Search results for: eccentric training

Authors: Zunaira Zaman, David Bohannon

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This study explores the application of machine learning (ML) for detecting security vulnerabilities in source code. The research aims to assist organizations with large application portfolios and limited security testing capabilities in prioritizing security activities. ML-based approaches offer benefits such as increased confidence scores, false positives and negatives tuning, and automated feedback. The initial approach using natural language processing techniques to extract features achieved 86% accuracy during the training phase but suffered from overfitting and performed poorly on unseen datasets during testing. To address these issues, the study proposes using the abstract syntax tree (AST) for Java and C++ codebases to capture code semantics and structure and generate path-context representations for each function. The Code2Vec model architecture is used to learn distributed representations of source code snippets for training a machine-learning classifier for vulnerability prediction. The study evaluates the performance of the proposed methodology using two datasets and compares the results with existing approaches. The Devign dataset yielded 60% accuracy in predicting vulnerable code snippets and helped resist overfitting, while the Juliet Test Suite predicted specific vulnerabilities such as OS-Command Injection, Cryptographic, and Cross-Site Scripting vulnerabilities. The Code2Vec model achieved 75% accuracy and a 98% recall rate in predicting OS-Command Injection vulnerabilities. The study concludes that even partial AST representations of source code can be useful for vulnerability prediction. The approach has the potential for automated intelligent analysis of source code, including vulnerability prediction on unseen source code. State-of-the-art models using natural language processing techniques and CNN models with ensemble modelling techniques did not generalize well on unseen data and faced overfitting issues. However, predicting vulnerabilities in source code using machine learning poses challenges such as high dimensionality and complexity of source code, imbalanced datasets, and identifying specific types of vulnerabilities. Future work will address these challenges and expand the scope of the research.

Keywords: code embeddings, neural networks, natural language processing, OS command injection, software security, code properties

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Karen Eastman

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More youths and young adults with autism spectrum disorder (ASD) have been entering the workforce in recent years. Historically, students with ASD struggle after leaving high school and experience lower rates of employment, with social skills continuing to be the most problematic area of concern. Special education teachers may find it challenging to identify effective combinations of evidence-based practices (EBPs) and supports to best guide these students. One EBP, Peer Mediated Instruction and Intervention (PMII) has been well documented in the literature as being effective for younger students with autism but not researched as much with older students and adults, particularly in work settings. A need to combine PMII with other EBPs has been identified as a way to achieve a greater positive impact rather than any practice alone. A multiple baseline across skills design was used in this research project with two participants in different settings. PMII was combined with Visual Supports, with typical peers being trained in both practices. PMII is an evidence-based practice used to address social concerns by training peers without disabilities as to how they can provide feedback to and support, the student with ASD with social interactions in structured settings. The peers without disabilities were the instructors, while the adults facilitated the social situations and provided support to both the peers and students with ASD when needed. Because many individuals with ASD learn best with visual input, rather than using only the spoken word (verbal directions and feedback), Visual Supports were used in conjunction with PMII. Visual Supports can include written words, pictures, symbols, videos, or objects. In this project, the Visual Supports used were written social scripts, videos, Stop and Think signs, written reminder cards, a school map, and a pictorial task analysis of work tasks. Variables that may affect intervention outcomes in this project included attendance at school and school-based work settings for both the students with ASD and the peers without disabilities and behaviors and responses from others in the settings. Qualitative data was also collected from observations and surveys with peers about the process and their role. Data indicated that the students with ASD responded more positively to redirection and support from their peers than to teachers and staff and showed an increase in positive interactions with others. Those surveyed indicated a positive attitude toward and response to the use of peer interventions with visual supports.

Keywords: autism, social skills, vocational training, peer interventions

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Authors: A. Ham, A. Kuckert-Wostheinrich

