Search results for: speech dataset

Authors: Jie Huo, Jonathan Wu

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Segmentation of anatomical structures in medical images is essential for scientific inquiry into the complex relationships between biological structure and clinical diagnosis, treatment and assessment. As a method of incorporating the prior knowledge and the anatomical structure similarity between a target image and atlases, multi-atlas segmentation has been successfully applied in segmenting a variety of medical images, including the brain, cardiac, and abdominal images. The basic idea of multi-atlas segmentation is to transfer the labels in atlases to the coordinate of the target image by matching the target patch to the atlas patch in the neighborhood. However, this technique is limited by the pairwise registration between target image and atlases. In this paper, a novel multi-atlas segmentation approach is proposed by introducing a dynamic energy model. First, the target is mapped to each atlas image by minimizing the dynamic energy function, then the segmentation of target image is generated by weighted fusion based on the energy. The method is tested on MICCAI 2012 Multi-Atlas Labeling Challenge dataset which includes 20 target images and 15 atlases images. The paper also analyzes the influence of different parameters of the dynamic energy model on the segmentation accuracy and measures the dice coefficient by using different feature terms with the energy model. The highest mean dice coefficient obtained with the proposed method is 0.861, which is competitive compared with the recently published method.

Keywords: brain MRI segmentation, dynamic energy model, multi-atlas segmentation, energy minimization

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Authors: Jake Gonzalez, Tommy Dang

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This paper proposes a temporal graph approach to visualize and analyze the evolution of gene relationships and nomenclature over time. An interactive web-based tool implements this temporal graph, enabling researchers to traverse a timeline and observe coupled dynamics in network topology and naming conventions. Analysis of a real human genomic dataset reveals the emergence of densely interconnected functional modules over time, representing groups of genes involved in key biological processes. For example, the antimicrobial peptide DEFA1A3 shows increased connections to related alpha-defensins involved in infection response. Tracking degree and betweenness centrality shifts over timeline iterations also quantitatively highlight the reprioritization of certain genes’ topological importance as knowledge advances. Examination of the CNR1 gene encoding the cannabinoid receptor CB1 demonstrates changing synonymous relationships and consolidating naming patterns over time, reflecting its unique functional role discovery. The integrated framework interconnecting these topological and nomenclature dynamics provides richer contextual insights compared to isolated analysis methods. Overall, this temporal graph approach enables a more holistic study of knowledge evolution to elucidate complex biology.

Keywords: temporal graph, gene relationships, nomenclature evolution, interactive visualization, biological insights

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Authors: Mirela-Gabriela Bratu

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The main objective of this study is to highlight the way in which emotions are constructed discursively in the French written press, more particularly in the titles of informative articles. To achieve this objective, we will begin the study with the theoretical part, which aims to capture the characteristics of journalistic discourse, to which we will add clues of emotions that we will identify in the titles of the articles. The approach is based on the empirical results from the analysis of the articles published on the earthquake that took place on August 24, 2016, in Italy, as described by two French national daily newspapers: Le Monde and Le Point. The corpus submitted to the analysis contains thirty-seven titles, published between August 24, 2016, and August 24, 2017. If the textual content of the speech offers information respecting the grammatical standards and following the presentation conventions, the choice of words can touch the reader, so the journalist must add other means than mastering of the language to create emotion. This study aims to highlight the strategies, such as rhetorical figures, the tenses, or factual data, used by journalists to create emotions for the readers. We also try, thanks to the study of the articles which were published for several days relating to the same event, to emphasize whether we can speak or not of the dissipation of emotion and the catastrophic side as the event fades away in time. The theoretical framework is offered by works on rhetorical strategies (Perelman, 1992; Amossi, 2000; Charaudeau, 2000) and on the study of emotions (Plantin, 1997, 1998, 2004; Tetu, 2004).

