Search results for: equivalence class

Authors: Marina Paula Carreira Rolim

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In order to have ELLs take responsibility for their own learning, it is important that they develop skills to work their studies strategically. The less they rely on the instructor as the content provider, the more they become active learners and have a higher sense of self-regulation and confidence in the learning process. This e-poster proposes a new teacher-student relationship that encourages learners to reflect, think critically, and act upon their realities. It also suggests the implementation of different autonomy-supportive teaching tools, such as portfolios, written journals, problem-solving activities, and strategy-based discussions in class. These teaching tools enable ELLs to develop awareness of learning strategies, learning styles, study plans, and available learning resources as means to foster their creative power of learning outside of classroom. In the role of a learning advisor, the teacher is no longer the content provider but a facilitator that introduces skills such as ‘elaborating’, ‘planning’, ‘monitoring’, and ‘evaluating’. The teacher acts as an educator and promotes the use of lifelong metacognitive skills to develop learner autonomy in the ESL/EFL context.

Keywords: autonomy, metacognitive skills, self-regulation, learning strategies, reflection

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Authors: Ghada S. Moussa

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Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems.

Keywords: vehicle classification, bag-of-words technique, SVM classifier, LDA classifier, KNN classifier, decision tree classifier, SIFT algorithm

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Authors: Nivedha Rajaram

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Hybrid Quantum solvers have been given prime focus in recent days by computation problem-solving domain industrial applications. D’Wave Quantum Computers are one such paragon of systems built using quantum annealing mechanism. Discrete Quadratic Models is a hybrid quantum computing model class supplied by D’Wave Ocean SDK - a real-time software platform for hybrid quantum solvers. These hybrid quantum computing modellers can be employed to solve classic problems. One such problem that we consider in this paper is finding a network connectivity knowledge hub in a huge network of systems. Using this quantum solver, we try to find out the prime system hub, which acts as a supreme connection point for the set of connected computers in a large network. This paper establishes an innovative problem approach to generate a connectivity system hub plot for a set of systems using DWave ocean SDK hybrid quantum solvers.

Keywords: quantum computing, hybrid quantum solver, DWave annealing, network knowledge graph

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Authors: Carolina Busco, Daniela Osses

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The development of digital literacy has become a fundamental element that allows for citizen inclusion, access to quality jobs, and a labor market capable of responding to the digital economy. There are no methodological instruments available in Chile to measure the workforce’s digital literacy and improve national policies on this matter. Thus, the objective of this research is to develop a survey to measure digital literacy in a sample of 200 Chilean workers. Dimensions considered in the instrument are sociodemographics, access to infrastructure, digital education, digital skills, and the ability to use e-government services. To achieve the research objective of developing a digital literacy model of indicators and a research instrument for this purpose, along with an exploratory analysis of data using factor analysis, we used an empirical, quantitative-qualitative, exploratory, non-probabilistic, and cross-sectional research design. The research instrument is a survey created to measure variables that make up the conceptual map prepared from the bibliographic review. Before applying the survey, a pilot test was implemented, resulting in several adjustments to the phrasing of some items. A validation test was also applied using six experts, including their observations on the final instrument. The survey contained 49 items that were further divided into three sets of questions: sociodemographic data; a Likert scale of four values ranked according to the level of agreement; iii) multiple choice questions complementing the dimensions. Data collection occurred between January and March 2022. For the factor analysis, we used the answers to 12 items with the Likert scale. KMO showed a value of 0.626, indicating a medium level of correlation, whereas Bartlett’s test yielded a significance value of less than 0.05 and a Cronbach’s Alpha of 0.618. Taking all factor selection criteria into account, we decided to include and analyze four factors that together explain 53.48% of the accumulated variance. We identified the following factors: i) access to infrastructure and opportunities to develop digital skills at the workplace or educational establishment (15.57%), ii) ability to solve everyday problems using digital tools (14.89%), iii) online tools used to stay connected with others (11.94%), and iv) residential Internet access and speed (11%). Quantitative results were discussed within six focus groups using heterogenic selection criteria related to the most relevant variables identified in the statistical analysis: upper-class school students; middle-class university students; Ph.D. professors; low-income working women, elderly individuals, and a group of rural workers. The digital divide and its social and economic correlations are evident in the results of this research. In Chile, the items that explain the acquisition of digital tools focus on access to infrastructure, which ultimately puts the first filter on the development of digital skills. Therefore, as expressed in the literature review, the advance of these skills is radically different when sociodemographic variables are considered. This increases socioeconomic distances and exclusion criteria, putting those who do not have these skills at a disadvantage and forcing them to seek the assistance of others.

