Search results for: classification society

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: 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: 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: Lucie Pospíšilová, Robert Osman, Hana Porkertová

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The scholarly literature demonstrates disability and masculinity in conflict with each other. While disability is associated with dependence, weakness, or helplessness, masculinity is associated with independence, strength, and power. Thus, disabled masculinity might be a dilemma experienced on a personal level. The relationship between masculinity and disability is also interesting from a geographical point of view because the conception of space is gendered. In our society, the skills like spatial orientation, working with the maps, and navigation technologies as same as with scale are associated with masculinity. And because these skills are related to the visual imagination, it is the blindness that is associated with the limitation or even the absence of them. Thus, the conflict of masculinity and disability in the spatial experience is very well apparent in the case of visually impaired men. To study this conflict can tell us a lot not only about the experience of visually impaired men but also about the conception of space in geography and in our society. The paper uses Henri Lefebvre's theory of space based on a triad of spatial practice, representations of space, and representational space. It answers the question: How masculinity and disability intersect in the spatial experience of visually impaired men? The data come from research conducted in Brno and Prague (Czechia) in 2020 and 2021 and include 7 interviews and 6 go-alongs with visually impaired men.

Keywords: disability, masculinity, abstract space, spatial experience, visually impaired men

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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: Jonathan Sohn

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Deep neural networks (DNNs) have shown state-of-the-art performance for many applications, including computer vision, natural language processing, and speech recognition. Recently, adversarial attacks have been studied in the context of deep neural networks, which aim to alter the results of deep neural networks by modifying the inputs slightly. For example, an adversarial attack on a DNN used for object detection can cause the DNN to miss certain objects. As a result, the reliability of DNNs is undermined by their lack of robustness against adversarial attacks, raising concerns about their use in safety-critical applications such as autonomous driving. In this paper, we focus on studying the adversarial attacks and defenses on DNNs for image classification. There are two types of adversarial attacks studied which are fast gradient sign method (FGSM) attack and projected gradient descent (PGD) attack. A DNN forms decision boundaries that separate the input images into different categories. The adversarial attack slightly alters the image to move over the decision boundary, causing the DNN to misclassify the image. FGSM attack obtains the gradient with respect to the image and updates the image once based on the gradients to cross the decision boundary. PGD attack, instead of taking one big step, repeatedly modifies the input image with multiple small steps. There is also another type of attack called the target attack. This adversarial attack is designed to make the machine classify an image to a class chosen by the attacker. We can defend against adversarial attacks by incorporating adversarial examples in training. Specifically, instead of training the neural network with clean examples, we can explicitly let the neural network learn from the adversarial examples. In our experiments, the digit recognition accuracy on the MNIST dataset drops from 97.81% to 39.50% and 34.01% when the DNN is attacked by FGSM and PGD attacks, respectively. If we utilize FGSM training as a defense method, the classification accuracy greatly improves from 39.50% to 92.31% for FGSM attacks and from 34.01% to 75.63% for PGD attacks. To further improve the classification accuracy under adversarial attacks, we can also use a stronger PGD training method. PGD training improves the accuracy by 2.7% under FGSM attacks and 18.4% under PGD attacks over FGSM training. It is worth mentioning that both FGSM and PGD training do not affect the accuracy of clean images. In summary, we find that PGD attacks can greatly degrade the performance of DNNs, and PGD training is a very effective way to defend against such attacks. PGD attacks and defence are overall significantly more effective than FGSM methods.

Keywords: deep neural network, adversarial attack, adversarial defense, adversarial machine learning