Keywords: disaster, earthquake, emotion, feeling

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Authors: Peng-Shao Chen, Yen-Jong Chen

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In recent years, global warming, the dramatic change in energy prices and the exhaustion of natural resources illustrated that energy-related topic cannot be ignored. Despite the relationship between the cities and CO₂ emissions has been extensively studied in recent years, little attention has been paid to differences in the geographical location of the city. However, the geographical climate has a great impact on lifestyle from city to city, such as the type of buildings, the major industry of the city, etc. Therefore, the paper instigates empirically the effects of kinds of urban factors and CO₂ emissions with consideration of the different geographic, climatic zones which cities are located. Using the regression model and a dataset of urban agglomeration in East Asia cities with over one million population, including 2005, 2010, and 2015 three years, the findings suggest that the impact of urban factors on CO₂ emissions vary with the latitude of the cities. Surprisingly, all kinds of urban factors, including the urban population, the share of GDP in service industry, per capita income, and others, have different level of impact on the cities locate in the tropical climate zone and temperate climate zone. The results of the study analyze the impact of different urban factors on CO₂ emissions in urban area with different geographical climate zones. These findings will be helpful for the formulation of relevant policies for urban planners and policy makers in different regions.

Keywords: carbon emissions, urban agglomeration, urban factor, urban geography

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Authors: Mohamed Khaled Ahmed Azzam

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Complete dentures, a non biological appliance, were and are still used to replace missing teeth and surrounding structures. Its main objectives are esthetics, speech, function and psychological state improvement. Dentists must realize that, just as dentate patients vary in their dental treatment complexity; edentulous patients also vary in the difficulty of their treatment plan. There are two main problems facing the removable Prosthodontist which harden his/her task how to please his patient with their new dentures being: Denture construction which however its fabrication is at the highest standards still is an unpleasant experience to all patients in the beginning and improves by time. This varies from one to several years according to the patient’s attitude, age, gender, socio-economical level and culture. The second problem of edentulous patients is both physical and psychological. Good interview, communication and note how patients present themselves for the concerns of their appearance, overall attitude and expectations concerning treatment is very important physically. On the psychological aspect patients have great difficulty to cope with new dentures to the extent of not using them at all. Hence their mind preparation should be commenced from day one by more than one method. This had a great impact on the acceptance which led to habituation to their dentures and patients were appreciative and pleased. In conclusion to successfully treat edentulous patients a great deal of information is required to complete a proper diagnosis, including patient mental attitude, past and present medical and dental conditions, and extra and intra-oral examinations. In addition to the clinical experience and skill of the whole dental team.

Keywords: complete dentures, edentulous patients, management of denture, psychological mind preparation

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Authors: Aniekan James Akpan

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The use of signs and symbols to represent cardinal information is inalienable among African communities. It has been the tenable instrument of cultural transmission for decades, but in the current transposal of Western values, a good number of communicating codes and symbolic images have been gradually relegated to the background. This paper discusses the regeneration of cultural artifacts and sign languages in Nigerian films using Johnpaul Rebecca and Ayamma as models in a qualitative research based on Charles Sanders Pierce's Semiotics as well as quantitative methods of survey using questionnaire and focus group discussion as instruments to obtain data. With a population of 2,343 viewers of the movies used for the study and a sample size of 322 respondents using Philip Meyer’s guide, findings show that cultural relics can stabilise the subject matter of a film. Artifacts can stimulate curiosity and invoke nostalgia. Sign languages encode deeper information that ordinary speech may not properly convey in films. It concludes that the use of non-verbal codes in filmmaking deserves deep research into the culture portrayed and that non-verbal cues communicate deeper information about a people's cosmos in a film than dialogue. It recommends that adequate research should be made before producing a film and the idea of cultural values represented in artifacts and sign language should be ingrained in the courses for students to acquaint themselves with.