Keywords: digital literacy, digital society, workforce digitalization, digital skills

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Authors: Khalak Bin Khair, Saqib Jahir, Mohammed Ibrahim, Fahad Bin, Debajyoti Karmaker

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Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.

Keywords: deep learning, transfer learning, TensorFlow, camouflage, object detection, architecture, accuracy, model, VGG16

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Authors: Samia Sadouki Chibani, Abdelkamel Tari

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Web service composition combines available services to provide new functionality. Given the number of available services with similar functionalities and different non functional aspects (QoS), the problem of finding a QoS-optimal web service composition is considered as an optimization problem belonging to NP-hard class. Thus, an optimal solution cannot be found by exact algorithms within a reasonable time. In this paper, a meta-heuristic bio-inspired is presented to address the QoS aware web service composition; it is based on Elephant Herding Optimization (EHO) algorithm, which is inspired by the herding behavior of elephant group. EHO is characterized by a process of dividing and combining the population to sub populations (clan); this process allows the exchange of information between local searches to move toward a global optimum. However, with Applying others evolutionary algorithms the problem of early stagnancy in a local optimum cannot be avoided. Compared with PSO, the results of experimental evaluation show that our proposition significantly outperforms the existing algorithm with better performance of the fitness value and a fast convergence.

Keywords: bio-inspired algorithms, elephant herding optimization, QoS optimization, web service composition

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Authors: Martina Holenko Dlab, Natasa Hoic-Bozic, Ivica Boticki

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This paper presents technologically innovative and scalable mobile learning solution within the SCOLLAm project (“Opening up education through Seamless and COLLAborative mobile learning on tablet computers”). The main research method applied during the development of the SCOLLAm mobile learning system is design-based research. It assumes iterative refinement of the system guided by collaboration between researches and practitioners. Following the identification of requirements, a multiplatform mobile learning system SCOLLAm [in]Form was developed. Several experiments were designed and conducted in the first and second grade of elementary school. SCOLLAm [in]Form system was used to design learning activities for math classes during which students practice calculation. System refinements were based on experience and interaction data gathered during class observations. In addition to implemented improvements, the data were used to outline possible improvements and deficiencies of the system that should be addressed in the next phase of the SCOLLAm [in]Form development.

Keywords: adaptation, collaborative learning, educational technology, mobile learning, tablet computers