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Demographic changes, like the ageing population in European countries and shortage of nursing staff, the increasing number of people with severe cognitive impairment, and elderly socially isolated people raise important questions about who will provide long-term care for ageing citizens. Due to the so-called refugee crisis in 2015, some health care institutions for ageing citizens in Europe invited first generation immigrants to start a nursing career and providing them language skills, nursing training, and internships. The aim of this ethnographic research was to explore the social processes affecting workforce integration and how newcomers enact good care in ageing citizens in a German nursing home. By ethnographic fieldwork, 200 hours of participant observations, 25 in-depth interviews with immigrants and established staff, 2 focus groups with 6 immigrants, and 6 established staff members, data were analysed. The health care institution provided the newcomers a nursing program on psychogeriatric theory and nursing skills in the psychogeriatric field and professional oriented language skills. Courses of health prevention and theater plays accompanied the training. The knowledge learned in education could be applied in internships on the wards. Additionally, diversity and inclusivity courses were given to established personal for cultural awareness and sensitivity. They learned to develop a collegial attitude of respect and appreciation, regardless of gender, nationality, ethnicity, religion or belief, age sexual orientation, or disability and identity. The qualitative data has shown that social processes affected workforce integration, like organizational constraints, staff shortages, and a demanding workload. However, zooming in on the interactions between newcomers and residents, we noticed how they tinkered to enact good care by embodied caring, playing games, singing and dancing. By situational acting and practical wisdom in nursing care, the newcomers could meet the needs of ageing residents. Thus, when health care institutions open up nursing programs for newcomers with refugees’ backgrounds and focus on talent instead of shortcomings, we might as well stimulate the unknown competencies, attitudes, skills, and expertise of newcomers and create excellent nurses for excellent care.

Keywords: established staff, Germany, nursing, refugees

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Authors: Stephanie Al Otaiba, Kyle Roberts

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Background and Significance Globally far too many students read below grade level; thus improving literacy outcomes is vital. Research suggests that non-cognitive factors, including Social and Emotional Learning (SEL) are linked to success in literacy outcomes. Converging evidence exists that early interventions are more effective than later remediation; therefore teachers need strategies to support early literacy while developing students’ SEL and their vocabulary, or language, for learning. This presentation describe findings from a US federally-funded project that trained teachers to provide an evidence-based read-aloud program for young children, using commercially available books with multicultural characters and themes to help their students “GROW”. The five GROW SEL themes include: “I can name my feelings”, “I can learn from my mistakes”, “I can persist”, “I can be kind to myself and others”, and “I can work toward and achieve goals”. Examples of GROW vocabulary (from over 100 words taught across the 5 units) include: emotions, improve, resilient, cooperate, accomplish, responsible, compassion, adapt, achieve, analyze. Methodology This study used a mixed methods research design, with qualitative methods to describe data from teacher feedback surveys (regarding satisfaction, feasibility), observations of fidelity of implementation, and with quantitative methods to assess the effect sizes for student vocabulary growth. GROW Intervention and Teacher Training Procedures Researchers trained classroom teachers to implement GROW. Each thematic unit included four books, vocabulary cards with images of the vocabulary words, and scripted lessons. Teacher training included online and in-person training; researchers incorporated virtual reality videos of instructors with child avatars to model lessons. Classroom teachers provided 2-3 20 min lessons per week ranging from short-term (8 weeks) to longer-term trials for up to 16 weeks. Setting and Participants The setting for the study included two large urban charter schools in the South. Data was collected across two years; during the first year, participants included 7 kindergarten teachers and 108 and the second year involved an additional set of 5 kindergarten and first grade teachers and 65 students. Initial Findings The initial qualitative findings indicate teachers reported the lessons to be feasible to implement and they reported that students enjoyed the books. Teachers found the vocabulary words to be challenging and important. They were able to implement lessons with fidelity. Quantitative analyses of growth for each taught word suggest that students’ growth on taught words ranged from large (ES = .75) to small (<.20). Researchers will contrast the effects for more and less successful books within the GROW units. Discussion and Conclusion It is feasible for teachers of young students to effectively teach SEL vocabulary and themes during shared book reading. Teachers and students enjoyed the books and students demonstrated growth on taught vocabulary. Researchers will discuss implications of the study and about the GROW program for researchers in learning sciences, will describe some limitations about research designs that are inherent in school-based research partnerships, and will provide some suggested directions for future research and practice.

Keywords: early literacy, learning science, language and vocabulary, social and emotional learning, multi-cultural

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Authors: Corey Clay

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A fraction of mechanical trainees (graduate students) from underrepresented groups (URM) has steadily increased through targeted recruitment and interventions to support their success during training. However, this trend has yet to translate to a connected increase in the number of faculty from these underrepresented groups. The purpose here is to look at proven strategies that departments and research institutions can develop to increase faculty hiring and promotion equity to address the lack of racial and gender diversity among their faculty. We will look at this process through an Adlerian lens, i.e., Adler theorized social interest as “a feeling of community, an orientation to living cooperatively with others, and a lifestyle that values the common good above one’s own interests and desires.” This abstract will look at implementing a cogent DEI strategy through an Adlerian perspective.