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Authors: Prachi Chavda

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The world, as of today, is smaller than it was for those who lived few decades ago. Internet, media and telecommunications have impacted the world like never before. Culture is the pillar upon which a society mushrooms. A culture develops with human creativity over the years and also by the exchange and intermixing of ideas and way of life across different civilizations and we can say that one of the influencing medium of exchange and intermixing of these ideas is cinema. Cinema has been the wonderful as well as important medium of communication since it has been emerged. Change is the thumb rule of life and so have been Indian cinema. As society has evolved from time to time so has the stories of Indian Cinema and its characters, hence it directly effects to the Indian culture as cinema has been very strong mediator for information exchange. The paper tries to discuss deeply how Indian cinema (reel life) and Indian culture (real life) has been influencing each other that results into a constant modification in both. Moreover, the research tries to deal with the issue with some examples that as a outcome how movies impact the Indian culture positively and negatively on culture. Therefore, it spreads the wave of change in cultural settings of society. The paper also tries to light the psychology of youth of India. Today, children and youth greatly admire the ostentatious materialistic display of outfits and style of the actors in the movies. Also, the movies bearing romanticism and showcasing disputatious issues like pre-marital sex, live-in relationship, homo-sexuality etc. though without highlighting them extensively have indeed inspired the commoners. Pros and cons always exist. Such revelation of issues certainly give a spark in the minds of those who are in their formative years and the effect of which is seen with the passage of time Thus, we can say that emergence of cinema as a strong tool of social change as well as culture as a triggering factor for transformation in cinema. As, a finding we can say that culture and cinema of India are influencing factors for each other. Cinema and culture are two sides of a coin, where both are responsible for evolution of each other.

Keywords: cinema, culture, influence, transformation

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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: Niyi Adegoke, Saanumi Jimmy Omolou

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The prevalence rate of youth involving in cybercrime is alarming, which calls for concern among the government, parents, NGO and religious bodies, hence this paper aims at examining youth involvement in cybercrime in Nigeria. Achievement motivation theory was used to explain the activities of cyber-criminals in Nigerian society. A descriptive survey method was adopted for the study. The sample for the study was one hundred and fifty (150) respondents randomly selected from the population of the study. A questionnaire was used to gather information and data from the respondents. Data collected through the questionnaire were analyzed using percentage tool for the respondents’ bio-data while chi-square was employed to test the hypotheses. Findings from the study have revealed that parental negligence, unemployment, peer influence, and quest for materialism were responsible for cyber-crimes in Nigeria. The study concludes with the following recommendations among which are: creating employment opportunities for the youths and ensure good governance and accountability among other things will go a long way to solve the problem of cybercrime in our society.

Keywords: cybercrime, youth, Nigeria, unemployment, information communication technology

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Authors: Akbarnejad Neda

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A foreign language acquisition occurs when we can tell a joke and understand it. Most jokes are told in slang and common language. In the process of foreign language acquisition, an autonomous learner try to learn the standard language. But there is a colossal divergence between the usage of the different types of language in society. Here, we investigate the french slang and common language and examine the accurate perception of their usage. We illuminate the slang language in the french literature that provide considerably different types of language for an autonomous learner. We provide furthermore evidence from the french novels that demonstrate properly the different types of language and give in one sentence its social meanings. For example, the famous Queneau expression « Doukipudonktant » present the impact of slang language in society. The characters in the novel transfer the slang and the common language and their accurate usages. We present that the language of the autonomous learner depends on the language of the text that is read. Because literature is a vehicle of the culture and the expression demonstrate their real significations and usage in the culture, slang and common language have a crucial role in the culture and all of them are manifested in the oral language.

Keywords: common language, french, humor, slang language

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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: 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: Magdalena Igras, Joanna Grzybowska, Mariusz Ziółko

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The study of emotions expressed in emergency phone call is presented, covering both statistical analysis of emotions configurations and an attempt to automatically classify emotions. An emergency call is a situation usually accompanied by intense, authentic emotions. They influence (and may inhibit) the communication between caller and responder. In order to support responders in their responsible and psychically exhaustive work, we studied when and in which combinations emotions appeared in calls. A corpus of 45 hours of conversations (about 3300 calls) from emergency call center was collected. Each recording was manually tagged with labels of emotions valence (positive, negative or neutral), type (sadness, tiredness, anxiety, surprise, stress, anger, fury, calm, relief, compassion, satisfaction, amusement, joy) and arousal (weak, typical, varying, high) on the basis of perceptual judgment of two annotators. As we concluded, basic emotions tend to appear in specific configurations depending on the overall situational context and attitude of speaker. After performing statistical analysis we distinguished four main types of emotional behavior of callers: worry/helplessness (sadness, tiredness, compassion), alarm (anxiety, intense stress), mistake or neutral request for information (calm, surprise, sometimes with amusement) and pretension/insisting (anger, fury). The frequency of profiles was respectively: 51%, 21%, 18% and 8% of recordings. A model of presenting the complex emotional profiles on the two-dimensional (tension-insecurity) plane was introduced. In the stage of acoustic analysis, a set of prosodic parameters, as well as Mel-Frequency Cepstral Coefficients (MFCC) were used. Using these parameters, complex emotional states were modeled with machine learning techniques including Gaussian mixture models, decision trees and discriminant analysis. Results of classification with several methods will be presented and compared with the state of the art results obtained for classification of basic emotions. Future work will include optimization of the algorithm to perform in real time in order to track changes of emotions during a conversation.