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Authors: Ani Kojoyan

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The Syrian refugee crisis has been a matter of worldwide concern during the recent years. And though refugees’ problems are contextualized in terms of time and space, the refugee crisis still remains a global issue to discuss. Since the start of the conflict, Armenia has welcomed thousands of Syrian refugees too. Taking into consideration Armenia’s current socio-economic and geopolitical situation, the flow of refugees is a challenge both for the country and for refugees themselves. However, these people are not simply refugees from Syria, they are Syrian-Armenian refugees; people whose ancestors were survivals of the Armenian Genocide, perpetrated by the Ottoman Turks in 1915, people whose ancestors became refugees a century ago in Syria and now, ironically, a century later they follow their ancestors’ paths, turning into refugees themselves in their historical homeland, facing various difficulties, among them socio-economic, socio-ideological, and identity and gender issues, the latter being the main topic of discussion in the present paper. The situation presented above makes us discuss certain questions within this study: how do Syrian-Armenian refugees define themselves and their status? Which are their gender roles in the socio-economic context? How do social and economic challenges re-shape Syrian-Armenian women refugees’ identities? The study applies qualitative research methods of analysis, which includes semi-structured and in-depth interviews with 15 participants (18-25, 26-40 age groups), and two focus group works, involving 8 participants (18-35 age group) for each focus group activity. The activities were carried out in October 2016, Yerevan, Armenia. The study also includes Secondary Data Analysis. In addition, in order to centralize refugee women’s experiences and identity issues, the study adopts a qualitative lens from a feminist standpoint position. It is based on the assumption that human activity structures and limits understanding, and that the distorted comprehension of events or activities has emerged from the male-oriented dominant judgement which can be discovered through uncovering the understanding of the situation from women’s activity perspectives. The findings suggest that identity is dynamic, complex, over-changing and sensitive to time and space, gender and class. The process of re-shaping identity is even more complicated and multi-layered and is based on internal and external factors, conditioned by individual and collective needs and interests. Refugees are mostly considered as people who lost their identity in the past since they have no longer connection anywhere and try to find it in the present. In turn, female refugees, being a more vulnerable class, go through more complicated identity re-formulating discourse negotiations. They stand between the borders of the old and new, borders of lost and re-found selves, borders of creating and self-fashioning, between illusions and the challenging reality. Particularly, refugee women become more sensitive within the discourses of the private and the public domains: some of them try to create a ‘new-self’, creating their space in a new society, whereas others try to negotiate their affective/emotional labour within their own family domains.

Keywords: feminist standpoint position, gender, identity, refugee studies, Syrian-Armenian women refugees

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Authors: Pamela Chia, Tay Yoong Chuan

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Introduction: Severe pulmonary hypertension (PH) patients requiring non-cardiac surgery risk have increased mortality rates ranging. These patients are plagued with cardiorespiratory failure, dysrhythmias and anticoagulation potentially with concurrent sepsis and renal insufficiency, perioperative morbidity. We present a deaf-mute patient with severe idiopathic PH emergently prepared for ruptured ectopic laparotomy. Case Report: A 20 year-old female, 62kg (BMI 25 kg/m2) with severe idiopathic PH (2DE Ejection Fraction was 41%, Pulmonary Artery Systolic Pressure (PASP) 105 mmHg, Right ventricle strain and hypertrophy) and selective mutism was rushed in for emergency laparotomy after presenting to the emergency department for abdominal pain. The patient had an NYHA Class II with room air SpO2 93-95%. While awaiting lung transplant, the patient takes warfarin, Sildanefil, Macitentan and even Selexipag for rising PASP. At presentation, vital signs: BP 95/63, HR 119 SpO2 88% (room air). Despite decreasing haemoglobin 14 to 10g/dL, INR 2.59 was reversed with prothrombin concentrate, and Vitamin K. ECG revealed Right Bundle Branch Block with right ventricular strain and x-ray showed cardiomegaly, dilated Right Ventricle, Pulmonary Arteries, basal atelectasis. Arterial blood gas showed compensated metabolic acidosis pH 7.4 pCO2 32 pO2 53 HCO3 20 BE -4 SaO2 88%. The cardiothoracic surgeon concluded no role for Extracorporeal Membrane Oxygenation (ECMO). We inserted invasive arterial and central venous lines with blood transfusion via an 18G cannula before the patient underwent a midline laparotomy, haemostasis of ruptured ovarian cyst with 2.4L of clots under general anesthesia and FloTrac cardiac output monitoring. Rapid sequence induction was done with Midazolam/Propofol, remifentanil infusion, and rocuronium. The patient was maintained on Desflurane. Blood products and colloids were transfused for further 1.5L blood loss. Postoperatively, the patient was transferred to the intensive care unit and was extubated uneventfully 7hours later. The patient went home a week later. Discussion: Emergency hemostasis laparotomy in anticoagulated WHO Class I PH patient awaiting lung transplant with no ECMO backup poses tremendous stress on the deaf-mute patient and the anesthesiologist. Balancing hemodynamics avoiding hypotension while awaiting hemostasis in the presence of pulmonary arterial dilators and anticoagulation requires close titration of volatiles, which decreases RV contractility. We review the contraindicated anesthetic agents (ketamine, N2O), choice of vasopressors in hypotension to maintain Aortic-right ventricular pressure gradients and nitric oxide use perioperatively. Conclusion: Interdisciplinary communication with a deaf-mute moribund patient and anesthesia considerations pose many rare challenges worth sharing.

