search efficiency of the best segmentation point by an average of 89%, increase the search efficiency of the Gini segmentation index by 3853%, and increase the pruning efficiency by 146% on average; and as the size of the data set increases, the performance of the algorithm remains stable, which meets the requirements of contemporary massive data processing.

Keywords: classification, Gini index, parallel data mining, pruning ahead

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Authors: Neda Orak, Mostafa Zarei

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Environmental assessment is an important session in environment management. Since various methods and techniques have been produces and implemented. Remote sensing (RS) is widely used in many scientific and research fields such as geology, cartography, geography, agriculture, forestry, land use planning, environment, etc. It can show earth surface objects cyclical changes. Also, it can show earth phenomena limits on basis of electromagnetic reflectance changes and deviations records. The research has been done on mangrove forests assessment by RS techniques. Mangrove forests quantitative analysis in Basatin and Bidkhoon estuaries was the aim of this research. It has been done by Landsat satellite images from 1975- 2013 and match to ground control points. This part of mangroves are the last distribution in northern hemisphere. It can provide a good background to improve better management on this important ecosystem. Landsat has provided valuable images to earth changes detection to researchers. This research has used MSS, TM, +ETM, OLI sensors from 1975, 1990, 2000, 2003-2013. Changes had been studied after essential corrections such as fix errors, bands combination, georeferencing on 2012 images as basic image, by maximum likelihood and IPVI Index. It was done by supervised classification. 2004 google earth image and ground points by GPS (2010-2012) was used to compare satellite images obtained changes. Results showed mangrove area in bidkhoon was 1119072 m2 by GPS and 1231200 m2 by maximum likelihood supervised classification and 1317600 m2 by IPVI in 2012. Basatin areas is respectively: 466644 m2, 88200 m2, 63000 m2. Final results show forests have been declined naturally. It is due to human activities in Basatin. The defect was offset by planting in many years. Although the trend has been declining in recent years again. So, it mentioned satellite images have high ability to estimation all environmental processes. This research showed high correlation between images and indexes such as IPVI and NDVI with ground control points.

Keywords: IPVI index, Landsat sensor, maximum likelihood supervised classification, Nayband National Park

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Authors: Tracie Catlett

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Students enrolled in high-achieving schools are under tremendous pressure to perform at high levels inside and outside the classroom. Achievement pressure is a prevalent source of stress for students enrolled in high-achieving schools, and female students in particular experience a higher frequency and higher levels of stress compared to their male peers. The practice of mindfulness in a school setting is one tool that has been linked to improved self-regulation of emotions, increased positive emotions, and stress reduction. A mixed methods randomized pretest-posttest no-treatment control trial, evaluated the effects of a six-session mindfulness intervention taught during a regularly scheduled life skills period in an independent day school, one type of high-achieving school. Twenty-nine students in Grades 10 and 11 were randomized by class where Grade 11 students were in the intervention group (n = 14) and Grade 10 students were in the control group (n = 15). Findings from the study produced mixed results. There was no evidence that the mindfulness program reduced participants’ stress levels and negative emotions. In fact, contrary to what was expected, students enrolled in the intervention group experienced higher levels of stress and increased negative emotions at posttreatment when compared to pretreatment. Neither the within-group nor the between-groups changes in stress level were statistically significant, p > .05, and the between-groups effect size was small, d = .2. The study found evidence that the mindfulness program may have had a positive impact on students’ ability to regulate their emotions. The within-group comparison and the between-groups comparison at posttreatment found that students in the mindfulness course experienced statistically significant improvement in the in their ability to regulate their emotions at posttreatment, p = .009 < .05 and p =. 034 < .05, respectively. The between-groups effect size was medium, d =.7, suggesting that the positive differences in emotion regulation difficulties were substantial and have practical implications. The analysis of gender differences as they relate to stress and emotions revealed that female students perceive higher levels of stress and report experiencing stress more often than males. There were no gender differences when analyzing sources of stress experienced by the student participants. Both females and males experience regular achievement pressures related to their school performance and worry about their future, college acceptance, grades, and parental expectations. Females reported an increased awareness of their stress and actively engaged in practicing mindfulness to manage their stress. Students in the treatment group expressed that the practice of mindfulness resulted in feelings of relaxation and calmness.

