Search results for: voice segmentation

Authors: Sibo Lu, Zhongkai Qian, Haotian Zhang

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This article examines the phenomenon of micro-sovereignty in the context of property management and its implications for biopolitics and urban governance in mainland China. It explores the transformation of urban spaces into privatized communities managed by property companies, leading to the reterritorialization of urban areas and the segmentation of urban populations. Drawing on legal frameworks, we analyze how commercial real estate development and property management have reshaped the urban landscape, placing nearly all urban residents within service areas of property management firms, thus establishing micro-sovereign entities that exercise control over residential spaces. Through a critique of property management's sovereign effects on social organization and the exploration of autonomous, democratic alternatives in community governance, this article contributes to the broader discourse on sovereignty, governance, and resistance within the urban milieu of contemporary China. It underscores the urgent need for more democratic forms of community management that can transcend the capitalist logic of property management companies and foster genuine participatory governance at the grassroots level.

Keywords: biopolitic, critical theory, political sociology, political philosophy

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Authors: Mercy Itohan Aruya

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This paper focuses on Usen, a speech form enclaved by the Edo communities in Ovia South West Local Government Area of Edo State, Nigeria. Usen lies at the border between Edo and the Osun state in Nigeria and has a population size of about a hundred and eighty thousand native speakers (2006 population census of Nigeria). Usen, as it is spoken today is highly endangered and it is serious struggling for survival. The aim, therefore, is to ascertain the linguistics status of Usen using a lexicostatical approach. Lexicostatics is a linguistic technique employed in accessing the degree of linguistic divergence or relatedness between two or more languages based on the proportion of cognates. Data for this study were collected from competent native speakers whose ages fall within the range of 40-65. The instrument for this study is the Ibadan 400 word-list of basic items which are collected with of a digital voice recorder. Our major finding in this paper reveals and establishes the facts that Usen speech form is not a dialect but a language of its own. However, Usen is more related to Yoruba than Edo as the degree of relatedness between Usen and Yoruba is 56.14% while that between Usen and Edo is about 21.4% as shown in this research effort.

Keywords: Usen, lexicostatistics, cognate words, language status

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Authors: Yasith Mindula Saipath Wickramasinghe

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Augmented reality applications require a high processing power to load, render and live stream high-definition AR models and virtual scenes; it also requires device sensors to work excessively to coordinate with internal hardware, OS and give the expected outcome in advance features like object detection, real time tracking, as well as voice and text recognition. Excessive thermal generation due to these advanced functionalities has become a major research problem as it is unbearable for smaller mobile devices to manage such heat increment and battery drainage as it causes physical harm to the devices in the long term. Therefore, effective thermal management is one of the major requirements in Augmented Reality application development. As this paper discusses major causes for this issue, it also provides possible solutions in the means of operating system adaptations as well as further research on best coding practises to optimize the application performance that reduces thermal excessive thermal generation.

Keywords: augmented reality, device thermal management, GPU, operating systems, device I/O, overheating

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Authors: Ksenia Meshkova

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With increasing amount of multimedia data, it is very important to automate and speed up the process of obtaining meta. This process means not just recognition of some object or its movement, but recognition of the entire scene versus separate frames and having timeline segmentation as a final result. Labeling datasets is time consuming, besides, attributing characteristics to particular scenes is clearly difficult due to their nature. In this article, we will consider autoencoders application to unsupervised scene recognition and clusterization based on interpretable features. Further, we will focus on particular types of auto encoders that relevant to our study. We will take a look at the specificity of deep learning related to information theory and rate-distortion theory and describe the solutions empowering poor interpretability of deep learning in media content processing. As a conclusion, we will present the results of the work of custom framework, based on autoencoders, capable of scene recognition as was deeply studied above, with highlights generation resulted out of this recognition. We will not describe in detail the mathematical description of neural networks work but will clarify the necessary concepts and pay attention to important nuances.