Keywords: pulmonary hypertension, case report, warfarin reversal, emergency surgery

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Authors: Anoop Kumar Suraj

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Dr Rammanohar Lohia (1910-67), a radical Indian socialist thinker, left an unfinished and critical oeuvre of works on ‘Social Revolution’, argued for the necessity of fundamentally reordering our social structures and offered the ideological framework for such a radical change. An alternative kind of democratic political action called Saat Krantiya, or ‘seven revolutions’, sought to establish socialism with a strong cultural and historical foundation in Indian society. Lohia cautiously adopted civil disobedience [a Gandhian tool] as a means of seven revolutions as a mode of revolution. He saw Indian youth as the vanguard of the social revolution and claimed that the ideas of ‘constructive militancy’ and ‘militant construction’ were at the core of such a revolution. This paper demonstrates that Lohia presented a unique short theoretical paradigm to interpret history and revolution, and Sapta Kranti was a normative framework to arrive at an egalitarian society.

Keywords: Rammanohar Lohia, Sapt Kranti, matter and spirit, caste-class, human approximation

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Authors: S. H. Rashmi, G. M. Madhu, A. A. Kittur, R. Suresh

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Polymer nanocomposites represent a new class of materials in which nanomaterials act as the reinforcing material in composites, wherein small additions of nanomaterials lead to large enhancements in thermal, optical, and mechanical properties. A boost in these properties is due to the large interfacial area per unit volume or weight of the nanoparticles and the interactions between the particle and the polymer. Micro-sized particles used as reinforcing agents scatter light, thus, reducing light transmittance and optical clarity. Efficient nanoparticle dispersion combined with good polymer–particle interfacial adhesion eliminates scattering and allows the exciting possibility of developing strong yet transparent films, coatings and membranes. This paper aims at synthesizing zinc oxide nanoparticles which are reinforced in poly vinyl alcohol (PVA) polymer. The mechanical properties showed that the tensile strength of the PVA nanocomposites increases with the increase in the amount of nanoparticles.

Keywords: glutaraldehyde, polymer nanocomposites, poly vinyl alcohol, zinc oxide

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Authors: Kendall Artz

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In the 1950s, a wave of violent labor unrest shook a small town in south-western Louisiana leading to the racialisation of workers—previously considered white—as ‘mixed-race’ or, in local terms, ‘Redbone.’ This paper examines why the group known as ‘Redbones’ were marked as non-white in relation to strike violence and their opposition to capitalist expansion. Utilising archival research, historiography and oral testimony, I examine how an instance of labor unrest was reinterpreted by local law enforcement, an interstate capitalist class and the national press as calling into question the racial integrity of a group of workers who had been formerly marked as white. This explosive and largely unstudied strike provides an opportunity to better understand how racialisation operates as a technology of control, even over individuals who appear phenotypically white. The strike at Elizabeth allows a glimpse at the tactics of representatives of white supremacy when white workers do not fully embrace the ‘wages of whiteness.

Keywords: American federation of labor, labor history, Louisiana history, wages of whiteness

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Authors: Taksina Bunbut

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Personality and grooming are vital for creating professionalism and safety image for all staffs in the airline industry. Airline Business Program also has an aim to educate students through the subject Personality Development and Grooming in order to elevate the quality of students to meet standard requirements of the airline industry. However, students agree that there are many difficulties that cause unsuccessful learning experience in this subject. The research is to study problems that can afflict students from getting good results in the classroom. Furthermore, exploring possible solutions to overcome challenges are also included in this study. The research sample consists of 140 students who attended the class of Personality Development and Grooming. The employed research instrument is a questionnaire. Statistic for data analysis is t-test and Multiple Regression Analysis. The result found that although students are satisfied with teaching and learning of this subject, they considered that teaching in English and teaching topics in social etiquette in different cultures are difficult for them to understand.