Keywords: acoustic analysis, complex emotions, emotion recognition, machine learning

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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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Authors: Leone Sherman

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While the democratic rights of a citizen may be very clearly outlined in a country’s constitution, it’s not uncommon for political elite to undermine those rights and gain more power and control over a country than it is allowed by this constitution. Moreover, while such a change in some smaller states may not have a substantial impact on the international community, the same change in countries with vast resources and political influence, such as Russia, is always a considerable factor for the world policy. This article aims to research the weaknesses of the Russian democratic system and their effect on the international policy through the three key aspects: The Russian people’s ability to produce the required political will to control their government’s decisions, the current development of the Russian political environment, and the affection of this environment on the world community as a whole during the recent years. The used methodology is a narrative analysis of recent political events, official statistics, international investigations and media statements. As a result, the ever-widening gap between the people and the government becomes evidently seen, as well as the challenges it imposes on the political world arena, both current and those that still lie ahead of us.

Keywords: Russia, political analysis, democratic weaknesses, international society

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Authors: Hafiz Khubaib Ur Rehman Awan

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Education used to play a central role in the formation and transfiguration of society since early times, owing in part to the centrality of scripture and its study in the human circles. According to the Islamic purpose of education, its pivotal contribution in the society is to produce a balanced growth of the entire persona of an individual through training the spirit, intellect, rational self, feelings, and bodily senses such that faith is infused into the whole personality. The purpose of this study is to attempt the exploration of the development of the Islamic religious curriculum in the Islamic world with an emphasis on Pakistan because this homeland came into existence under the name of Islam. This study persists of necessary historical background on the curricular reform of religious education in Pakistan and their impact on it and the suburban countries. However, the mainstay of this paper bases on reform in the religious education curriculum and the challenges faced by Pakistan and the Islamic world. Some suggestions are positioned at the end for areas of Islamic religious education and the improvement of Islamic curricular reform, especially in Pakistan and generally in Muslim countries.

Keywords: curricula, religious educational institutions, Pakistan, Muslim world, educational, religious , curricula

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Authors: Harsha B. R., K. S. Anil Kumar

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Land characterization, classification, and soil suitability evaluation of grapes-growing pedons were assessed at fifteen taluks covering four agro climatic zones of Karnataka. Study on problems and potentials of grapes cultivation in selected agro-climatic zones was carried out along with the plant sample analysis. Twenty soil profiles were excavated as study site based on the dominance of area falling under grapes production and existing spatial variability of soils. The detailed information of profiles and horizon wise soil samples were collected to study the morphological, physical, chemical, and fertility characteristics. Climatic analysis and water retention characteristics of soils of major grapes-growing areas were also done. Based on the characterisation and classification study, it was revealed that soils of Doddaballapur (Bangalore Blue and Wine grapes), Bangalore North (GKVK Farm, Rajankunte, and IIHR Farm), Devanahalli, Magadi, Hoskote, Chikkaballapur (Dilkush and Red globe), Yelaburga, Hagari Bommanahalli, Bagalkot (UHS farm) and Indi fall under the soil order Alfisol. Vijaypur pedon of northern dry zone was keyed out as Vertisols whereas, Jamkhandi and Athani as Inceptisols. Properties of Aridisols were observed in B. Bagewadi (Manikchaman and Thompson Seedless) and Afzalpur. Soil fertility status and its mapping using GIS technique revealed that all the nutrients were found to be in adequate range except nitrogen, potassium, zinc, iron, and boron, which indicated the need for application along with organic matter to improve the SOC status. Varieties differed among themselves in yield and plant nutrient composition depending on their age, climatic, soil, and management requirements. Bangalore North (GKVK farm) and Jamkhandi are having medium soil organic carbon stocks of 6.21 and 6.55 kg m⁻³, respectively. Soils of Bangalore North (Rajankunte) were highly suitable (S1) for grapes cultivation. Under northern Karnataka, Vijayapura, B. Bagewadi, Indi, and Afzalpur vineyards were good performers despite the limitations of fertility and free lime content.