Keywords: achievement pressure, adolescents, emotion regulation, emotions, high-achieving schools, independent schools, mindfulness, negative affect, positive affect, stress

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Authors: Benjamin Beltzung, Marie Pelé, Julien P. Renoult, Cédric Sueur

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To this day, it is not known if drawing is specifically human behavior or if this behavior finds its origins in ancestor species. An interesting window to enlighten this question is to analyze the drawing behavior in genetically close to human species, such as non-human primate species. A good candidate for this approach is the orangutan, who shares 97% of our genes and exhibits multiple human-like behaviors. Focusing on figurative aspects may not be suitable for orangutans’ drawings, which may appear as scribbles but may have meaning. A manual feature selection would lead to an anthropocentric bias, as the features selected by humans may not match with those relevant for orangutans. In the present study, we used deep learning to analyze the drawings of a female orangutan named Molly († in 2011), who has produced 1,299 drawings in her last five years as part of a behavioral enrichment program at the Tama Zoo in Japan. We investigate multiple ways to decipher Molly’s drawings. First, we demonstrate the existence of differences between seasons by training a deep learning model to classify Molly’s drawings according to the seasons. Then, to understand and interpret these seasonal differences, we analyze how the information spreads within the network, from shallow to deep layers, where early layers encode simple local features and deep layers encode more complex and global information. More precisely, we investigate the impact of feature complexity on classification accuracy through features extraction fed to a Support Vector Machine. Last, we leverage style transfer to dissociate features associated with drawing style from those describing the representational content and analyze the relative importance of these two types of features in explaining seasonal variation. Content features were relevant for the classification, showing the presence of meaning in these non-figurative drawings and the ability of deep learning to decipher these differences. The style of the drawings was also relevant, as style features encoded enough information to have a classification better than random. The accuracy of style features was higher for deeper layers, demonstrating and highlighting the variation of style between seasons in Molly’s drawings. Through this study, we demonstrate how deep learning can help at finding meanings in non-figurative drawings and interpret these differences.

Keywords: cognition, deep learning, drawing behavior, interpretability

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Authors: Marrone Silverio Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner, Djamel Fawzi Hadj Sadok

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The creation of a dataset is time-consuming and often discourages researchers from pursuing their goals. To overcome this problem, we present and discuss two solutions adopted for the automation of this process. Both optimize valuable user time and resources and support video object segmentation with object tracking and 3D projection. In our scenario, we acquire images from a moving robotic arm and, for each approach, generate distinct annotated datasets. We evaluated the precision of the annotations by comparing these with a manually annotated dataset, as well as the efficiency in the context of detection and classification problems. For detection support, we used YOLO and obtained for the projection dataset an F1-Score, accuracy, and mAP values of 0.846, 0.924, and 0.875, respectively. Concerning the tracking dataset, we achieved an F1-Score of 0.861, an accuracy of 0.932, whereas mAP reached 0.894. In order to evaluate the quality of the annotated images used for classification problems, we employed deep learning architectures. We adopted metrics accuracy and F1-Score, for VGG, DenseNet, MobileNet, Inception, and ResNet. The VGG architecture outperformed the others for both projection and tracking datasets. It reached an accuracy and F1-score of 0.997 and 0.993, respectively. Similarly, for the tracking dataset, it achieved an accuracy of 0.991 and an F1-Score of 0.981.

Keywords: RJ45, automatic annotation, object tracking, 3D projection

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Authors: Patsy Tan, Dana Baltutis

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This presentation demonstrates the effectiveness of music therapy in co-treatment with speech pathology and occupational therapy as an innovative way when working with children and adolescents with complex individual differences to facilitate communication, emotional, motor and social skills development. Each child with special needs and their carer has an individual profile which encompasses their visual-spatial, auditory, language, learning, mental health, family dynamic, sensory-motor, motor planning and sequencing profiles. The most common issues among children with special needs, especially those diagnosed with Autism Spectrum Disorder, are in the areas of regulation, communication, and social-emotional development. The ability of children living with challenges to communicate and use language and understand verbal and non-verbal information, as well as move their bodies to explore and interact with their environments in social situations, depends on the children being regulated both internally and externally and trusting their communication partners and understanding what is happening in the moment. For carers, it is about understanding the tempo, rhythm, pacing, and timing of their own individual profile, as well as the profile of the child they are interacting with, and how these can sync together. In this study, music therapy is used in co-treatment sessions with a speech pathologist and/or an occupational therapist using the DIRFloortime approach to facilitate the regulation, attention, engagement, reciprocity and social-emotional capacities of children presenting with complex individual differences. Documented changes in 10 domains of children’s development over a 12-month period using the Individual Music Therapy Assessment Profile (IMTAP) were observed. Children were assessed biannually, and results show significant improvements in the social-emotional, musicality and receptive language domains indicating that co-treatment with a music therapist using the DIRFloortime framework is highly effective. This presentation will highlight strategies that facilitate regulation, social-emotional and communication development for children and adolescents with complex individual profiles.

Keywords: communication, shared attention, regulation, social emotional

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Authors: Mohd Yusri Ibrahim

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Although it is known that interpersonal skills are essential for personal development, the debate however continues as to how to measure those skills, especially in youths. This study was conducted to develop a measurement model of interpersonal skills by suggesting three construct namely personal, skills and relationship; six function namely self, perception, listening, conversation, emotion and conflict management; and 30 behaviours as indicators. This cross-sectional survey by questionnaires was applied in east side of peninsula of Malaysia for 150 respondents, and analyzed by structural equation modelling (SEM) by AMOS. The suggested constructs, functions and indicators were consider accepted as measurement elements by observing on regression weight for standard loading, average variance extracted (AVE) for convergent validity, square root of AVE for discriminant validity, composite reliability (CR), and at least three fit indexes for model fitness. Finally, a measurement model of interpersonal skill for youth was successfully developed.