Keywords: personality development, grooming, Airline Business Program, soft skill

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Authors: Malik Ghulam Behlol, Alison Fox, Faiza Masood, Sabiha Arshad

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This paper explores the barriers to the application of learning-supportive assessment at teaching practicum while investigating the role of university teachers (UT), cooperative teachers (CT), prospective teachers ( PT) and heads of the practicum schools (HPS) in the selected universities of Pakistan and the UK. It is a qualitative case study and data were collected through the lesson observation of UT in the pre-service teacher education setting and PT in practicum schools. Interviews with UT, HPS, and Focus Group Discussions with PT were conducted too. The study has concluded that as compared to the UK counterpart, PT in Pakistan faces significant barriers in applying learning-supportive assessment in the school practicum settings because of large class sizes, lack of institutionalised collaboration between universities and schools, poor modelling of the lesson, ineffective feedback practices, lower order thinking assignments, and limited opportunities to use technology in school settings.

Keywords: assessment, pre-service teacher education, theory-practice gap, teacher education

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Authors: V. Vsevolozhskaia

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Since 1990, energy-efficient development concepts have constituted both a turning point in civil engineering and a challenge for an environmentally friendly future. Energy and water currently play an essential role in the sustainable economic growth of the world in general and Russia in particular: the efficiency of the water supply system is the second most important parameter for energy consumption according to the British assessment method, while the water-energy nexus has been identified as a focus for accelerating sustainable growth and developing effective, innovative solutions. The activities considered in this study were aimed at organizing and executing the renovation of the property in residential buildings located in St. Petersburg, specifically buildings with local or federal historical heritage status under the control of the St. Petersburg Committee for the State Inspection and Protection of Historic and Cultural Monuments (KGIOP) and UNESCO. Even after reconstruction, these buildings still fall into energy efficiency class D. Russian Government Resolution No. 87 on the structure and required content of project documentation contains a section entitled ‘Measures to ensure compliance with energy efficiency and equipment requirements for buildings, structures, and constructions with energy metering devices’. Mention is made of the need to install collectors and meters, which only calculate energy, neglecting the main purpose: to make buildings more energy-efficient, potentially even energy efficiency class A. The least-explored aspects of energy-efficient technology in the Russian Federation remain the water balance and the possibility of implementing rain and meltwater collection systems. These modern technologies are used exclusively for new buildings due to a lack of government directive to create project documentation during the planning of major renovations and reconstruction that would include the collection and reuse of rainwater. Energy-efficient technology for rain and meltwater collection is currently applied only to new buildings, even though research has proved that using rainwater is safe and offers a huge step forward in terms of eco-efficiency analysis and water innovation. Where conservation is mandatory, making changes to protected sites is prohibited. In most cases, the protected site is the cultural heritage building itself, including the main walls and roof. However, the installation of a second water supply system and collection of rainwater would not affect the protected building itself. Water efficiency in St. Petersburg is currently considered only from the point of view of the installation that regulates the flow of the pipeline shutoff valves. The development of technical guidelines for the use of grey- and/or rainwater to meet the needs of residential buildings during reconstruction or renovation is not yet complete. The ideas for water treatment, collection and distribution systems presented in this study should be taken into consideration during the reconstruction or renovation of residential cultural heritage buildings under the protection of KGIOP and UNESCO. The methodology applied also has the potential to be extended to other cultural heritage sites in northern countries and lands with an average annual rainfall of over 600 mm to cover average toilet-flush needs.

Keywords: cultural heritage, energy efficiency, renovation, rainwater collection, reconstruction, water management, water supply

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Authors: Heny Hartono, Cecilia Titiek Murniati

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The pandemic has given teachers and students no other choice but having full online learning. In such an emergency situation as the time of the covid-19 pandemic, the application of LMS (Learner Management System) in higher education is the most reasonable solution for students and teachers. In fact, the online learning requires all elements of a higher education systems, including the human resources, infrastructure, and supporting systems such as the application, server, and stable internet connection. The readiness of the higher education institution in preparing the online system may secure those who are involved in the online learning process. It may also result in students’ self-efficacy in online learning. This research aimed to investigate how students perceive their self-efficacy in online English learning, especially in speaking classes which is considered as a productive language skill. This research collects qualitative data with narrative inquiry involving 25 students of speaking classes as the respondents. The results of this study show that students perceive their self-efficacy in speaking online classes as not very high.