Keywords: land characterization, suitability, soil orders, soil organic carbon stock

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Authors: Nassima Noufail, Sara Bouhali

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In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

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Authors: Salah Eddine Ben Fadhel

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Lev Vygotsky (1896–1934) was one of the most significant psychologists of the twentieth century despite his short life. His cultural-historical theory is still inspiring many researchers today. At the same time, we observe in many studies a lack of understanding of his thoughts. Vygotsky poses in this theory the contribution of society to individual development and learning. Thus, it suggests that human learning is largely a social and cultural process, further mentioning the influence of interactions between people and the culture in which they live. In this presentation, we highlight, on the one hand, the strong points of the theory by highlighting the major questions it raises and its contribution to developmental psychology in general. On the other hand, we will demonstrate what Vygotsky's theory brings today to the understanding of the cognitive development of children and adolescents. The major objective is to better understand the cognitive mechanisms involved in the learning process in children and adolescents and, therefore, demonstrate the complex nature of psychological development. The main contribution is to provide conceptual insight, which allows us to better understand the importance of the theory and its major pedagogical implications.

Keywords: vygotsky, society, culture, history

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Authors: Vinod Chovvayil Panengal

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The idea of secularism in India has taken a different direction after independence when religion became a reason for a great divide in, otherwise harmonious society. Since then the religious spaces became protected and more sacred and not shared. However, there is a larger threat on beliefs, rituals, and the spirituality of these religions in the form of technology, tourism and globalization. In a way, they weaken the importance of religion from our society over a period of time. The importance of religion to a sense of place has been overlooked or diminished. Religion provides symbolic meaning to places which distinguishes certain physical environments from otherwise similar ones. The rapid transformation of urban spaces, eliminating the territorial differences of sense, spirit and identity have started creating urban centers rooting out this genre of unique urban spaces from our cities. Indian cities, with a strong identity created by rich and colorful overlays of culture through its evolution, have been threatened by this de-territorialization. This paper enquires the relationship of the symbol of the identity and religiosity of a place, through spatial form, rituals and activity, and accommodating the technology and the changing social structure within the bounds of that relationship. The subjects for this enquiry are Sufism and the Sufi city- Ajmer. The internal transformations in the ideologies of Islam and Sufism and the changes in the society surround it triggered the phenomena of de- territorialization. The need for establishing a symbiotic relationship between the spiritual content and the social life, through the manifestation of space, time and activity derived from this concern on abated territory of Sufism inside the city. Redirecting transformation catalyst such as tourism, technology, etc, towards the improvement of physical and social conditions, preservation of the heritage and the expansion of the notional idea of religion over the city will help to re- territorialize city as a Sufi city.

Keywords: sense of place, religion, Islam, identity

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Authors: Ali Aldakhil

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This paper explores the many faces of the barriers and exclusionary attitudes and practices that disabled teachers and students experience in a school where they teach or attend. Critical disability studies and inclusive education theory were used to conceptualise this inquiry and ground it in the literature. These theories were used because they magnify and expose the problems of disability/disablism as within-society instead of within-individual. Similarly, disability-first language was used in this study because it seeks to expose the social oppression and discrimination of disabled. Data were generated through conducting in-depth semi-structured interviews with six disabled teachers who teach disabled children in a Saudi mainstream school. Thematic analysis of data concludes that the school is fettered by disabling barriers, attitudes, and practices, which reflect the dominant culture of disablism that disabled people encounter in the Saudi society on a daily basis. This leads to the conclusion that overall deconstruction and reformation of Saudi mainstream schools are needed, including non-disabled people’s attitudes, policy, spaces, and overall arrangements of teaching and learning.