Keywords: interpersonal communication, interpersonal skill, youth, communication skill

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Authors: Vishal Raj, Noorhan Abbas

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Ambiguity in NLP (Natural language processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential am-biguities. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a novel method to create an affinity matrix to calculate the affinity be-tween any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an al-gorithm to create the sense clusters of tokens using affinity matrix under hierar-chy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contex-tual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and chal-lenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model.

Keywords: word sense disambiguation (wsd), contextual sense model (csm), most frequent sense (mfs), part of speech (pos), natural language processing (nlp), oov (out of vocabulary), lemma_pos (a token where lemma and pos of word are joined by underscore), information retrieval (ir), machine translation (mt)

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Authors: M. Eckert, D. Mendes, J P. Gonçalves, C. Moço, M. Oliveira

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The intensive extraction of natural aggregates leads to both depletion of natural resources and unwanted environmental impacts. On the other hand, uncontrolled disposal of Construction and Demolition Wastes (C&DW) causes the lifetime reduction of landfills. It is known that the European Union produces, each year, about 850 million tons of C&DW. For all the member States of the European Union, one of the milestones to be reached by 2020, according to the Resource Efficiency Roadmap (COM (2011) 571) of the European Commission, is to recycle 70% of the C&DW. In this work, properties of different types of recycled C&DW aggregates and natural aggregates were compared. Assays were performed according to European Standards (EN 13285; EN 13242+A1; EN 12457-4; EN 12620; EN 13139) for the characterization of there: physical, mechanical and chemical properties. Not standardized tests such as water absorption over time, mass stability and post compaction sieve analysis were also carried out. The tested recycled C&DW aggregates were classified according to the requirements of the European Standards regarding there potential use in concrete, mortar, unbound layers of road pavements and embankments. The results of the physical and mechanical properties of recycled C&DW aggregates indicated, in general, lower quality properties when compared to natural aggregates, particularly, for concrete preparation and unbound layers of road pavements. The results of the chemical properties attested that the C&DW aggregates constitute no environmental risk. It was concluded that recycled aggregates produced from C&DW have the potential to be used in many applications.

Keywords: recycled aggregate, sustainability, aggregate properties, European Standard Classification

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Authors: David Simpson, Kyle Nash

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There is considerable evidence linking slow, deliberative reasoning (system 2) with utilitarian judgments in dilemmas involving the sacrificing of another person for the greater good (other-sacrificial dilemmas). Joshua Greene has argued, based on this kind of evidence, that system 2 drives utilitarian judgments. However, the evidence on whether system 2 is associated with utilitarian judgments in self-sacrificial dilemmas is more mixed. We employed process dissociation to measure a self-sacrificial utilitarian (SU) parameter and an other-sacrificial (OU) utilitarian parameter. It was initially predicted that contra Greene, the cognitive reflection test (CRT) would only be positively correlated with the OU parameter and not the SU parameter. However, Greene’s hypothesis was corroborated: the CRT positively correlated with both the OU parameter and the SU parameter. By contrast, the CRT did not correlate with the other two moral parameters we extracted (altruism and deontology).

Keywords: dual-process model, utilitarianism, altruism, reason, emotion, process dissociation

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Authors: Kwan Hee Han

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In today’s business environments, frequent change of customer requirements is a tough challenge to manufacturing company. To cope with these challenges, a production planning and scheduling (PP&S) function might be established to provide accountability for both customer service and operational efficiency. Nowadays, many manufacturing firms have utilized PP&S software solutions to generate a realistic production plan and schedule to adapt to external changes efficiently. However, companies which consider the introduction of PP&S software solution, still have difficulties for selecting adequate solution to meet their specific needs. Since the task of PP&S is the one of major building blocks of SCM (Supply Chain Management) architecture, which deals with short term decision making in the production process of SCM, it is needed that the functionalities of PP&S should be analysed within the whole SCM process. The aim of this paper is to analyse the PP&S functionalities and its system architecture from the SCM perspective by using the criteria of level of planning hierarchy, major 4 SCM processes and problem-solving approaches, and finally propose a classification framework of PP&S solutions to facilitate the comparison among various commercial software solutions. By using proposed framework, several major PP&S solutions are classified and positioned according to their functional characteristics in this paper. By using this framework, practitioners who consider the introduction of computerized PP&S solutions in manufacturing firms can prepare evaluation and benchmarking sheets for selecting the most suitable solution with ease and in less time.