Keywords: neural networks, computer vision, representation learning, autoencoders

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, Rebecca K. Rossi, Yanmin Yuan, Xianpei Li

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The fiscal growth of any country hinges on the prudent administration of water resources. The river Yamuna and Ganga are measured as the life line of India as it affords the needs for life to endure. Earth observation over remote sensing images permits the precise description and identification of ingredients on the superficial from space and airborne platforms. Multiple and heterogeneous image sources are accessible for the same geographical section; multispectral, hyperspectral, radar, multitemporal, and multiangular images. In this paper, a taxonomical learning of the fluvial geomorphological features of river Yamuna and Ganga over doon valley using color fusion of multispectral remote sensing images was performed. Experimental results exhibited that the segmentation based colorization technique stranded on pattern recognition, and color mapping fashioned more colorful and truthful colorized images for geomorphological feature extraction.

Keywords: color fusion, geomorphology, fluvial processes, multispectral images, pattern recognition

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Authors: Alyona Gencheva

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The most common way to study diagnostic results is using specialized programs at a stationary workplace. Magnetic Resonance Imaging is presented in a two-dimensional (2D) format, and Computed Tomography sometimes looks like a three-dimensional (3D) model that can be interacted with. The main idea of the research is to compare ways of displaying diagnostic results in virtual reality that can help a surgeon during or before an operation in augmented reality. During the experiment, the medical staff examined liver vessels in the abdominal area and heart boundaries. The search time and detection accuracy were measured on black-and-white and coloured scans. Usability testing in virtual reality shows convenient ways of interaction like hand input, voice activation, displaying risk to the patient, and the required number of scans. The results of the experiment will be used in the new C# program based on Magic Leap technology.

Keywords: augmented reality, computed tomography, magic leap, magnetic resonance imaging, usability testing, VTE risk

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Authors: Shana Malio-Satele, Lemalu Silao Vaisola Sefo

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Introductory Statement: South Seas Healthcare is Ōtara’s largest Pacific health provider in South Auckland, New Zealand. Our vision is excellent health and well-being for Pacific people and all communities through strong Pacific values. During the DELTA and Omicron outbreak of COVID-19, our Pacific people, indigenous Māori, and the community of South Auckland were disproportionately affected and faced significant hardship with existing inequities magnified. This study highlights the community-based learnings of harnessing community-based strengths such as indigenous resilience, family-informed experiences and stories that provide critical insights that inform health reform changes that will be sustainable and equitable for all indigenous populations. This study is based on critical learnings acquired during COVID-19 that challenge the deficit narrative common in healthcare about indigenous populations. This study shares case studies of marginalised groups and religious groups and the successful application of indigenous cultural strengths, such as collectivism, positive protective factors, and using trusted relationships to create meaningful change in the way healthcare is delivered. The significance of this study highlights the critical conditions needed to adopt a community-informed way of creating integrated healthcare that works and the role that the community can play in being part of the solution. Methodologies: Key methodologies utilised are indigenous and Pacific-informed. To achieve critical learnings from the community, Pacific research methodologies, heavily informed by the Polynesian practice, were applied. Specifically, this includes; Teu Le Va (Understanding the importance of trusted relationships as a way of creating positive health solutions); The Fonofale Methodology (A way of understanding how health incorporates culture, family, the physical, spiritual, mental and other dimensions of health, as well as time, context and environment; The Fonua Methodology – Understanding the overall wellbeing and health of communities, families and individuals and their holistic needs and environmental factors and the Talanoa methodology (Researching through conversation, where understanding the individual and community is through understanding their history and future through stories). Major Findings: Key findings in the study included: 1. The collectivist approach in the community is a strengths-based response specific to populations, which highlights the importance of trusted relationships and cultural values to achieve meaningful outcomes. 2. The development of a “village model” which identified critical components to achieving health reform change; system navigation, a sense of service that was culturally responsive, critical leadership roles, culturally appropriate support, and the ability to influence the system enablers to support an alternative way of working. Concluding Statement: There is a strong connection between community-based strengths being implemented into healthcare strategies and reforms and the sustainable success of indigenous populations and marginalised communities accessing services that are cohesive, equitably resourced, accessible and meaningful for families. This study highlights the successful community-informed approaches and practices used during the COVID-19 response in New Zealand that are now being implemented in the current health reform.