Keywords: diversity, equity, inclusion, adlerian

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Authors: Germans Natuhwera, Peter Ellis, Acuda Wilson, Anne Merriman, Martha Rabwoni

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Objective: The aim of the study was to diagnose the socio-economic burden and impact of a diagnosis of cervical cancer (CC) in rural women in the context of low-resourced country Uganda, using a phenomenological enquiry. Methods: This was a multi-site phenomenological inquiry, conducted at three hospice settings; Mobile Hospice Mbarara in southwestern, Little Hospice Hoima in Western, and Hospice Africa Uganda Kampala in central Uganda. A purposive sample of women with a histologically confirmed diagnosis of CC was recruited. Data was collected using open-ended audio-recorded interviews conducted in the native languages of participants. Interviews were transcribed verbatim in English, and Braun and Clarke’s (2019) framework of thematic analysis was used. Results: 13 women with a mean age of 49.2 and age range 29-71 participated in the study. All participants were of low socioeconomic status. The majority (84.6%) had advanced disease at diagnosis. A fuller reading of transcripts produced four major themes clustered under; (1) socioeconomic characteristics of women, (2) impact of CC on women’s relationships, (3) disrupted and impaired activities of daily living (ADLs), and (4) economic disruptions. Conclusions: A diagnosis of CC introduces significant socio-economic disruptions in a woman’s and her family’s life. CC causes disability, impairs the woman and her family’s productivity hence exacerbating levels of poverty in the home. High and expensive out-of-pocket expenditure on treatment, investigations, and transport costs further compound the socio-economic burden. Decentralizing cancer care services to regional centers, scaling up screening services, subsidizing costs of cancer care services, or making cervical cancer care treatment free of charge, strengthening monitoring mechanisms in public facilities to curb the vice of healthcare workers soliciting bribes from patients, increased mass awareness campaigns about cancer, training more healthcare professionals in cancer investigation and management, and palliative care, and introducing an introductory course on gynecologic cancers into all health training institutions are recommended.

Keywords: activities of daily living, cervical cancer, out-of-pocket, expenditure, phenomenology, socioeconomic

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Authors: Zoja Raud, Valery Vodovozov

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Modelling and simulation provide effective way to acquire engineering experience. An active approach to modelling and simulation proposed in the paper involves, beside the compulsory part directed by the traditional step-by-step instructions, the new optional part basing on the human’s habits to design thus stimulating the efforts towards success in active learning. Computer exercises as a part of engineering curriculum incorporate a set of effective activities. In addition to the knowledge acquired in theoretical training, the described educational arrangement helps to develop problem solutions, computation skills, and experimentation performance along with enhancement of practical experience and qualification.

Keywords: modelling, simulation, engineering education, electronics, active learning

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Authors: Ivana Scidà, Francesco Alotto, Anna Osello

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Due to the importance given today to innovation, the education sector is evolving thanks digital technologies. Virtual Reality (VR) can be a potential teaching tool offering many advantages in the field of training and education, as it allows to acquire theoretical knowledge and practical skills using an immersive experience in less time than the traditional educational process. These assumptions allow to lay the foundations for a new educational environment, involving and stimulating for students. Starting from the objective of strengthening the innovative teaching offer and the learning processes, the case study of the research concerns the digitalization of MastrLAB, High Quality Laboratory (HQL) belonging to the Department of Structural, Building and Geotechnical Engineering (DISEG) of the Polytechnic of Turin, a center specialized in experimental mechanical tests on traditional and innovative building materials and on the structures made with them. The MastrLAB-VR has been developed, a revolutionary innovative training tool designed with the aim of educating the class in total safety on the techniques of use of machinery, thus reducing the dangers arising from the performance of potentially dangerous activities. The virtual laboratory, dedicated to the students of the Building and Civil Engineering Courses of the Polytechnic of Turin, has been projected to simulate in an absolutely realistic way the experimental approach to the structural tests foreseen in their courses of study: from the tensile tests to the relaxation tests, from the steel qualification tests to the resilience tests on elements at environmental conditions or at characterizing temperatures. The research work proposes a methodology for the virtualization of technical laboratories through the application of Building Information Modelling (BIM), starting from the creation of a digital model. The process includes the creation of an independent application, which with Oculus Rift technology will allow the user to explore the environment and interact with objects through the use of joypads. The application has been tested in prototype way on volunteers, obtaining results related to the acquisition of the educational notions exposed in the experience through a virtual quiz with multiple answers, achieving an overall evaluation report. The results have shown that MastrLAB-VR is suitable for both beginners and experts and will be adopted experimentally for other laboratories of the University departments.