Keywords: self-efficacy, online learning, speaking class, college students, e-learning

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Authors: Hadeer M. Sayed, Hesham E. El Deeb, Shereen A. Taie

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In the big data era, we are facing a diversity of datasets from different sources in different domains that describe a single life event. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. Multimodal fusion is the concept of integrating information from multiple modalities in a joint representation with the goal of predicting an outcome through a classification task or regression task. In this paper, multimodal fusion techniques are classified into two main classes: model-agnostic techniques and model-based approaches. It provides a comprehensive study of recent research in each class and outlines the benefits and limitations of each of them. Furthermore, the audiovisual speech recognition task is expressed as a case study of multimodal data fusion approaches, and the open issues through the limitations of the current studies are presented. This paper can be considered a powerful guide for interested researchers in the field of multimodal data fusion and audiovisual speech recognition particularly.

Keywords: multimodal data, data fusion, audio-visual speech recognition, neural networks

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Authors: William Kwabena Nantwi

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This quantitative study investigates Creative Arts teachers' perceptions regarding the implementation of an Inclusive Creative Arts curriculum. The research employs a descriptive approach utilizing a 5-point Likert scale questionnaire comprising 15 items to gather data from Creative Arts educators. The Census with a disproportionate stratified sampling method was utilized to select 226 teachers from five educational circuits (Circuit A, B, C, D & E) within Offinso Municipality, Ghana. The findings indicate that most Creative Arts teachers hold a positive perception towards implementing an inclusive Creative Arts curriculum. Positive perceptions and attitudes among teachers are correlated with increased student engagement and participation in class activities. This study recommends organizing workshops and in-service training sessions focused on inclusive Creative Arts education for Creative Arts teachers. Additionally, it suggests that Colleges of Education and Universities responsible for teacher training integrate foundational courses in Creative Arts and Special Education into their primary education teacher preparation programs.

Keywords: perception, attitude, inclusive education, creative art, creative arts teachers, Ghana

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: Penn P. Wu

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Recent advances in virtual reality (VR) technologies have made it possible for students to experience a virtual on-the-scene or virtual in-person observation of an educational event. In an experimental class, the author uses VR, particularly 360° videos, to virtually engage students in an event, through a wide spectrum of educational resources, such s a virtual “bystander.” Students were able to observe the event as if they were physically on site, although they could not intervene with the scene. The author will describe the adopted equipment, specification, and cost of building them as well as the quality of VR. The author will discuss (a) feasibility, effectiveness, and efficiency of using VR as a supplemental technology to teach college students and criteria and methodologies used by the authors to evaluate them; (b) barriers and issues of technological implementation; and (c) pedagogical practices learned through this experiment. The author also attempts to explore (a) how VR could provide an interactive virtual in-person learning experience; (b) how VR can possibly change traditional college education and online education; (c) how educators and balance six critical factors: cost, time, technology, quality, result, and content.

Keywords: learning with VR, virtual experience of learning, virtual in-person learning, virtual reality for education

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Authors: Edward W. Staley, Corban G. Rivera, Ashley J. Llorens

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Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.

Keywords: reinforcement learning, multi-agent, deep learning, artificial intelligence

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Authors: A. E. Dumitriu

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The LHCb experiment is situated at one of the four points around CERN’s Large Hadron Collider, being a single-arm forward spectrometer covering 10 mrad to 300 (250) mrad in the bending (non-bending) plane, designed primarily to study particles containing b and c quarks. Each one of LHCb’s sub-detectors specializes in measuring a different characteristic of the particles produced by colliding protons, its significant detection characteristics including a high precision tracking system and 2 ring-imaging Cherenkov detectors for particle identification. The major two topics that I am currently concerned in are: the RIVET project (Robust Independent Validation of Experiment and Theory) which is an efficient and portable tool kit of C++ class library useful for validation and tuning of Monte Carlo (MC) event generator models by providing a large collection of standard experimental analyses useful for High Energy Physics MC generator development, validation, tuning and regression testing and V0 analysis for 2013 LHCb NoBias type data (trigger on bunch + bunch crossing) at √s=2.76 TeV.