Keywords: indigenous voice, community voice, health reform, New Zealand

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Eliziane Navarro

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It is intended, in this study, from some poems from the work of the poet and Bishop of Sao Felix do Araguaia-MT Brazil Dom Pedro Casaldaliga, to analyze his poetics from the perspective of the environmental law. In his work, Casaldaliga made a considerable manifest against the oppression experienced especially by Xavante people inside the countryside of the state of Mato Grosso when some government programs benefited a large number of landowners in instead of that minority as a power and control self-affirmation process. The attention which Casaldaliga dismissed to the cause of indigenous eviction of their land called Maraiwatsede resulted in numerous death threats against the poet who was not silenced in the face of the landowners’ grievances. His voice contributed significantly to the process of land returning to the indigenous people. Because of the international pressure, the Italian company AGIP, owner of the land, tried to return it to the hands of the indigenous, unfortunately, in the middle of the process, the land was occupied by politicians and big landowners of the region. Another objective of this research is to check the connection of his testimonial literature with the actual legal context of the state in the 50s and also to analyze his poetry as a complaint that led the cause of the state's indigenous to the Eco 92 discussion in Rio de Janeiro.

Keywords: law and literature, indigenous migration, Mato Grosso, Pedro Casaldaliga

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Authors: Sourour Khouaja, Hejer Jlassi, Nadia Feddaoui, Kamel Hamrouni

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Most medical images, and especially mammographies, are now stored in large databases. Retrieving a desired image is considered of great importance in order to find previous similar cases diagnosis. Our method is implemented to assist radiologists in retrieving mammographic images containing breast with similar density aspect as seen on the mammogram. This is becoming a challenge seeing the importance of density criteria in cancer provision and its effect on segmentation issues. We used the BEMD (Bidimensional Empirical Mode Decomposition) to characterize the content of images and Euclidean distance measure similarity between images. Through the experiments on the MIAS mammography image database, we confirm that the results are promising. The performance was evaluated using precision and recall curves comparing query and retrieved images. Computing recall-precision proved the effectiveness of applying the CBIR in the large mammographic image databases. We found a precision of 91.2% for mammography with a recall of 86.8%.

Keywords: BEMD, breast density, contend-based, image retrieval, mammography

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Authors: Stephan Boehm, Julia Engel, Judith Eisser

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During the last decade communication in the Internet transformed from a broadcast to a conversational model by supporting more interactive features, enabling user generated content and introducing social media networks. Another important trend with a significant impact on electronic commerce is a massive usage shift from desktop to mobile devices. However, a presentation of product- or service-related information accumulated on websites, micro pages or portals often remains the pivot and focal point of a customer journey. A more recent change of user behavior –especially in younger user groups and in Asia– is going along with the increasing adoption of messaging applications supporting almost real-time but asynchronous communication on mobile devices. Mobile apps of this type cannot only provide an alternative for traditional one-to-one communication on mobile devices like voice calls or short messaging service. Moreover, they can be used in mobile commerce as a new marketing and sales channel, e.g., for product promotions and direct marketing activities. This requires a new way of customer interaction compared to traditional mobile commerce activities and functionalities provided based on mobile web-sites. One option better aligned to the customer interaction in mes-saging apps are so-called chatbots. Chatbots are conversational programs or dialog systems simulating a text or voice based human interaction. They can be introduced in mobile messaging and social media apps by using rule- or artificial intelligence-based imple-mentations. In this context, a comparative analysis is conducted to examine the impact of using traditional websites or chatbots for promoting a product in an impulse purchase situation. The aim of this study is to measure the impact on the customers’ user experi-ence and emotions. The study is based on a random sample of about 60 smartphone users in the group of 20 to 30-year-olds. Participants are randomly assigned into two groups and participate in a traditional website or innovative chatbot based mobile com-merce scenario. The chatbot-based scenario is implemented by using a Wizard-of-Oz experimental approach for reasons of sim-plicity and to allow for more flexibility when simulating simple rule-based and more advanced artificial intelligence-based chatbot setups. A specific set of metrics is defined to measure and com-pare the user experience in both scenarios. It can be assumed, that users get more emotionally involved when interacting with a system simulating human communication behavior instead of browsing a mobile commerce website. For this reason, innovative face-tracking and analysis technology is used to derive feedback on the emotional status of the study participants while interacting with the website or the chatbot. This study is a work in progress. The results will provide first insights on the effects of chatbot usage on user experiences and emotions in mobile commerce environments. Based on the study findings basic requirements for a user-centered design and implementation of chatbot solutions for mobile com-merce can be derived. Moreover, first indications on situations where chatbots might be favorable in comparison to the usage of traditional website based mobile commerce can be identified.