Keywords: cultural artifact, Nollywood films, non-verbal communication, sign language

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Authors: Mairo Musa Galadima

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This paper examines the impact of Mother tongue interference on students’ performance in English Language in Bauchi State. It is observed that the students of Bauchi district share the same problem with Hausa native speakers of Kano dialect which is the standard form. It is observed that there are some phonemes which are present in English but absent in Hausa so the Hausa speakers of Bauchi district also replace these sounds with similar ones present in Hausa. Students in Bauchi district fail English language because they transfer features of their mother tongue (MT) into English. The data is obtained through unobtrusive observation of the English speech of about fifty Hausa native speakers of Bauchi district which is similar to Kano dialect from Abubakar Tatari Ali Polytechnic, Bauchi since only those who have had some good background of secondary education are used because uneducated Nigeria English of whatever geographical location is more likely to be unintelligible as cockney or uneducated African-American English. For instance /Ə:/ is absent in Hausa so the speakers find it difficult to distinguish between such pairs of words as /bƏ:d / and /bΛst/, /fa:st/ and /fƏ:st / hence /a:/ is generally used wherever /Ə:/ is present regardless of the spelling, that is why words like ‘work’, ‘first’ and ‘person’ all have / a:/. In Hausa most speakers use /P/ in place of, or in alternation with /f/, e.g. ‘few’ is pronounced as ‘pew’, or ‘pen’, as ‘fen’, /b/ for /v/, /s/ for /z/ and /z/ for /ᵹ/. Also the word vision/visn/ is pronounced as /vidzn/. Therefore, there is confusion in spellings and pronunciation of words. One solution out of the problem is having constant practice with a qualified consistent staff and making use of standard textbooks in the learning process.

Keywords: English, failure, mother tongue, interference, students

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Authors: El Sayed A. Sharara, A. Tsuji, K. Terada

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Call centers have been expanding and they have influence on activation in various markets increasingly. A call center’s work is known as one of the most demanding and stressful jobs. In this paper, we propose the fatigue detection system in order to detect burnout of call center agents in the case of a neck pain and upper back pain. Our proposed system is based on the computer vision technique combined skin color detection with the Viola-Jones object detector. To recognize the gesture of hand poses caused by stress sign, the YCbCr color space is used to detect the skin color region including face and hand poses around the area related to neck ache and upper back pain. A cascade of clarifiers by Viola-Jones is used for face recognition to extract from the skin color region. The detection of hand poses is given by the evaluation of neck pain and upper back pain by using skin color detection and face recognition method. The system performance is evaluated using two groups of dataset created in the laboratory to simulate call center environment. Our call center agent burnout detection system has been implemented by using a web camera and has been processed by MATLAB. From the experimental results, our system achieved 96.3% for upper back pain detection and 94.2% for neck pain detection.

Keywords: call center agents, fatigue, skin color detection, face recognition

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Authors: Guangyan Chen

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This study examined Chinese teachers’ perceptions of assessing U.S. college students’ L2 (second language) Chinese oral performances at different levels. Ten oral performances were videotaped from which three were chosen as samples to represent three different proficiency levels based on professionals’ judgments according to the ACTFL proficiency guidelines. The three samples were shown to L2 Chinese teachers who completed questionnaires about their assessments for each speech sample. In total, 104 L2 Chinese teachers responded to each of the three samples. The Exploratory Factor Analyses (EFA) of the teachers’ responses revealed three similar rating criteria patterns for assessing the three levels of oral performances. The teachers’ responses to Samples 2 and 3 revealed five rating criteria: Global proficiency, Chinese conceptual framework, content richness, communication appropriateness, and communication clarity. The teachers’ responses to Sample 1 revealed four rating criteria: global proficiency, Chinese conceptual framework, communication appropriateness/content richness, and communication clarity. However, the analyses of variance (ANOVAs) revealed that the proficiency levels of the three oral performances differed significantly across all rating criteria. Therefore, the data suggests that L2 classroom teachers could use the similar rating criteria pattern to assess college-level L2 Chinese students’ oral performances at different proficiency levels.