Keywords: production planning, production scheduling, supply chain management, the advanced planning system

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Authors: Ibtissem Daoudi, Raoudha Chebil, Wided Lejouad Chaari

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Serious games constitute actually a recent and attractive way supposed to replace the classical boring courses. However, the choice of the adapted serious game to a specific learning environment remains a challenging task that makes teachers unwilling to adopt this concept. To fill this gap, we present, in this paper, a multi-agent-based simulator allowing to predict the impact of a serious game integration in a learning environment given several game and players characteristics. As results, the presented tool gives intensities of several emotional aspects characterizing learners reactions to the serious game adoption. The presented simulator is tested to predict the effect of basing a coding course on the serious game ”CodeCombat”. The obtained results are compared with feedbacks of using the same serious game in a real learning process.

Keywords: emotion, learning process, multi-agent simulation, serious games

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Authors: Ilma Robo, Saimir Heta, Vera Ostreni, Elsaida Agrushi, Eduart Kapaj

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Introduction: Cyclic hormonal fluctuations are known from literature to have a clinically visible effects on gingival tissue reactions, to the diagnosed processes of gingival inflammation. Materials and methods: At a total of 47 female patients, ad-hock presented at the University Clinic, were recorded data on effect of hormonal oscillations at periodontal treatment protocol. Oral examination was performed on soft tissue of gingiva and the oral mucous membrane, always respecting the air-drying procedure and then checking with free eye differences in oral mucosal relief. After the patients were informed about the study protocol, the purpose of the study and the ongoing procedure, verbal consensus was required. Results: The study was conducted in a total of 47 patients, out of which 13 patients were under the gingivitis classification, and 24 patients under the periodontal classification. Patients included in the study are divided by age, cycle week respectively 1,2,3 and 4.The younger age of female patients is more prone to the appearance of gingivitis, which is further aggravated by the effects of sexual hormones and the effect of the controlled or non-regulated fluctuations of the latter. Conclusions: The healing process is more fuel-intensive in the absence of high hormone levels, as they are these pro-inflammatory hormones, both in or near the ho Younger women are more open to volunteering in studies that record individual and study data that may last in time.

Keywords: gingiva, hormonal oscillations, female patients, mucosa, periodontal non-surgical treatment

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Authors: Fernanda A. P. Peres, Thiago N. Peres, Flavio S. Fogliatto, Michel J. Anzanello

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Batch processes are widely used in food industry and have an important role in the production of high added value products, such as chocolate. Process performance is usually described by variables that are monitored as the batch progresses. Data arising from these processes are likely to display a strong correlation-autocorrelation structure, and are usually monitored using control charts based on multiway principal components analysis (MPCA). Process control of a new batch is carried out comparing the trajectories of its relevant process variables with those in a reference set of batches that yielded products within specifications; it is clear that proper determination of the reference set is key for the success of a correct signalization of non-conforming batches in such quality control schemes. In chocolate manufacturing, misclassifications of non-conforming batches in the conching phase may lead to significant financial losses. In such context, the accuracy of process control grows in relevance. In addition to that, the main assumption in MPCA-based monitoring strategies is that all batches are synchronized in duration, both the new batch being monitored and those in the reference set. Such assumption is often not satisfied in chocolate manufacturing process. As a consequence, traditional techniques as MPCA-based charts are not suitable for process control and monitoring. To address that issue, the objective of this work is to compare the performance of three dynamic time warping (DTW) methods in the alignment and synchronization of chocolate conching process variables’ trajectories, aimed at properly determining the reference distribution for multivariate statistical process control. The power of classification of batches in two categories (conforming and non-conforming) was evaluated using the k-nearest neighbor (KNN) algorithm. Real data from a milk chocolate conching process was collected and the following variables were monitored over time: frequency of soybean lecithin dosage, rotation speed of the shovels, current of the main motor of the conche, and chocolate temperature. A set of 62 batches with durations between 495 and 1,170 minutes was considered; 53% of the batches were known to be conforming based on lab test results and experts’ evaluations. Results showed that all three DTW methods tested were able to align and synchronize the conching dataset. However, synchronized datasets obtained from these methods performed differently when inputted in the KNN classification algorithm. Kassidas, MacGregor and Taylor’s (named KMT) method was deemed the best DTW method for aligning and synchronizing a milk chocolate conching dataset, presenting 93.7% accuracy, 97.2% sensitivity and 90.3% specificity in batch classification, being considered the best option to determine the reference set for the milk chocolate dataset. Such method was recommended due to the lowest number of iterations required to achieve convergence and highest average accuracy in the testing portion using the KNN classification technique.