Keywords: chatbots, emotions, mobile commerce, user experience, Wizard-of-Oz prototyping

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Authors: Qianwei Cheng, A. K. M. Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber

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Thomas Friedman, in his famous book, argued that the world in this 21st century is flat and will continue to be flatter. This is attributed to rapid globalization and the interdependence of humanity that engendered tremendous in-flow of human migration towards the urban spaces. In order to keep the urban environment sustainable, policy makers need to plan based on extensive analysis of the urban environment. With the advent of high definition satellite images, high resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding urban space lies in useful categorization of the space that is usable for data collection, analysis, and visualization. In this paper, we propose a pragmatic categorization method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. Categorization to plan sustainable urban spaces should encompass the buildings and their surroundings. However, the state-of-the-art is mostly dominated by classification of building structures, building types, etc. and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh, where the surrounding environment is crucial for the categorization. Moreover, these categorizations propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real time. Our proposed method is divided into two steps-categorization and automation. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently a map was drawn. The categorization is based broadly on two dimensions-the state of urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four categories: 1) highly informal area; 2) moderately informal area; 3) moderately formal area; and 4) highly formal area. In total, sixteen sub-categories were identified. For semantic segmentation and automatic categorization, Google’s DeeplabV3+ model was used. The model uses Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used to train the model, and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean Intersection over Union (mIoU). In this paper, we propose a pragmatic categorization method that is readily applicable for automatic use in both developing and developed world context. The method can be augmented for real-time socio-economic comparative analysis among cities. It can be an essential tool for the policy makers to plan future sustainable urban spaces.

Keywords: semantic segmentation, urban environment, deep learning, urban building, classification

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Authors: Olivia Meeschaert

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The Conference of the Parties (COP) refers to the countries that signed on to the United Nations Framework Convention on Climate Change. This annual conference provides a platform for countries to voice their major climate concerns, negotiate on a number of global issues, and come to agreements with the world’s largest emitters on how to make incremental changes that will achieve global climate goals. Historically, the outcome of COP includes major climate pledges and international agreements. COP27 will take place in Egypt at the beginning of November 2022. The 197 parties will come together to develop solutions to the dire consequences of climate change that many people around the world are already experiencing. The war in Ukraine will require a different tone from last year’s COP, particularly given that major impacts of the war are being felt throughout Europe and have had a detrimental effect on the region’s progress in achieving the benchmarks set in their climate pledges. Last year’s COP opened with many climate advocates feeling optimistic but the commitments made in Glasgow have so far remained empty promises, and the main contributors to climate change – China, the European Union, and the United States of America – have not moved fast enough.

Keywords: environment, law and policy, china, European union, united states, greenhouse gas, climate change

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Authors: Ramkumar Gunasekaran, John Cater, Vinod Suresh, Haribalan Kumar

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The main objective of this project is to develop a statistical shape model using principal component analysis that could be used for analyzing the shape of the human airway. The ultimate goal of this project is to identify geometric risk factors for diagnosis and management of Obstructive Sleep Apnoea (OSA). Anonymous CBCT scans of 25 individuals were obtained from the Otago Radiology Group. The airways were segmented between the hard-palate and the aryepiglottic fold using snake active contour segmentation. The point data cloud of the segmented images was then fitted with a bi-cubic mesh, and pseudo landmarks were placed to perform PCA on the segmented airway to analyze the shape of the airway and to find the relationship between the shape and OSA risk factors. From the PCA results, the first four modes of variation were found to be significant. Mode 1 was interpreted to be the overall length of the airway, Mode 2 was related to the anterior-posterior width of the retroglossal region, Mode 3 was related to the lateral dimension of the oropharyngeal region and Mode 4 was related to the anterior-posterior width of the oropharyngeal region. All these regions are subjected to the risk factors of OSA.