Keywords: language assessment, L2 Chinese, oral performance, rating criteria

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Authors: Kiurski S. Jelena, Kecić S. Vesna, Aksentijević M. Snežana

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Samples of tap and wastewater were collected in three offset printing facilities in Novi Sad, Serbia. Ten physicochemical parameters were analyzed within all collected samples: pH, conductivity, m - alkalinity, p - alkalinity, acidity, carbonate concentration, hydrogen carbonate concentration, active oxygen content, chloride concentration and total alkali content. All measurements were conducted using the standard analytical and instrumental methods. Comparing the obtained results for tap water and wastewater, a clear quality difference was noticeable, since all physicochemical parameters were significantly higher within wastewater samples. The study also involves the application of simple linear regression analysis on the obtained dataset. By using software package ORIGIN 5 the pH value was mutually correlated with other physicochemical parameters. Based on the obtained values of Pearson coefficient of determination a strong positive correlation between chloride concentration and pH (r = -0.943), as well as between acidity and pH (r = -0.855) was determined. In addition, statistically significant difference was obtained only between acidity and chloride concentration with pH values, since the values of parameter F (247.634 and 182.536) were higher than F_critical (5.59). In this way, results of statistical analysis highlighted the most influential parameter of water contamination in offset printing, in the form of acidity and chloride concentration. The results showed that variable dependence could be represented by the general regression model: y = a₀ + a₁x+ k, which further resulted with matching graphic regressions.

Keywords: pollution, printing industry, simple linear regression analysis, wastewater

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Authors: Misbah Fatma, Anam Saiyeda

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This analysis charts the introduction and expansion of research into the filter bubble phenomena over the last 10 years using a large dataset of academic publications. This bibliometric study demonstrates how interdisciplinary filter bubble research is. The identification of key authors and organizations leading the filter bubble study sheds information on collaborative networks and knowledge transfer. Relevant papers are organized based on themes including algorithmic bias, polarisation, social media, and ethical implications through a systematic examination of the literature. In order to shed light on how these patterns have changed over time, the study plots their historical history. The study also looks at how research is distributed globally, showing geographic patterns and discrepancies in scholarly output. The results of this bibliometric analysis let us fully comprehend the development and reach of filter bubble research. This study offers insights into the ongoing discussion surrounding information personalization and its implications for societal discourse, democratic participation, and the potential risks to an informed citizenry by exposing dominant themes, interdisciplinary collaborations, and geographic patterns. In order to solve the problems caused by filter bubbles and to advance a more diverse and inclusive information environment, this analysis is essential for scholars and researchers.

Keywords: bibliometric analysis, social media, social networking, algorithmic personalization, self-selection, content moderation policies and limited access to information, recommender system and polarization

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Authors: Jingjie Li, Wenjie Hu

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Phraseological units in academic English texts have been a central focus in recent corpus linguistic research. A wide variety of phraseological units have been explored, including collocations, chunks, lexical bundles, patterns, semantic sequences, etc. This paper describes a special category of clause-level phraseological units, namely, Characteristic Sentence Stems (CSSs), with a view to describing their defining criteria and extraction method. CSSs are contiguous lexico-grammatical sequences which contain a subject-predicate structure and which are frame expressions characteristic of academic writing. The extraction of CSSs consists of six steps: Part-of-speech tagging, n-gram segmentation, structure identification, significance of occurrence calculation, text range calculation, and overlapping sequence reduction. Significance of occurrence calculation is the crux of this study. It includes the computing of both the internal association and the boundary independence of a CSS and tests the occurring significance of the CSS from both inside and outside perspectives. A new normalization algorithm is also introduced into the calculation of LocalMaxs for reducing overlapping sequences. It is argued that many sentence stems are so recurrent in academic texts that the most typical of them have become the habitual ways of making meaning in academic writing. Therefore, studies of CSSs could have potential implications and reference value for academic discourse analysis, English for Academic Purposes (EAP) teaching and writing.