Keywords: batch process monitoring, chocolate conching, dynamic time warping, reference set distribution, variable duration

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Authors: Vladimir A. Todiras

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Spatio-temporal models provide new possibilities for real-time action in pest risk analysis. It should be noted that estimation of the possibility and probability of introduction of a pest and of its economic consequences involves many uncertainties. We present a new mapping technique that assesses pest invasion risk using online BioClass software. BioClass is a GIS tool designed to solve multiple-criteria classification and optimization problems based on fuzzy logic and level set methods. This research describes a method for predicting the potential establishment and spread of a plant pest into new areas using a case study: corn rootworm (Diabrotica spp.), tomato leaf miner (Tuta absoluta) and plum fruit moth (Grapholita funebrana). Our study demonstrated that in BioClass we can combine fuzzy logic and geographic information systems with knowledge of pest biology and environmental data to derive new information for decision making. Pests are sensitive to a warming climate, as temperature greatly affects their survival and reproductive rate and capacity. Changes have been observed in the distribution, frequency and severity of outbreaks of Helicoverpa armigera on tomato. BioClass has demonstrated to be a powerful tool for applying dynamic models and map the potential future distribution of a species, enable resource to make decisions about dangerous and invasive species management and control.

Keywords: classification, model, pest, risk

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Authors: Gubair Tarabeh, Ghadir Zriek, David Oppenheim, Avi Sagi-Schwartz, Nina Koren-Karie

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Introduction: Mind-Mindedness (MM) focuses on mothers' attunement to their infant's mental states as reflected in their speech to the infant. Appropriate MM comments are associated with attachment security in individualistic Western societies where parents value their children’s autonomy and independence, and may therefore be more likely to engage in mind-related discourse with their children that highlights individual thoughts, preferences, emotions, and motivations. Such discourse may begin in early infancy, even before infants are likely to understand the semantic meaning of parental speech. Parents in collectivistic societies, by contrast, are thought to emphasize conforming to social norms more than individual goals, and this may lead to parent-child discourse that emphasizes appropriate behavior and compliance with social norms rather than internal mental states of the self and the other. Therefore, the examination of maternal MM and its relationship with attachment in Arab collectivistic culture in Israel was of particular interest. Aims of the study: The goal of the study was to examine whether the associations between MM and attachment in the Arab culture in Israel are the same as in Western samples. An additional goal was to examine whether appropriate and non-attuned MM comments could, together, distinguish among mothers of children in the different attachment classifications. Material and Methods: 76 Arab mothers and their infants between the ages of 12 and 18 months were observed in the Strange Situation Procedure (49 secure (B), 11 ambivalent (C), 14 disorganized (D), and 2 avoidant (A) infants). MM was coded from an 8-minute free-play sequence. Results: Mothers of B infants used more appropriate and less non-attuned MM comments than mothers of D infants, with no significant differences with mothers of C infants. Also, mothers of B infants used less non-attuned MM comments than both mothers of D infants and mothers of C infants. In addition, Mothers of B infants were most likely to show the combination of high appropriate and low non-attuned MM comments; Mothers of D infants were most likely to show the combination of high non-attuned and low appropriate MM comments; and a non-significant trend indicated that mothers of C infants were most likely to show a combination of high appropriate and high non-attuned MM comments. Conclusion: Maternal MM was associated with attachment in the Arab culture in Israel with combinations of appropriate and non-attuned MM comments distinguishing between different attachment classifications.

Keywords: attachment, maternal mind-mindedness, Arab culture, collectivistic culture

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Authors: Paul Licina, Emma M. Johnston, David Lisle, Mark Young, Chris Brady

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Background: Lumbar pars pathology is a common cause of pain in the growing spine. It can be seen in young athletes participating in at-risk sports and can affect sporting performance and long-term health due to its resistance to traditional management. There is a current lack of consensus of classification and treatment for pars injuries. Previous systems used CT to stage pars defects but could not assess early stress reactions. A modified classification is proposed that considers findings on MRI, significantly improving early treatment guidance. The treatment protocol is designed for patients aged 5 to 19 years. Method: Clinical screening identifies patients with a low, medium, or high index of suspicion for lumbar pars injury using patient age, sport participation and pain characteristics. MRI of the at-risk cohort enables augmentation of existing CT-based classification while avoiding ionising radiation. Patients are classified into five categories based on MRI findings. A type 0 lesion (stress reaction) is present when CT is normal and MRI shows high signal change (HSC) in the pars/pedicle on T2 images. A type 1 lesion represents the ‘early defect’ CT classification. The group previously referred to as a 'progressive stage' defect on CT can be split into 2A and 2B categories. 2As have HSC on MRI, whereas 2Bs do not. This distinction is important with regard to healing potential. Type 3 lesions are terminal stage defects on CT, characterised by pseudarthrosis. MRI shows no HSC. Results: Stress reactions (type 0) and acute fractures (1 and 2a) can heal and are treated in a custom-made hard brace for 12 weeks. It is initially worn 23 hours per day. At three weeks, patients commence basic core rehabilitation. At six weeks, in the absence of pain, the brace is removed for sleeping. Exercises are progressed to positions of daily living. Patients with continued pain remain braced 23 hours per day without exercise progression until becoming symptom-free. At nine weeks, patients commence supervised exercises out of the brace for 30 minutes each day. This allows them to re-learn muscular control without rigid support of the brace. At 12 weeks, bracing ceases and MRI is repeated. For patients with near or complete resolution of bony oedema and healing of any cortical defect, rehabilitation is focused on strength and conditioning and sport-specific exercise for the full return to activity. The length of this final stage is approximately nine weeks but depends on factors such as development and level of sports participation. If significant HSC remains on MRI, CT scan is considered to definitively assess cortical defect healing. For these patients, return to high-risk sports is delayed for up to three months. Chronic defects (2b and 3) cannot heal and are not braced, and rehabilitation follows traditional protocols. Conclusion: Appropriate clinical screening and imaging with MRI can identify pars pathology early. In those with potential for healing, we propose hard bracing and appropriate rehabilitation as part of a multidisciplinary management protocol. The validity of this protocol will be tested in future studies.