Keywords: LHCb physics, RIVET plug-in, RIVET, CERN

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Authors: Tushar K. Routh

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If inputs are engineered in certain manners, they can influence deep neural networks’ (DNN) performances by facilitating misclassifications, a phenomenon well-known as adversarial attacks that question networks’ vulnerability. Recent studies have unfolded the relationship between vulnerability of such networks with their complexity. In this paper, the distinctive influence of additional convolutional layers at the decision boundaries of several DNN architectures was investigated. Here, to engineer inputs from widely known image datasets like MNIST, Fashion MNIST, and Cifar 10, we have exercised One Step Spectral Attack (OSSA) and Fast Gradient Method (FGM) techniques. The aftermaths of adding layers to the robustness of the architectures have been analyzed. For reasoning, separation width from linear class partitions and local geometry (curvature) near the decision boundary have been examined. The result reveals that model complexity has significant roles in adjusting relative distances from margins, as well as the local features of decision boundaries, which impact robustness.

Keywords: DNN robustness, decision boundary, local curvature, network complexity

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Authors: Daniel Bulanda, Janusz A. Starzyk, Adrian Horzyk

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The electrocardiogram (ECG) is one of the most commonly used medical tests, essential for correct diagnosis and treatment of the patient. While ECG devices generate a huge amount of data, only a small part of them carries valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the past years. However, the rapid development of new machine learning techniques poses new challenges. To address this class of problems, we created the FlexPoints algorithm that searches for characteristic points on the ECG signal and ignores all other points that do not carry relevant medical information. The conducted experiments proved that the presented algorithm can significantly reduce the number of data points which represents ECG signal without losing valuable medical information. These sparse but essential characteristic points (flex points) can be a perfect input for some modern machine learning models, which works much better using flex points as an input instead of raw data or data compressed by many popular algorithms.

Keywords: characteristic points, electrocardiogram, ECG, machine learning, signal compression

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Authors: Tomas Vampola, Michael Valášek

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On the basis of simulation-generated failure states of structural elements of a turboprop engine suitable for the busy-jet class of aircraft, an algorithm for early prediction of damage or reduction in functionality of structural elements of the engine is designed and verified with real data obtained at dynamometric testing facilities of aircraft engines. Based on an expanding database of experimentally determined data from temperature and pressure sensors during the operation of turboprop engines, this strategy is constantly modified with the aim of using the minimum number of sensors to detect an inadmissible or deteriorated operating mode of specific structural elements of an aircraft engine. The assembled algorithm for the early prediction of reduced functionality of the aircraft engine significantly contributes to the safety of air traffic and to a large extent, contributes to the economy of operation with positive effects on the reduction of the energy demand of operation and the elimination of adverse effects on the environment.

Keywords: detectability of malfunction, dynamometric testing, prediction of damage, turboprop engine

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Authors: David Steven Rosenstein

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The widely accepted “Input Theory” of language acquisition proposes that language is basically acquired unconsciously through extensive exposure to all kinds of natural oral and written sources, especially those where the level of the input is slightly above the learner’s competence. As such, it implies that oral error correction by teachers in a classroom is unnecessary, a waste of time, and maybe even counterproductive. And yet, oral error correction by teachers in the classroom continues to be a very common phenomenon. While input theory advocates claim that such correction doesn’t work, interrupts a student’s train of thought, harms fluency, and may cause students embarrassment and fear, many teachers would disagree. They would claim that students know they make mistakes and want to be corrected in order to know they are improving, thereby encouraging students’ desire to keep studying. Moreover, good teachers can create a positive atmosphere where students will not be embarrassed or fearful. Perhaps now is the time to revisit oral error correction in the classroom and consider the results of research carried out long ago by the present speaker. The research indicates that oral error correction may be beneficial in many cases.