Keywords: building information modelling, digital learning, education, virtual laboratory, virtual reality

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Authors: Mohamed Abd Esselem Dems, Souhila Laib, Nadjia Latelli, Nadia Ouddai

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In this work, we have developed QSAR model for Relative Binding Affinity (RBA) of a large diverse set of estradiol among these derivatives, the organometallic derivatives. By dividing the dataset into a training set of 24 compounds and a test set of 6 compounds. The DFT method was used to calculate quantum chemical descriptors and physicochemical descriptors (MR and MLOGP) were performed using E-Dragon. All the validations indicated that the QSAR model built was robust and satisfactory (R2 = 90.12, Q2LOO = 86.61, RMSE = 0.272, F = 60.6473, Q2ext =86.07). We have therefore apply this model to predict the RBA, for two isomers β and α wherein Mn(CO)3 complex with the aromatic ring of estradiol, and the two isomers show little appreciation for the estrogenic receptor (RBAβ = 1.812 and RBAα = 1.741).

Keywords: DFT, estradiol, haptotropic rearrangement, QSAR, relative binding affinity

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Authors: Malizgani Paul Chavula

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Background: Comprehensive sexuality education (CSE) plays a critical role in promoting youth and adolescents’ sexual and reproductive health and well-being. However, little is known about the enablers and barriers affecting the integration of CSE into educational programmes. The aim of this review is to explore positive and negative factors influencing the integration of CSE into national curricula and educational systems in low- and middle-income countries. Methods: We conducted a systematic literature review (January 2010 to August 2022). The results accord with the Preferred Reporting Items for Systematic Reviews and Meta-analysis standards for systematic reviews. Data were retrieved from the PubMed, Cochrane, Google Scholar, and Web of Hinari databases. The search yielded 431 publications, of which 23 met the inclusion criteria for full-text screening. The review is guided by an established conceptual framework that incorporates the integration of health innovations into health systems. Data were analyzed using a thematic synthesis approach. Results: The magnitude of the problem is evidenced by sexual and reproductive health challenges such as high teenage pregnancies, early marriages, and sexually transmitted infections. Awareness of these challenges can facilitate the development of interventions and the implementation and integration of CSE. Reported aspects of the interventions include core CSE content, delivery methods, training materials and resources, and various teacher-training factors. Reasons for adoption include perceived benefits of CSE, experiences and characteristics of both teachers and learners, and religious, social, and cultural factors. Broad system characteristics include strengthening links between schools and health facilities, school and community-based collaboration, coordination of CSE implementation, and the monitoring and evaluation of CSE. Ultimately, the availability of resources, national policies and laws, international agendas, and political commitment will impact upon the extent and level of integration. Conclusion: Social, economic, cultural, political, legal, and financial contextual factors influence the implementation and integration of CSE into national curricula and educational systems. Stakeholder collaboration and involvement in the design and appropriateness of interventions is critical.

Keywords: comprehensive sexuality education, factors, integration, sexual reproductive health rights

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Authors: Ulrich Wake, Eniman Syamsuddin

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The model for camera model identificaiton is trained using pretrained model ResNet43 and ResNet50. The dataset consists of 500 photos of each phone. Dataset is divided into 1280 photos for training, 320 photos for validation and 400 photos for testing. The model is trained using One Cycle Policy Method and tested using Test-Time Augmentation. Furthermore, the model is trained for 50 epoch using regularization such as drop out and early stopping. The result is 90% accuracy for validation set and above 85% for Test-Time Augmentation using ResNet50. Every model is also trained by slightly updating the pretrained model’s weights

Keywords: ​ One Cycle Policy, ResNet34, ResNet50, Test-Time Agumentation

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Bingquan Shen

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Convolutional Neural Networks (CNN) have demonstrated their effectiveness in synthesizing 3D views of object instances at various viewpoints. Given the problem where one have limited viewpoints of a particular object for classification, we present a pose normalization architecture to transform the object to existing viewpoints in the training dataset before classification to yield better classification performance. We have demonstrated that this Pose Normalization Network (PNN) can capture the style of the target object and is able to re-render it to a desired viewpoint. Moreover, we have shown that the PNN improves the classification result for the 3D chairs dataset and ShapeNet airplanes dataset when given only images at limited viewpoint, as compared to a CNN baseline.