Keywords: adolescents, MRI classification, pars interticularis, treatment protocol

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Piotr Prewysz-Kwinto, Grazyna Voss

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In June 2013, the European Parliament and the Council adopted a directive on financial reporting (Directive 2013/34/EU). The main objective was to simplify the principles of the preparation of financial statements, including the principles of the presentation and disclosures of financial information by adapting reporting burdens to the type and size of an undertaking. Therefore, the Directive introduced a classification of all undertakings into five groups, i.e. micro, small, medium-sized, large and public-interest entities, and defined in detail the classification criteria. The principles of the preparation of financial statements and the presentation of financial information as well as applicable simplifications were defined for each group. The EU Member States had to implement the provisions of Directive 34 relating to accounting and financial reporting into domestic norms until January 1, 2016. In Poland, the provisions of Directive 34 were implemented into domestic accounting norms specified in the Polish Accounting Act on a gradual basis. On July 11, 2014, the Polish Parliament adopted an amendment to the Act, introducing the Directive's solutions for micro-undertakings and on July 23, 2015, for the remaining undertakings. The aim of this paper is to present Polish solutions relating to financial reporting after the implementation of Directive 34 and the results of the survey conducted among accountants regarding the evaluation of the implemented simplifications for micro and small undertakings.

Keywords: accounting standards, financial reporting, financial statement, simplification

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Authors: Ahmad Shahin, Walid Moudani, Ali Bekraki

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This research aims at presenting a predictive data mining model to handle an accurate diagnosis of acute appendicitis with patients for the purpose of maximizing the health service quality, minimizing morbidity/mortality, and reducing cost. However, acute appendicitis is the most common disease which requires timely accurate diagnosis and needs surgical intervention. Although the treatment of acute appendicitis is simple and straightforward, its diagnosis is still difficult because no single sign, symptom, laboratory or image examination accurately confirms the diagnosis of acute appendicitis in all cases. This contributes in increasing morbidity and negative appendectomy. In this study, the authors propose to generate an accurate model in prediction of patients with acute appendicitis which is based, firstly, on the segmentation technique associated to ABC algorithm to segment the patients; secondly, on applying fuzzy logic to process the massive volume of heterogeneous and noisy data (age, sex, fever, white blood cell, neutrophilia, CRP, urine, ultrasound, CT, appendectomy, etc.) in order to express knowledge and analyze the relationships among data in a comprehensive manner; and thirdly, on applying dynamic programming technique to reduce the number of data attributes. The proposed model is evaluated based on a set of benchmark techniques and even on a set of benchmark classification problems of osteoporosis, diabetes and heart obtained from the UCI data and other data sources.

Keywords: healthcare management, acute appendicitis, data mining, classification, decision tree

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Authors: Madhusudan Hamirwasia, Sarang Barbarwar, Arshleen Kaur

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The construction of memorials began thousands of years ago and the practice is still continuing. These memorials became a symbol to honor great people and events in the history. The aim of the study was to understand the evolution of memorials from an architectural design perspective. It is also concentrated on the similarities and differences between the memorials in Europe and those in the Indian subcontinent. The study shows how the design of a memorial has seen a considerable shift from the tribal Urasgattas to the contemporary commemorative structures. While they were somber symbolic gestures in the past, they have now transformed into a socio-cultural space in urban areas. Not only the memorials were inspired by the culture but the culture too got influenced by the memorials as with progressing time, they hold the vital link to our past. The study intends to encapsulate the essence of design elements in these memorials that convey the visitors the intangible messages held by the edifice in its tangible presence.