Keywords: characteristic sentence stem, extraction method, phraseological unit, the statistical measure

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Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

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Authors: Vallence Ngabo M., Leonard Atuhaire, Peter Clever Rutayisire

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The main aim of this study was to establish the differences in both the determinants of stunting and the causal mechanism through which the identified determinants influence stunting amongst male and female under-fives in Uganda. Literature shows that male children below the age of five years are at a higher risk of being stunted than their female counterparts. Specifically, studies in Uganda indicate that being a male child is positively associated with stunting, while being a female is negatively associated with stunting. Data for 904 males and 829 females under-fives was extracted form UDHS-2016 survey dataset. Key variables for this study were identified and used in generating relevant models and paths. Structural equation modeling techniques were used in their generalized form (GSEM). The generalized nature necessitated specifying both the family and link functions for each response variable in the system of the model. The sex of the child (b4) was used as a grouping factor and the height for age (HAZ) scores were used to construct the status for stunting of under-fives. The estimated models and path clearly indicated that the set of underlying factors that influence male and female under-fives respectively was different and the path through which they influence stunting was different. However, some of the determinants that influenced stunting amongst male under-fives also influenced stunting amongst the female under-fives. To reduce the stunting problem to the desirable state, it is important to consider the multifaceted and complex nature of the risk factors that influence stunting amongst the under-fives but, more importantly, consider the different sex-specific factors and their causal mechanism or paths through which they influence stunting.

Keywords: stunting, underfives, sex of the child, GSEM, causal mechanism

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Authors: Polina Pekker

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This work analyzes the role of lecturers in e-learning in Russia. It is based on qualitative research of lecturers who conduct courses on Russian national platforms of open education. The platform is based on edx software (provider of massive open online courses). The interviews with e-learning lecturers were conducted: from December 2015 till January 2016 and from April 2016 till May 2016. The results of interviews (face-to-face, telephone, skype) show, firstly, the difference between the role of lecturers in e-learning and in traditional education and, secondly, that the competition between lecturers is high in Russia. The results of interviews in Russia show that e-learning lecturer should have several special professional competences: the ability to keep attention of audiences without real contact, the ability to work on camera and competences related with e-learning course support (test, forum, communication on forum and etc.) It is concluded that lecturers need special course on acting and speech skills and on conducting and organizing of e-learning course in Russia. It is planned to conduct French study. When results from French research will be totally ready, they will be compared to Russian. As well French platform, France Universite Numerique, was launched earlier, in January 2014, so Russian lecturers should get best practice from the French colleagues.

Keywords: e-courses lecturer, e-learning, professional competences of lecturers, national Russian and French platforms of open education

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Authors: Matias Micheletto, Rodrigo Santos, Sergio F. Ochoa

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In developing countries, the most roads in rural areas are dirt road. They require frequent maintenance since are affected by erosive events, such as rain or wind, and the transit of heavy-weight trucks and machinery. Early detection of damages on the road condition is a key aspect, since it allows to reduce the main-tenance time and cost, and also the limitations for other vehicles to travel through. Most proposals that help address this problem require the explicit participation of drivers, a permanent internet connection, or important instrumentation in vehicles or roads. These constraints limit the suitability of these proposals when applied into developing regions, like in Latin America. This paper proposes an alternative method, based on unattended crowdsensing, to determine the quality of dirt roads in rural areas. This method involves the use of a mobile application that complements the road condition surveys carried out by organizations in charge of the road network maintenance, giving them early warnings about road areas that could be requiring maintenance. Drivers can also take advantage of the early warnings while they move through these roads. The method was evaluated using information from a public dataset. Although they are preliminary, the results indicate the proposal is potentially suitable to provide awareness about dirt roads condition to drivers, transportation authority and road maintenance companies.