Keywords: medical imaging, image processing, FEM/BEM, statistical modelling

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Authors: Sweta Rout-Hoolash

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Each year, selected students from around 20 countries begin their degrees at Middlesex University with the International Foundation Program (IFP), developing the skills required for academic study at a UK university. The IFP runs for 30 learning/teaching weeks at Middlesex University Mauritius Branch Campus, which is an international campus of UK’s Middlesex University. Successful IFP students join their degree courses already settled into life at their chosen campus (London, Dubai, Mauritius or Malta) and confident that they understand what is required for degree study. Although part of the School of Science and Technology, in Mauritius it prepares students for undergraduate level across all Schools represented on campus – including disciplines such as Accounting, Business, Computing, Law, Media and Psychology. The researcher has critically reviewed the framework and resources in the curriculum for a particular six week period of IFP study (dedicated group work phase). Despite working together closely for 24 weeks, IFP students approach the final 6 week small group work project phase with mainly inhibitive feelings. It was observed that students did not engage effectively in the group work exercise. Additionally, groups who seemed to be working well did not necessarily produce results reflecting effective collaboration, nor individual members’ results which were better than prior efforts. The researcher identified scope for change and innovation in the IFP curriculum and how group work is introduced and facilitated. The study explores the challenges of groupwork in the context of the Mauritius campus, though it is clear that the implications of the project are not restricted to one campus only. The presentation offers a reflective review on the previous structure put in place for the management of small group assessed projects on the programme from both the student and tutor perspective. The focus of the research perspective is the student voice, by taking into consideration past and present IFP students’ experiences as written in their learning journals. Further, it proposes the introduction of a revised framework to help students take greater ownership of the group work process in order to engage more effectively with the learning outcomes of this crucial phase of the programme. The study has critically reviewed recent and seminal literature on how to achieve greater student ownership during this phase especially under an environment of assessed multicultural group work. The presentation proposes several new approaches for encouraging students to take more control of the collaboration process. Detailed consideration is given to how the proposed changes impact on the work of other stakeholders, or partners to student learning. Clear proposals are laid out for evaluation of the different approaches intended to be implemented during the upcoming academic year (student voice through their own submitted reflections, focus group interviews and through the assessment results). The proposals presented are all realistic and have the potential to transform students’ learning. Furthermore, the study has engaged with the UK Professional Standards Framework for teaching and supporting learning in higher education, and demonstrates practice at the level of ‘fellow’ of the Higher Education Academy (HEA).

Keywords: collaborative peer learning, enhancing learning experiences, group work assessment, learning communities, multicultural diverse classrooms, studying abroad

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Authors: Justin Reverdi, Sixin Zhang, Saïd Aoues, Fabrice Gamboa, Serge Gratton, Thomas Pellegrini

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In vapor cycle systems, the mass flow sensor plays a key role for different monitoring and control purposes. However, physical sensors can be inaccurate, heavy, cumbersome, expensive, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor, based on other standard sensors, is a good alternative. This paper has two main objectives. Firstly, a data-driven model using a convolutional neural network is proposed to estimate the mass flow of the compressor. We show that it significantly outperforms the standard polynomial regression model (thermodynamic maps) in terms of the standard MSE metric and engineer performance metrics. Secondly, a semi-automatic segmentation method is proposed to compute the engineer performance metrics for real datasets, as the standard MSE metric may pose risks in analyzing the dynamic behavior of vapor cycle systems.

Keywords: deep learning, convolutional neural network, vapor cycle system, virtual sensor

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Authors: Ly Quyet Tien

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Starting in the 1990s and growing in tandem with the LGBTQI movement in Vietnam, Modern Saigon Lottery Show is one of the most popular Vietnamese drag queen shows in Ho Chi Minh City, Vietnam. Transgender by nature, underprivileged by status, avoided by the family, and despised by society for decades, these queer artists came out of the closet and decided to change their own destiny by organizing lottery music shows. Unprofessionally in the start, they gradually appeared professionally on stage in big cities and won the heart of the larger public. Their success affirms themself, promotes their gender right, and assures their life in a proud way: honest, independent, and sustainable. This paper aims to give the readers a full portrait of Modern Saigon’s activities through musical bingo. It examines the background into which the drag queen show was born and developed: its styles, themes as well as own particularity. It also studies its identity, diversity in the entertainment industry, and its revolutionary role in the promotion of the welfare of Vietnamese transgender citizens and the local popular queer culture in Vietnam and Asia.