Keywords: evolution, emotion, memorials, symbolism

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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Authors: Esra Polat

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Partial Least Squares Discriminant Analysis (PLSDA) is a statistical method for classification and consists a classical Partial Least Squares Regression (PLSR) in which the dependent variable is a categorical one expressing the class membership of each observation. PLSDA can be applied in many cases when classical discriminant analysis cannot be applied. For example, when the number of observations is low and when the number of independent variables is high. When there are missing values, PLSDA can be applied on the data that is available. Finally, it is adapted when multicollinearity between independent variables is high. The aim of this study is to determine the economic and/or demographic indicators, which are effective in grouping the 28 European Union (EU) member countries and 7 candidate countries (including potential candidates Bosnia and Herzegovina (BiH) and Kosova) by using the data set obtained from database of the World Bank for 2014. Leaving the political issues aside, the analysis is only concerned with the economic and demographic variables that have the potential influence on country’s eligibility for EU entrance. Hence, in this study, both the performance of PLSDA method in classifying the countries correctly to their pre-defined groups (candidate or member) and the differences between the EU countries and candidate countries in terms of these indicators are analyzed. As a result of the PLSDA, the value of percentage correctness of 100 % indicates that overall of the 35 countries is classified correctly. Moreover, the most important variables that determine the statuses of member and candidate countries in terms of economic indicators are identified as 'external balance on goods and services (% GDP)', 'gross domestic savings (% GDP)' and 'gross national expenditure (% GDP)' that means for the 2014 economical structure of countries is the most important determinant of EU membership. Subsequently, the model validated to prove the predictive ability by using the data set for 2015. For prediction sample, %97,14 of the countries are correctly classified. An interesting result is obtained for only BiH, which is still a potential candidate for EU, predicted as a member of EU by using the indicators data set for 2015 as a prediction sample. Although BiH has made a significant transformation from a war-torn country to a semi-functional state, ethnic tensions, nationalistic rhetoric and political disagreements are still evident, which inhibit Bosnian progress towards the EU.

Keywords: classification, demographic indicators, economic indicators, European Union, partial least squares discriminant analysis

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Authors: Marden Silva, Danielle Guerra

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Some studies have indicated that pronunciation teaching hasn’t been paid enough attention by teachers regarding EFL contexts. In particular, segmental and suprasegmental features through genre-based approach may be an opportunity on how to integrate pronunciation into a more meaningful learning practice. Therefore, the aim of this project was to carry out a survey on some aspects related to English pronunciation that Brazilian students consider more difficult to learn, thus enabling the discussion of strategies that can facilitate the development of oral skills in English classes by integrating the teaching of phonetic-phonological aspects into the genre-based approach. Notions of intelligibility, fluency and accuracy were proposed by some authors as an ideal didactic sequence. According to their proposals, basic learners should be exposed to activities focused on the notion of intelligibility as well as intermediate students to the notion of fluency, and finally more advanced ones to accuracy practices. In order to test this hypothesis, data collection was conducted during three high school English classes at Federal Center for Technological Education of Minas Gerais (CEFET-MG), in Brazil, through questionnaires and didactic activities, which were recorded and transcribed for further analysis. The genre debate was chosen to facilitate the oral expression of the participants in a freer way, making them answering questions and giving their opinion about a previously selected topic. The findings indicated that basic students demonstrated more difficulty with aspects of English pronunciation than the others. Many of the intelligibility aspects analyzed had to be listened more than once for a better understanding. For intermediate students, the speeches recorded were considerably easier to understand, but nevertheless they found it more difficult to pronounce the words fluently, often interrupting their speech to think about what they were going to say and how they would talk. Lastly, more advanced learners seemed to express their ideas more fluently, but still subtle errors related to accuracy were perceptible in speech, thereby confirming the proposed hypothesis. It was also seen that using genre-based approach to promote oral communication in English classes might be a relevant method, considering the socio-communicative function inherent in the suggested approach.

Keywords: EFL, genre-based approach, oral skills, pronunciation

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Authors: Anoop T. R., Otman Basir, Robert F. Hess, Eileen E. Birch, Brooke A. Koritala, Reed M. Jost, Becky Luu, David Stager, Ben Thompson

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Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. We developed a two-stage method for strabismus detection and classification based on photographs of the face. The first stage detects the presence or absence of strabismus, and the second stage classifies the type of strabismus. The first stage comprises face detection using Haar cascade, facial landmark estimation, face alignment, aligned face landmark detection, segmentation of the eye region, and detection of strabismus using VGG 16 convolution neural networks. Face alignment transforms the face to a canonical pose to ensure consistency in subsequent analysis. Using facial landmarks, the eye region is segmented from the aligned face and fed into a VGG 16 CNN model, which has been trained to classify strabismus. The CNN determines whether strabismus is present and classifies the type of strabismus (exotropia, esotropia, and vertical deviation). If stage 1 detects strabismus, the eye region image is fed into stage 2, which starts with the estimation of pupil center coordinates using mask R-CNN deep neural networks. Then, the distance between the pupil coordinates and eye landmarks is calculated along with the angle that the pupil coordinates make with the horizontal and vertical axis. The distance and angle information is used to characterize the degree and direction of the strabismic eye misalignment. This model was tested on 100 clinically labeled images of children with (n = 50) and without (n = 50) strabismus. The True Positive Rate (TPR) and False Positive Rate (FPR) of the first stage were 94% and 6% respectively. The classification stage has produced a TPR of 94.73%, 94.44%, and 100% for esotropia, exotropia, and vertical deviations, respectively. This method also had an FPR of 5.26%, 5.55%, and 0% for esotropia, exotropia, and vertical deviation, respectively. The addition of one more feature related to the location of corneal light reflections may reduce the FPR, which was primarily due to children with pseudo-strabismus (the appearance of strabismus due to a wide nasal bridge or skin folds on the nasal side of the eyes).