Keywords: dirt roads automatic quality assessment, collaborative system, unattended crowdsensing method, roads quality awareness provision

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Authors: Alexander G. Yushchenko

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From a multi-science point of view, we analyze threats to security resulting from globalization of international information space and information and communication aggression of Russia. A definition of Ruschism is formulated as an ideology supporting aggressive actions of modern Russia against the Euro-Atlantic community. Stages of the hybrid war Russia is leading against Ukraine are described, including the elements of subversive activity of the special services, the activation of the military phase and the gradual shift of the focus of confrontation to the realm of information and communication technologies. We reveal an emergence of a threat for democratic states resulting from the destabilizing impact of a target state’s mass media and social networks being exploited by Russian secret services under freedom-of-speech disguise. Thus, we underline the vulnerability of cyber- and information security of the network society in regard of hybrid war. We propose to define the latter a synergetic war. Our analysis is supported with a long-term qualitative monitoring of representation of top state officials on popular TV channels and Facebook. From the memetics point of view, we have detected a destructive psycho-information technology used by the Kremlin, a kind of information catastrophe, the essence of which is explained in detail. In the conclusion, a comprehensive plan for information protection of the public consciousness and mentality of Euro-Atlantic citizens from the aggression of the enemy is proposed.

Keywords: cyber and information security, hybrid war, psycho-information technology, synergetic war, Ruschism

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Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.

Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares

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Authors: Dongju Kim, Youngjoo Suh, Hyojin Kim, Gyeongyeong Kim

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Acoustic Emission Testing (AET) is widely used to test the structural integrity of an operational hydrogen storage container, and clustering algorithms are frequently used in pattern recognition methods to interpret AET results. However, the interpretation of AET results can vary from user to user as the tuning of the relevant parameters relies on the user's experience and knowledge of AET. Therefore, it is necessary to use a deep learning model to identify patterns in acoustic emission (AE) signal data that can be used to classify defects instead. In this paper, a deep learning-based model for classifying the types of defects in hydrogen storage tanks, using AE sensor waveforms, is proposed. As hydrogen storage tanks are commonly constructed using carbon fiber reinforced polymer composite (CFRP), a defect classification dataset is collected through a tensile test on a specimen of CFRP with an AE sensor attached. The performance of the classification model, using one-dimensional convolutional neural network (1-D CNN) and synthetic minority oversampling technique (SMOTE) data augmentation, achieved 91.09% accuracy for each defect. It is expected that the deep learning classification model in this paper, used with AET, will help in evaluating the operational safety of hydrogen storage containers.

Keywords: acoustic emission testing, carbon fiber reinforced polymer composite, one-dimensional convolutional neural network, smote data augmentation

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: Temitope Abiodun Balogun

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Political interviews are one of the ways by which political office-seekers in Nigeria sell themselves to the electorates. Extant studies have examined the discourse of political interviews from conversational, philosophical, rhetorical, stylistic and pragmatic perspectives with insufficient attention paid to grammatical forms and communicative intentions of the interviews granted by the two presidential aspirants in the 2015 Nigerian general election. This study fills this scholarly gap to unmask their grammatical forms and communicative styles, intention and credibility. The paper adopts Halliday’s Systemic Functional Grammar, specifically interpersonal function coupled with Searle’s Model of Speech Acts Theory as a theoretical framework. A total of six interviews granted by the two presidential aspirants in media serve as the source of data. It is discovered that, in most cases, politicians’ communicative intention is to “pull-down” their political opponents. While declarative and interrogatives are simple, direct and straightforward, the intention is to condemn, lambast and castigate their opponents. This communicative style does not allow the general populace to decipher the political manifestoes of the political aspirants and the party they represent. The paper recommends that before Nigeria can boast of any sustainable growth and development, there is the need for her political office-seekers to adopt effective communication strategies and styles to unveil their intention and manifestoes so that electorates can evaluate their performance after their tenure of office.

Keywords: general election, grammatical forms and function, political interviews, presidential aspirants

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Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

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The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

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Authors: Youngjae Jin, Daeshik Kim

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This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in Verilog HDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset.