Keywords: transgender, lottery show, equality, culture

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Authors: Albatool Al Abdulwahid, Bayan Shakally, Mariam Mohamed, Wed Almokri

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Artificial intelligence has infiltrated every part of our life and every field we can think of. With technical developments, artificial intelligence applications are becoming more prevalent. We chose ELSA speak because it is a magnificent example of Artificial intelligent applications, ELSA speak is a smartphone application that is free to download on both IOS and Android smartphones. ELSA speak utilizes artificial intelligence to help non-native English speakers pronounce words and phrases similar to a native speaker, as well as enhance their English skills. It employs speech-recognition technology that aids the application to excel the pronunciation of its users. This remarkable feature distinguishes ELSA from other voice recognition algorithms and increase the efficiency of the application. This study focused on evaluating ELSA speak application, by testing the degree of effectiveness based on survey questions. The results of the questionnaire were variable. The generality of the participants strongly agreed that ELSA has helped them enhance their pronunciation skills. However, a few participants were unconfident about the application’s ability to assist them in their learning journey.

Keywords: ELSA speak application, artificial intelligence, speech-recognition technology, language learning, english pronunciation

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Authors: Aaminah Hassan

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Advertisements carry a great potential to influence our lives because they are crafted to meet particular ends. Stereotypical representation in advertisements is capable of forming unconscious attitudes among people towards any gender and their abilities. This study focuses on gender representation in Pakistani prime time advertisements. For this purpose, 13 advertisements were selected from three different categories of foods and beverages, cosmetics, cell phones and cellular networks from the prime time slots of one of the leading Pakistani entertainment channel, ‘Urdu 1’. Both quantitative and qualitative analyses are carried out for range of variables like gender, age, roles, activities, setting, appearance and voice overs. The results revealed that gender representation in advertisements is stereotypical. Moreover, in few instances, the portrayal of women is not only culturally inappropriate but is demeaning to the image of women as well. Their bodily charm is used to promote products. Comparing different entertainment channels for their prime time advertisements and broadening the scope of this research will yield greater implications for the researchers who want to carry out the similar research. It is hoped that the current study would help in the promotion of media literacy among the viewers and media authorities in Pakistan.

Keywords: Advertisements, Content Analysis, Gender, Prime time

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Authors: Costrie Ganes Widayanti

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Learning Disabilities (LDs) are less understood by the Indonesia’s educational practitioners. As a result, students with LDs are at risk of being outcast from the learning process that requires participation, which potentially disconnects them academically and socially. Its objective is to raise the voice of students with LDs regarding their engagement in the classroom. This research is conducted in two urban regular public primary schools in Indonesia. The study uses an ethnographic case study research design, which explores the views and experiences of four (4) students with LDs. The data were collected using participant observations and interviews. The preliminary findings highlighted two areas: 1) the stigmatization about LDs; and 2) perceived membership. Having LDs was a barrier to fully engage in the academic and social life. Interestingly, they were more likely dependent on each other for support as limited assistance was offered by teachers and peers. Their peers did not take a keen interest in helping them when they found difficulties with the assignments. Furthermore, due to their low academic performance, they were not in favor of being nominated as a group member. In a situation that required them to do a group assignment, they were not expected to give a contribution, positioning themselves as incompatible. These findings indicated that such practices legitimate the hegemony of the superior over those who are powerless and left behind.

Keywords: engagement, experiences, learning disability, qualitative design

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Authors: Unity Nkateng

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New forms of academic writing such as apprenticeship genres are developing in the field of applied linguistics. However, these perspectives have not adequately addressed the issue of social work students in Botswana. The paper addresses the issue of academic writing with special attention to the types of documents written by University of Botswana (UB) social work students on their fieldwork placement. The research method for this study combines two major research tools in the qualitative inquiry which are text analysis and interviews in order to investigate the context in which the texts are produced. 12 students were consulted and gave their consent for the study. 26 case reports were collected from the Department of Social work at the University of Botswana. The findings show that the case reports students write during their fieldwork placements have 6 moves, which focus on the clients’ story and describe what the students have done and achieved. The significance is that the discrepancy between professional writing and students writing raise questions about the extent to which students are being prepared for professional writing. Students have indicated that their academic writing varies according to the preferences of individual lecturers rather than the requirement of the work situation.