Keywords: convolutional neural networks, object classification, pose normalization, viewpoint invariant

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Authors: Chana Pavneet

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Background: In more recent years in dentistry, a greater emphasis has been placed on competency-based education (CBE) programmes. Undergraduate and postgraduate curriculums have been reformed to reflect these changes, and adopting a CBE approach has shown to be beneficial to trainees and places an emphasis on continuous lifelong learning. The literature is vast; however, very little work has been done specifically to the assessment of competence in dentistry and even less so in oral surgery. The majority of the literature tends to opinion pieces. Some small-scale studies have been undertaken in this area researching assessment tools which can be used to assess competence in oral surgery. However, there is a lack of general consensus on the preferable assessment methods. The aim of this review is to identify the assessment methods available and their usefulness. Methods: Electronic databases (Medline, Embase, and the Cochrane Database of systematic reviews) were searched. PRISMA guidelines were followed to identify relevant papers. Abstracts of studies were reviewed, and if they met the inclusion criteria, they were included in the review. Papers were reviewed against the critical appraisal skills programme (CASP) checklist and medical education research quality instrument (MERQSI) to assess their quality and identify any bias in a systematic manner. The validity and reliability of each assessment method or tool were assessed. Results: A number of assessment methods were identified, including self-assessment, peer assessment, and direct observation of skills by someone senior. Senior assessment tended to be the preferred method, followed by self-assessment and, finally, peer assessment. The level of training was shown to affect the preferred assessment method, with one study finding peer assessment more useful in postgraduate trainees as opposed to undergraduate trainees. Numerous tools for assessment were identified, including a checklist scale and a global rating scale. Both had their strengths and weaknesses, but the evidence was more favourable for global rating scales in terms of reliability, applicability to more clinical situations, and easier to use for examiners. Studies also looked into trainees’ opinions on assessment tools. Logbooks were not found to be significant in measuring the competence of trainees. Conclusion: There is limited literature exploring the methods and tools which assess the competence of oral surgery trainees. Current evidence shows that the most favourable assessment method and tool may differ depending on the stage of training. More research is required in this area to streamline assessment methods and tools.

Keywords: competence, oral surgery, assessment, trainees, education

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Authors: Ya-Hui Wang

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Lower speech therapy rate of tracheostomized patient was noted in comparison with previous researches. This study is aim to shed light on the referral status of speech therapy in those patients in Taiwan. This study developed an analysis for the size and key characteristics of the population of tracheostomized in-patient in the Taiwan. Method: We analyzed National Healthcare Insurance data (The Collaboration Center of Health Information Application, CCHIA) from Jan 1 2010 to Dec 31 2010. Result: over ages 3, number of tracheostomized in-patient is directly proportional to age. A high service loading was observed in North region in comparison with other regions. Only 4.87% of the tracheostomized in-patients were referred for speech therapy, and 1.9% for swallow examination, 2.5% for communication evaluation.

Keywords: refer, speech therapy, training, rehabilitation

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Authors: Natalia Uribe Corrales, María Fernanda Benavides Erazo, Santiago Henao Villegas

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Introduction: Transport is a crucial link in the porcine chain because it is considered a stressful event in the animal, due to it is a new environment, which generates new interactions, together with factors such as speed, noise, temperature changes, vibrations, deprivation of food and water. Therefore, inadequate handling at this stage can lead to bruises, musculoskeletal injuries, fatigue, and mortality, resulting in canal seizures and economic losses. Objective: To characterize the transport and driving practices for the mobilization of standing pigs directed to slaughter plants in the Valle de Aburrá, Antioquia, Colombia in 2017. Methods: A descriptive cross-sectional study was carried out with the transporters arriving at the slaughterhouses approved by National Institute for Food and Medicine Surveillance (INVIMA) during 2017 in the Valle de Aburrá. The process of obtaining the samples was made from probabilistic sampling. Variables such as journey time, mechanical technical certificate, training in animal welfare, driving speed, material, and condition of floors and separators, supervision of animals during the trip, load density and mortality were analyzed. It was approved by the ethics committee for the use and care of animals CICUA of CES University, Act number 14 of 2015. Results: 190 trucks were analyzed, finding that 12.4% did not have updated mechanical technical certificate; the transporters experience in pig’s transportation was an average of 9.4 years (d.e.7.5). The 85.8% reported not having received training in animal welfare. Other results were that the average speed was 63.04km/hr (d.e 13.46) and the 62% had floors in good condition; nevertheless, the 48% had bad conditions on separators. On the other hand, the 88% did not supervise their animals during the journey, although the 62.2% had an adequate loading density, in relation to the average mortality was 0.2 deaths/travel (d.e. 0.5). Conclusions: Trainers should be encouraged on issues such as proper maintenance of vehicles, animal welfare, obligatory review of animals during mobilization and speed of driving, as these poorly managed indicators generate stress in animals, increasing generation of injuries as well as possible accidents; also, it is necessary to continue to improve aspects such as aluminum floors and separators that favor easy cleaning and maintenance, as well as the appropriate handling in the density of load that generates animal welfare.