Keywords: auto-encoder, behavior model simulation, digital hardware design, pre-route simulation, Unsupervised feature learning

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Authors: Stelios A. Mitilineos, Nicolas-Alexander Tatlas, Georgia Korompili, Lampros Kokkalas, Stelios M. Potirakis

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Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a widespread chronic disease that mostly remains undetected, mainly due to the fact that it is diagnosed via polysomnography which is a time and resource-intensive procedure. Screening the disease’s symptoms at home could be used as an alternative approach in order to alert individuals that potentially suffer from OSAHS without compromising their everyday routine. Since snoring is usually linked to OSAHS, developing a snore detector is appealing as an enabling technology for screening OSAHS at home using ubiquitous equipment like commodity microphones (included in, e.g., smartphones). In this context, this study developed a snore detection tool and herein present the approach and selection of specific sound features that discriminate snoring vs. environmental sounds, as well as the performance of the proposed tool. Furthermore, a Real-Time Snore Detector (RTSD) is built upon the snore detection tool and employed in whole-night sleep sound recordings resulting to a large dataset of snoring sound excerpts that are made freely available to the public. The RTSD may be used either as a stand-alone tool that offers insight to an individual’s sleep quality or as an independent component of OSAHS screening applications in future developments.

Keywords: obstructive sleep apnea hypopnea syndrome, apnea screening, snoring detection, machine learning, neural networks

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

Abstract:

We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Rufus Olufemi Adebayo, Sylvia Phiwani Zulu

Abstract:

Interpreting in the setting of diverse language and multicultural congregants, is often understood as integrating the content of the message. Preaching, similar to any communication, takes seriously people’s multiple contexts. The one who provides the best insight into understanding “the other”, traditionally speaking could be an interpreter in a multilingual context. Nonetheless, there are reflections in the loss of spiritual communication, translation and interpretive dialogue. No matter how eloquent the preacher is, an interpreter can make or mere the sermon (speech). The sermon that the preacher preaches is not always the one the congregation hears from the interpreter. In other occurrences, however, interpreting can lead not only to distort messages but also to dissatisfied audiences and preacher being overshadowed by the pranks of the interpreter. Using qualitative methodology, this paper explores the challenges and the conventional assumptions about preachers’ interpreter as influenced by spirituality, culture, and language in empirical and theoretical perspectives. An emphasis on the bias translation and the basis of reality that suppresses or devalues the spiritual communication is examined. The result indicates that interpretation of the declaration of guilt, history of congregation, spirituality, attitudes, morals, customs, specific practices of a preacher, education, and the environment form an entangled and misinterpretation. The article concludes by re-examining these qualities and rearticulating them into a preliminary theory for practice, as distinguished from theory, which could possibly enhance the development of more sustainable multilingual interpretation in the South African Pentecostal churches.

Keywords: congregants, divergences, interpreting/translation, language & communication, sermon/preaching

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Authors: Neha Ahirwar

Abstract:

In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Reda A. H. Mahmoud

Abstract:

This paper investigates the pragmatic meaning of prosodic focus through four accentuation moods of blaming utterances in Egyptian Arabic. Prosodic focus results in various pragmatic meanings when the speaker utters the same blaming expression in different emotional moods: the angry, the mocking, the frustrated, and the informative moods. The main objective of this study is to interpret the meanings of these four accentuation moods in relation to their illocutionary forces and pre-locutionary effects, the integrated features of prosodic focus (e.g., tone movement distributions, pitch accents, lengthening of vowels, deaccentuation of certain syllables/words, and tempo), and the consonance between the former prosodic features and certain lexico-grammatical components to communicate the intentions of the speaker. The data on blaming utterances has been collected via elicitation and pre-recorded material, and the selection of blaming utterances is based on the criteria of lexical and prosodic regularity to be processed and verified by three computer programs, Praat, Speech Analyzer, and Spectrogram Freeware. A dual pragmatic approach is established to interpret expressive blaming utterance and their lexico-grammatical distributions into intonational focus structure units. The pragmatic component of this approach explains the variable psychological attitudes through the expressions of blaming and their effects whereas the analysis of prosodic focus structure is used to describe the intonational contours of blaming utterances and other prosodic features. The study concludes that every accentuation mood has its different prosodic configuration which influences the listener’s interpretation of the pragmatic meanings of blaming utterances.

Keywords: pragmatics, pragmatic interpretation, prosody, prosodic focus

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

Abstract:

The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

Procedia PDF Downloads 188