Keywords: apprenticeship genres, client's voice, material processes, relational possesive processes

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Authors: Moohyun Lee, Soojung Hur, Yongwan Park

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In this paper is proposed a method uses only LIDAR system to classification an obstacle and determine its type by establishing database for classifying obstacles based on LIDAR. The existing LIDAR system, in determining the recognition of obstruction in an autonomous vehicle, has an advantage in terms of accuracy and shorter recognition time. However, it was difficult to determine the type of obstacle and therefore accurate path planning based on the type of obstacle was not possible. In order to overcome this problem, a method of classifying obstacle type based on existing LIDAR and using the width of obstacle materials was proposed. However, width measurement was not sufficient to improve accuracy. In this research, the width data was used to do the first classification; database for LIDAR intensity data by four major obstacle materials on the road were created; comparison is made to the LIDAR intensity data of actual obstacle materials; and determine the obstacle type by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that data declined in quality in comparison to 3D LIDAR and it was possible to classify obstacle materials using 2D LIDAR.

Keywords: obstacle, classification, database, LIDAR, segmentation, intensity

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Authors: Jaqueline Maria Ribeiro Vieira

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Different strategies and tools are available at the oil and gas industry for detecting and analyzing tension and possible fractures in borehole walls. Most of these techniques are based on manual observation of the captured borehole images. While this strategy may be possible and convenient with small images and few data, it may become difficult and suitable to errors when big databases of images must be treated. While the patterns may differ among the image area, depending on many characteristics (drilling strategy, rock components, rock strength, etc.). Previously we developed and proposed a novel strategy capable of detecting patterns at borehole images that may point to regions that have tension and breakout characteristics, based on segmented images. In this work we propose the inclusion of data-mining classification strategies in order to create a knowledge database of the segmented curves. These classifiers allow that, after some time using and manually pointing parts of borehole images that correspond to tension regions and breakout areas, the system will indicate and suggest automatically new candidate regions, with higher accuracy. We suggest the use of different classifiers methods, in order to achieve different knowledge data set configurations.

Keywords: image segmentation, oil well visualization, classifiers, data-mining, visual computer

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Authors: Lidia Sanchis-Marco, Antonio Rubia, Pedro Serrano

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This paper studies the informational content of pricing errors in the term structure of sovereign CDS spreads. The residuals from a non-arbitrage model are employed to construct a Price discrepancy estimate, or noise measure. The noise estimate is understood as an indicator of market distress and reflects frictions such as illiquidity. Empirically, the noise measure is computed for an extensive panel of CDS spreads. Our results reveal an important fraction of systematic risk is not priced in default swap contracts. When projecting the noise measure onto a set of financial variables, the panel-data estimates show that greater price discrepancies are systematically related to a higher level of offsetting transactions of CDS contracts. This evidence suggests that arbitrage capital flows exit the marketplace during time of distress, and this consistent with a market segmentation among investors and arbitrageurs where professional arbitrageurs are particularly ineffective at bringing prices to their fundamental values during turbulent periods. Our empirical findings are robust for the most common CDS pricing models employed in the industry.

Keywords: credit default swaps, noise measure, illiquidity, capital arbitrage

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Authors: Olivia Meeschaert

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The Conference of the Parties (COP) refers to the countries that signed on to the United Nations Framework Convention on Climate Change. This annual conference provides a platform for countries to voice their major climate concerns, negotiate on a number of global issues, and come to agreements with the world’s largest emitters on how to make incremental changes that will achieve global climate goals. Historically, the outcome of COP includes major climate pledges and international agreements. COP27 will take place in Egypt at the beginning of November 2022. The 197 parties will come together to develop solutions to the dire consequences of climate change that many people around the world are already experiencing. The war in Ukraine will require a different tone from last year’s COP, particularly given that major impacts of the war are being felt throughout Europe and have had a detrimental effect on the region’s progress in achieving the benchmarks set in their climate pledges. Last year’s COP opened with many climate advocates feeling optimistic but the commitments made in Glasgow have so far remained empty promises, and the main contributors to climate change – China, the European Union, and the United States of America – have not moved fast enough.