Keywords: animal welfare, driving practices, pigs, truck infrastructure

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Authors: Tolulope Aremu

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This paper is based on the idea of using deep learning methodology for optimizing production yield by tuning a few key process parameters in a manufacturing environment. The study was explicitly on how to maximize production yield and minimize operational costs by utilizing advanced neural network models, specifically Long Short-Term Memory and Convolutional Neural Networks. These models were implemented using Python-based frameworks—TensorFlow and Keras. The targets of the research are the precision molding processes in which temperature ranges between 150°C and 220°C, the pressure ranges between 5 and 15 bar, and the material flow rate ranges between 10 and 50 kg/h, which are critical parameters that have a great effect on yield. A dataset of 1 million production cycles has been considered for five continuous years, where detailed logs are present showing the exact setting of parameters and yield output. The LSTM model would model time-dependent trends in production data, while CNN analyzed the spatial correlations between parameters. Models are designed in a supervised learning manner. For the model's loss, an MSE loss function is used, optimized through the Adam optimizer. After running a total of 100 training epochs, 95% accuracy was achieved by the models recommending optimal parameter configurations. Results indicated that with the use of RSM and DOE traditional methods, there was an increase in production yield of 12%. Besides, the error margin was reduced by 8%, hence consistent quality products from the deep learning models. The monetary value was annually around $2.5 million, the cost saved from material waste, energy consumption, and equipment wear resulting from the implementation of optimized process parameters. This system was deployed in an industrial production environment with the help of a hybrid cloud system: Microsoft Azure, for data storage, and the training and deployment of their models were performed on Google Cloud AI. The functionality of real-time monitoring of the process and automatic tuning of parameters depends on cloud infrastructure. To put it into perspective, deep learning models, especially those employing LSTM and CNN, optimize the production yield by fine-tuning process parameters. Future research will consider reinforcement learning with a view to achieving further enhancement of system autonomy and scalability across various manufacturing sectors.

Keywords: production yield optimization, deep learning, tuning of process parameters, LSTM, CNN, precision manufacturing, TensorFlow, Keras, cloud infrastructure, cost saving

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Authors: Javier López

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This Paper presents the evaluation model designed for the virtual education branch of The “Universidad Manuela Beltrán, Bogotá-Colombia”. This was the result of a research, developed as a case study, which had three stages: Document review, observation, and a perception survey for teachers. In the present model, the evaluation is a cross-cutting issue to the educational process. Therefore, it consists in a group of actions and guidelines which lead to analyze the student’s learning process from the admission, during the academic training, and to the graduation. This model contributes to the evaluation components which might interest other educational institutions or might offer methodological guidance to consolidate an own model

Keywords: model, evaluation, virtual education, learning process

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Authors: Jennifer Leach, Umashanger Thayasivam

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The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

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Authors: Lewis Mehl-Madrona, Barbara Mainguy

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We describe the type of counseling done by traditional cultural healers in North America. We follow an autoethnographic course development through the first author’s integration of mainstream training and Native-American heritage and study with traditional medicine people. We assemble traditional healing elders from North America and discuss with them their practices and their philosophies of healing. We draw parallels for their approaches in some European-based philosophies and religion, including the work of Heidegger, Levin, Fox, Kierkegaard, and others. An example of the treatment process with a depressed client is provided and similarities and differences with conventional psychotherapies are described.