Keywords: strabismus, deep neural networks, face detection, facial landmarks, face alignment, segmentation, VGG 16, mask R-CNN, pupil coordinates, angle deviation, horizontal and vertical deviation

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Authors: F. Abdelhamid

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Algeria is a multilingual speech community where individuals constantly mix between codes in spoken discourse. Code is used as a cover term to refer to the existing languages and language varieties which include, among others, the mother tongue of the majority Algerian Arabic, the official language Modern Standard Arabic and the foreign languages French and English. The present study explores whether Algerians mix between these codes in online communication as well. Facebook is the selected platform from which data is collected because it is the preferred social media site for most Algerians and it is the most used one. Adopting the notion of translanguaging, this study attempts explaining how users of Facebook use multilingual messages to communicate meaning. Accordingly, multilingual interactions are not approached from a pejorative perspective but rather as a creative linguistic behavior that multilingual utilize to achieve intended meanings. The study is intended as a contribution to the research on multilingualism online because although an extensive literature has investigated multilingualism in spoken discourse, limited research investigated it in the online one. Its aim is two-fold. First, it aims at ensuring that the selected platform for analysis, namely Facebook, could be a source for multilingual data to enable the qualitative analysis. This is done by measuring frequency rates of multilingual instances. Second, when enough multilingual instances are encountered, it aims at describing and interpreting some selected ones. 120 posts and 16335 comments were collected from two Facebook pages. Analysis revealed that third of the collected data are multilingual messages. Users of Facebook mixed between the four mentioned codes in writing their messages. The most frequent cases are mixing between Algerian Arabic and French and between Algerian Arabic and Modern Standard Arabic. A focused qualitative analysis followed where some examples are interpreted and explained. It seems that Algerians mix between codes when communicating online despite the fact that it is a conscious type of communication. This suggests that such behavior is not a random and corrupted way of communicating but rather an intentional and natural one.

Keywords: Algerian speech community, computer mediated communication, languages in contact, multilingualism, translanguaging

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Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

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This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

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Authors: M. Mueller, M. Kuehn, M. Voelker

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In small and medium-sized enterprises (SMEs), the challenge is to create a well-grounded and reliable basis for process analysis, optimization and planning due to a lack of data. SMEs have limited access to methods with which they can effectively and efficiently analyse processes and identify cause-and-effect relationships in order to generate the necessary database and derive optimization potential from it. The implementation of digitalization within the framework of Industry 4.0 thus becomes a particular necessity for SMEs. For these reasons, the abstract presents an analysis methodology that is subject to the objective of developing an SME-appropriate methodology for efficient, temporarily feasible data collection and evaluation in flexible production and logistics systems as a basis for process analysis and optimization. The overall methodology focuses on retrospective, event-based tracing and analysis of material flow objects. The technological basis consists of Bluetooth low energy (BLE)-based transmitters, so-called beacons, and smart mobile devices (SMD), e.g. smartphones as receivers, between which distance data can be measured and derived motion profiles. The distance is determined using the Received Signal Strength Indicator (RSSI), which is a measure of signal field strength between transmitter and receiver. The focus is the development of a software-based methodology for interpretation of relative movements of transmitters and receivers based on distance data. The main research is on selection and implementation of pattern recognition methods for automatic process recognition as well as methods for the visualization of relative distance data. Due to an existing categorization of the database regarding process types, classification methods (e.g. Support Vector Machine) from the field of supervised learning are used. The necessary data quality requires selection of suitable methods as well as filters for smoothing occurring signal variations of the RSSI, the integration of methods for determination of correction factors depending on possible signal interference sources (columns, pallets) as well as the configuration of the used technology. The parameter settings on which respective algorithms are based have a further significant influence on result quality of the classification methods, correction models and methods for visualizing the position profiles used. The accuracy of classification algorithms can be improved up to 30% by selected parameter variation; this has already been proven in studies. Similar potentials can be observed with parameter variation of methods and filters for signal smoothing. Thus, there is increased interest in obtaining detailed results on the influence of parameter and factor combinations on data quality in this area. The overall methodology is realized with a modular software architecture consisting of independently modules for data acquisition, data preparation and data storage. The demonstrator for initialization and data acquisition is available as mobile Java-based application. The data preparation, including methods for signal smoothing, are Python-based with the possibility to vary parameter settings and to store them in the database (SQLite). The evaluation is divided into two separate software modules with database connection: the achievement of an automated assignment of defined process classes to distance data using selected classification algorithms and the visualization as well as reporting in terms of a graphical user interface (GUI).

Keywords: event-based tracing, machine learning, process classification, parameter settings, RSSI, signal smoothing

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