Keywords: environment, law and policy, climate change, greenhouse gas, conference of the parties, China, United States, European Union

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Authors: Dzunisani Sibuyi

Abstract:

This article demonstrates how corruption is revealed in Mandla Langa’s Memory of Stones (2000) through the deployment of narrational strategies by applying narrative theories by Gerard Genette’s Narrative Discourse and Narrative Discourse Revisited, as well as Mikhail Bakhtin’s Dialogic Imagination to the text. This is accomplished by analysing Langa’s use of extradiegetic-heterodiegetic and intradiegetic-homodiegetic narrational strategies respectively employed by the anonymous narrator and character narrator Mpanza. The narration provided by these narrators is multi-voiced in its approach to the events depicting corruption from various completing and explanatory perspectives. In addition, Langa also employs narrative techniques of narrating times such as simultaneous, subsequent, and interpolated narration to highlight corruption taking place, which is highlighted by situating the story in its presentness moments coinciding with the corruption action. As a result, by emphasising the events portraying the plight of the main characters and their struggle to resist and defeat corrupt leaders, the narration strategically reveals corruption.

Keywords: narrational strategies, narrating voice, dialogism, corruption, Gérard Genette, Mandla Langa, Mikhail Bakhtin, time(s) of the narration

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Authors: Eman Tadros

Abstract:

Solution Focused Therapy sheds a positive light on a client’s problem(s) by instilling hope, focusing on the connection with the client, and describing the problem in a way to display change being possible. Solution focused therapists highlight clients’ positive strengths, reframe what clients say, do, or believe in a positive statement, action, or belief. Narrative Therapy focuses on the stories individuals tell about their past in which shape their current and future lives. Changing the language used aids clients in reevaluating their values and views of themselves, this then constructs a more positive way of thinking about their story. Both therapies are based on treating each client as an individual with a problem rather than that the individual is a problem and being able to give power back to the client. The purpose of these ideologies is to open a client to alternative understandings. This paper displays how clinicians can empower and identify their clients’ positive strengths and resiliency factors. Narrative and Solution-Focused Techniques will be integrated to instill positivity and empowerment in clients. Techniques such as deconstruction, collaboration, complimenting, miracle/exception/scaling questioning will be analyzed and modeled. Furthermore, bridging Solution Focused Therapy and Narrative Therapy gives a voice to unheard client(s).

Keywords: solution focused therapy, narrative therapy, empowerment, resilience

Procedia PDF Downloads 239

Authors: Aysun Sezer

Abstract:

Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity.

Keywords: YOLOv8, object detection, humerus, scapula, IRM

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Authors: C. Arslan, K. Yakar

Abstract:

Social media is one of the most important and effective means of social interaction among people in which they create, share and exchange their ideas via photos, videos or voice messages. Although there are lots of communication tools. Social media sites are the most prominent ones that allows the users articulate themselves in a matter of seconds all around the world with almost any expenses and thus, they became very popular and widespread after its emergence. As the usage of social media increases, it becomes an effective instrument in social matters. While it is possible to use social media to emphasize basic human rights and protest some failures of any government as in “Arab Spring”, it is also possible to spread propaganda and misinformation just to cause long lasting insurgency, upheaval, turmoil or disorder as an instrument of intervention to internal affairs and state sovereignty by some hostile groups or countries. It is certain that social media has positive effects on participation in democracies allowing people express themselves freely and limitlessly, but obviously, the misuse of it is very common and it is quite possible that even a five-minute-long video record can topple down a government or give a solid reason to a government to review its policies on some certain areas. As one of the most important and effective means of participation, social media presents some opportunities as well as risks. In this study, the place of social media for participation in democracies will be demonstrated under the light of opportunities and risks.

Keywords: social media, democracy, participation, risks, opportunities

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Authors: Ahmed Kamil Hasan Al-Ali, Bouchra Senadji, Ganesh Naik

Abstract:

We propose a system to real environmental noise and channel mismatch for forensic speaker verification systems. This method is based on suppressing various types of real environmental noise by using independent component analysis (ICA) algorithm. The enhanced speech signal is applied to mel frequency cepstral coefficients (MFCC) or MFCC feature warping to extract the essential characteristics of the speech signal. Channel effects are reduced using an intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) approach for classification. The proposed algorithm is evaluated by using an Australian forensic voice comparison database, combined with car, street and home noises from QUT-NOISE at a signal to noise ratio (SNR) ranging from -10 dB to 10 dB. Experimental results indicate that the MFCC feature warping-ICA achieves a reduction in equal error rate about (48.22%, 44.66%, and 50.07%) over using MFCC feature warping when the test speech signals are corrupted with random sessions of street, car, and home noises at -10 dB SNR.

Keywords: noisy forensic speaker verification, ICA algorithm, MFCC, MFCC feature warping

Procedia PDF Downloads 408