Keywords: indigenous approaches to counseling, indigenous bodywork, indigenous healing, North American indigenous people

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Authors: Eiman Kattan, Hong Wei

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In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

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Authors: Choi Sang Long

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It is vital for Human Resource (HR) managers to address and overcome employees’ turnover intention in their organization. Ability to keep good employees is critical for ensuring success of the organization in future. People are seeking many ways of live that is meaningful and less complicated and this new lifestyle actually has an impact on how an employee must be motivated and managed. Therefore, this paper discusses extensively on the impact of human resource practices that can alter the negative effect on the organization due to high employees’ turnover. These critical practices are employees’ career development, performance management, training and a fair compensation scheme.

Keywords: turnover intention, career development, performance management, compensation, human resource management, organization

Procedia PDF Downloads 493

Authors: David Ofosu-Hamilton, John K. E. Edumadze

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Universities have come to recognize the role Information and Communication Technology (ICT) skills plays in the daily activities of tertiary students. The ability to use ICT – essentially, computers and their diverse applications – are important resources that influence an individual’s economic and social participation and human capital development. Our society now increasingly relies on the Internet, and the Cloud as a means to communicate and disseminate information. The educated individual should, therefore, be able to use ICT to create and share knowledge that will improve society. It is, therefore, important that universities require incoming students to demonstrate a level of computer proficiency or trained to do so at a minimal cost by deploying advanced educational technologies. The training and standardized assessment of all in-coming first-year students of the University of Cape Coast in Information Technology Skills (ITS) have become a necessity as students’ most often than not highly overestimate their digital skill and digital ignorance is costly to any economy. The one-semester course is targeted at fresh students and aimed at enhancing the productivity and software skills of students. In this respect, emphasis is placed on skills that will enable students to be proficient in using Microsoft Office and Google Apps for Education for their academic work and future professional work whiles using emerging digital multimedia technologies in a safe, ethical, responsible, and legal manner. The course is delivered in blended mode - online and self-paced (student centered) using Alison’s free cloud-based tutorial (Moodle) of Microsoft Office videos. Online support is provided via discussion forums on the University’s Moodle platform and tutor-directed and assisted at the ICT Centre and Google E-learning laboratory. All students are required to register for the ITS course during either the first or second semester of the first year and must participate and complete it within a semester. Assessment focuses on Alison online assessment on Microsoft Office, Alison online assessment on ALISON ABC IT, Peer assessment on e-portfolio created using Google Apps/Office 365 and an End of Semester’s online assessment at the ICT Centre whenever the student was ready in the cause of the semester. This paper, therefore, focuses on the digital culture approach of hybrid teaching, learning and paperless examinations and the possible adoption by other courses or programs at the University of Cape Coast.

Keywords: assessment, blended, cloud, paperless

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Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun

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Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Keywords: machine learning, pattern clustering, pooling, classification

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Authors: Vladimira Hornackova, Jana Kottasova, Zuzana Vanova, Anna Jungrova

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Present study deals with drama education in preschool education. The research made in this field brings a qualitative comparative survey with the aim to find out the use of methods and techniques of drama education in preschool education at university or secondary school graduate preschool teachers. The research uses a content analysis and an unstandardized questionnaire for preschool teachers and obtained data are processed with the help of descriptive methods and correlations. The results allow a comparison of aspects applied through drama in preschool education. The research brings impulses for education improvement in kindergartens and inspiration for university study programs of drama education in the professional training of preschool teachers.