Keywords: disablism, disability studies, mainstream schools, Saudi Arabia

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Authors: K. Petcharaporn

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The external characteristics of tomatoes, such as freshness, color and size are typically used in quality control processes for tomatoes sorting. However, the internal mold infection of intact tomato cannot be sorted based on a visible assessment and destructive method alone. In this study, a non-destructive technique was used to predict the internal mold infection of intact tomatoes by using transmittance visible and near infrared (VIS-NIR) spectroscopy. Spectra for 200 samples contained 100 samples for normal tomatoes and 100 samples for mold infected tomatoes were acquired in the wavelength range between 665-955 nm. This data was used in conjunction with partial least squares-discriminant analysis (PLS-DA) method to generate a classification model for tomato quality between groups of internal mold infection of intact tomato samples. For this task, the data was split into two groups, 140 samples were used for a training set and 60 samples were used for a test set. The spectra of both normal and internally mold infected tomatoes showed different features in the visible wavelength range. Combined spectral pretreatments of standard normal variate transformation (SNV) and smoothing (Savitzky-Golay) gave the optimal calibration model in training set, 85.0% (63 out of 71 for the normal samples and 56 out of 69 for the internal mold samples). The classification accuracy of the best model on the test set was 91.7% (29 out of 29 for the normal samples and 26 out of 31 for the internal mold tomato samples). The results from this experiment showed that transmittance VIS-NIR spectroscopy can be used as a non-destructive technique to predict the internal mold infection of intact tomatoes.

Keywords: tomato, mold, quality, prediction, transmittance

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Authors: Bekim Avdiaj

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The transition period of Kosovo society brought fundamental changes in all the spheres of organizing life. This was the period when also in the cultural tradition the biggest movement and an emerging from ‘isolation’ or from the ‘shell’ occurred. Transformation of the traditional and embracing of the modern began here. The same was experienced and is currently being experienced also by Gjakova and its surrounding which is historically renowned for its great tradition and culture. The population of this region is actually facing a transition from the traditional system into the modern one and quite often with huge leaps. These ‘movements’ or ‘evolutions’ of the society of this region, besides the numerous positive things it ‘harvested’, also brought things that do not at all correspond with their tradition as well as new criminal occurrences which in the past were not present in this area. Furthermore, some of the ‘new’ behaviours that are embraced from other ‘cultures’ and ‘civilizations’, and which are often exceeded, are quite perturbing. The security situation is also worrying, particularly following the appearance of some new criminal occurrences. Therefore, with this research paper we will strive to analyse the new cultural “intersections” as well as the nature of the origin of some new very worrying criminal occurrences. We will present there also some factors inciting into these occurrences, which were confessed by the persons involved in these criminal occurrences and who come from this very region.

Keywords: crime, occurrence, culture, Gjakova Region

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim

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In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively.

Keywords: classification algorithms, Naïve Bayes, opinion review spam detection, singleton review spam detection, support vector machine

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Authors: Wenfu Zhang

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This study examines why young, privileged Chinese individuals emigrate, how they sustain fluid, ongoing movement, and the life goals through migrant entrepreneurship. Through interviews with 30 young Chinese migrant entrepreneurs in the UK, this study reveals that they migrate primarily to escape an increasingly "risk society", characterized by reduced social and personal freedoms, a hostile business environment, and hyper-competition within professional and entrepreneurial sectors in the PRC. In this context, elite-oriented immigration policies of Northern countries align with these migrants' goals, creating a duality of 'neoliberalism as exception' and 'exceptions to neoliberalism', which facilitates selective entry for young, privileged Chinese. Intriguingly, even within the perceived context of an increasingly 'risk society,' young, privileged migrants are hesitant to relinquish their Chinese nationality, despite having diligently obtained UK residency. This choice reflects a deliberate strategy to cultivate 'mobility capital'. A Northern country's residency offers mobility to exit China when “risks” emerge, while Chinese nationality enables a strategic return when advantageous. This study contributes to the literature on how young, privileged Chinese individuals from the not-so-distant ‘Deng Xiaoping Era’ view China’s ongoing “New Era” as shaping their future aspirations with uncertainty; it examines how revenue-driven immigration controls in postcolonial Northern countries impact Southern elites by enabling them to project their domestic inequality issues onto a global scale through the use of mobility capital. In this study, it term these young Chinese elites "Chinese Übermensches," an unparalleled phenomenon in Chinese migration history.

Keywords: mobility capital, elite migration, entrepreneurship, neoliberalism, risk society

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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