Keywords: input theory, language acquisition, teachers' corrections, recurrent errors

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Authors: A. Lambert-Mogiliansky

Abstract:

In this paper, we investigate the properties of simple rules for reappointment aimed at holding a public official accountable and monitor his activity. The public official allocates budget resources to various activities which results in the delivery of public services to citizens. He has discretion over the use of resource so he can divert some of them for private ends. Because of a liability constraint, zero diversion can never be secured in all states. The optimal reappointment mechanism under complete information is shown to exhibit some leniency thus departing from the zero tolerance principle. Under asymmetric information (about the state), a rule with random verification in a pre-announced subset is shown to be optimal in a class of common rules. Surprisingly, those common rules make little use of hard information about service delivery when available. Similarly, PO's claim about his record is of no value to improve the performance of the examined rules. In contrast requesting that the PO defends his records publicly can be very useful if the service users are given the chance to refute false claims with cheap talk complaints: the first best complete information outcome can be approached in the absence of any observation by the manager of the accountability mechanism.

Keywords: accountability, corruption, persuasion, debate

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Authors: K. Tejasri, K. Suvarna Vani, S. Prathyusha, S. Ramya

Abstract:

Proteins are chained sequences of amino acids which are brought together by the peptide bonds. Many varying formations of the chains are possible due to multiple combinations of amino acids and rotation in numerous positions along the chain. Protein structure prediction is one of the crucial goals worked towards by the members of bioinformatics and theoretical chemistry backgrounds. Among the four different structure levels in proteins, we emphasize mainly the secondary level structure. Generally, the secondary protein basically comprises alpha-helix and beta-sheets. Multi-class classification problem of data with disparity is truly a challenge to overcome and has to be addressed for the beta strands. Imbalanced data distribution constitutes a couple of the classes of data having very limited training samples collated with other classes. The secondary structure data is extracted from the protein primary sequence, and the beta-strands are predicted using suitable machine learning algorithms.

Keywords: proteins, secondary structure elements, beta-sheets, beta-strands, alpha-helices, machine learning algorithms

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Authors: Saumyaranjan Sahoo, Sudhir Yadav

Abstract:

Indian manufacturing firms operating in rapidly changing and highly competitive market, over the last few decades, have embraced organization-wide transformation to achieve cultural and operational excellence. In recent years, numerous approaches have been proposed to improve business and manufacturing performance. Lean practices in particular, Total Productive Management (TPM) and Total Quality Management (TQM) have received considerable attention, as they being adopted and adapted for raising the performance standard of Indian manufacturing firms to world class levels. The complementary nature of TPM and TQM is being practiced in many companies to achieve synergy. Specifically, this research investigates whether joint TPM-TQM implementation contribute to higher business performance when compared to individual implementation. Data from 160 manufacturing firms were analyzed that demonstrate synergetic implementation of both TPM-TQM practices over a reasonable period of time, contributed in delivering better business performance as compared to individual implementation strategy.

Keywords: total productive management, total quality management, Indian manufacturing firms, business performance

Procedia PDF Downloads 271

Authors: Daniel O'Shea, Mario M. Attard, David C. Kellermann

Abstract:

In this paper, we propose a hyperelastic strain energy function that maps isotopic hyperelastic constitutive laws for the use of orthotropic materials without the use of structural tensors or any kind of fiber vector, or the use of standard invariants. In particular, we focus on neo-Hookean class of models and represent them using an invariant-free formulation. To achieve this, we revise the invariant-free formulation of isotropic hyperelasticity. The formulation uses quadruple contractions between fourth-order tensors, rather than scalar products of scalar invariants. We also propose a new decomposition of the orthotropic Hookean stiffness tensor into two fourth-order Lamé tensors that collapse down to the classic Lamé parameters for isotropic continua. The resulting orthotropic hyperelastic model naturally maintains all of the advanced properties of the isotropic counterparts, and similarly collapse back down to their isotropic form by nothing more than equality of parameters in all directions (isotropy). Comparisons are made with large strain experimental results for transversely isotropic rubber type materials under tension.

Keywords: finite strain, hyperelastic, invariants, orthotropic

Procedia PDF Downloads 448