Keywords: drama education, preschool education, preschool teacher, research

Procedia PDF Downloads 365

Authors: Ankur Chaudhuri, Sibani Sen Chakraborty

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In human, glutaminyl cyclase activity is highly abundant in neuronal and secretory tissues and is preferentially restricted to hypothalamus and pituitary. The N-terminal modification of β-amyloids (Aβs) peptides by the generation of a pyro-glutamyl (pGlu) modified Aβs (pE-Aβs) is an important process in the initiation of the formation of neurotoxic plaques in Alzheimer’s disease (AD). This process is catalyzed by glutaminyl cyclase (QC). The expression of QC is characteristically up-regulated in the early stage of AD, and the hallmark of the inhibition of QC is the prevention of the formation of pE-Aβs and plaques. A computer-aided drug design (CADD) process was employed to give an idea for the designing of potentially active compounds to understand the inhibitory potency against human glutaminyl cyclase (QC). This work elaborates the ligand-based and structure-based pharmacophore exploration of glutaminyl cyclase (QC) by using the known inhibitors. Three dimensional (3D) quantitative structure-activity relationship (QSAR) methods were applied to 154 compounds with known IC50 values. All the inhibitors were divided into two sets, training-set, and test-sets. Generally, training-set was used to build the quantitative pharmacophore model based on the principle of structural diversity, whereas the test-set was employed to evaluate the predictive ability of the pharmacophore hypotheses. A chemical feature-based pharmacophore model was generated from the known 92 training-set compounds by HypoGen module implemented in Discovery Studio 2017 R2 software package. The best hypothesis was selected (Hypo1) based upon the highest correlation coefficient (0.8906), lowest total cost (463.72), and the lowest root mean square deviation (2.24Å) values. The highest correlation coefficient value indicates greater predictive activity of the hypothesis, whereas the lower root mean square deviation signifies a small deviation of experimental activity from the predicted one. The best pharmacophore model (Hypo1) of the candidate inhibitors predicted comprised four features: two hydrogen bond acceptor, one hydrogen bond donor, and one hydrophobic feature. The Hypo1 was validated by several parameters such as test set activity prediction, cost analysis, Fischer's randomization test, leave-one-out method, and heat map of ligand profiler. The predicted features were then used for virtual screening of potential compounds from NCI, ASINEX, Maybridge and Chembridge databases. More than seven million compounds were used for this purpose. The hit compounds were filtered by drug-likeness and pharmacokinetics properties. The selective hits were docked to the high-resolution three-dimensional structure of the target protein glutaminyl cyclase (PDB ID: 2AFU/2AFW) to filter these hits further. To validate the molecular docking results, the most active compound from the dataset was selected as a reference molecule. From the density functional theory (DFT) study, ten molecules were selected based on their highest HOMO (highest occupied molecular orbitals) energy and the lowest bandgap values. Molecular dynamics simulations with explicit solvation systems of the final ten hit compounds revealed that a large number of non-covalent interactions were formed with the binding site of the human glutaminyl cyclase. It was suggested that the hit compounds reported in this study could help in future designing of potent inhibitors as leads against human glutaminyl cyclase.

Keywords: glutaminyl cyclase, hit lead, pharmacophore model, simulation

Procedia PDF Downloads 131

Authors: Racheli Magnezi, Inbal Gazit, Michal Rassin, Joseph Barr, Orna Tal

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An effective process for transmitting patient critical information is essential for patient safety and for improving communication among healthcare staff. Previous studies have discussed handover tools such as SBAR (Situation, Background, Assessment, Recommendation) or SOFI (Short Observational Framework for Inspection). Yet, these formats lack flexibility, and require special training. In addition, nurses and physicians have different procedures for handing over information. The objectives of this study were to establish a universal, structured tool for handover, for both physicians and nurses, based on parameters that were defined as ‘important’ and ‘appropriate’ by the medical team, and to implement this tool in various hospital departments, with flexibility for each ward. A questionnaire, based on established procedures and on the literature, was developed to assess attitudes towards the most important information for effective handover between shifts (Cronbach's alpha 0.78). It was distributed to 150 senior physicians and nurses in 62 departments. Among senior medical staff, 12 physicians and 66 nurses responded to the questionnaire (52% response rate). Based on the responses, a handover form suitable for all hospital departments was designed and implemented. Important information for all staff included: Patient demographics (full name and age); Health information (diagnosis or patient complaint, changes in hemodynamic status, new medical treatment or equipment required); and Social Information (suspicion of violence, mental or behavioral changes, and guardianship). Additional information relevant to each unit included treatment provided, laboratory or imaging required, and change in scheduled surgery in surgical departments. ICU required information on background illnesses, Pediatrics required information on diet and food provided and Obstetrics required the number of days after cesarean section. Based on the model described, a flexible tool was developed that enables handover of both common and unique information. In addition, it includes general logistic information that must be transmitted to the next shift, such as planned disruptions in service or operations, staff training, etc. Development of a simple, clear, comprehensive, universal, yet flexible tool designed for all medical staff for transmitting critical information between shifts was challenging. Physicians and nurses found it useful and it was widely implemented. Ongoing research is needed to examine the efficiency of this tool, and whether the enthusiasm that accompanied its initial use is maintained.

Keywords: handover, nurses, hospital, critical information

Procedia PDF Downloads 248

Authors: Najeebullah Soomro, Habib Noorbhai, Mariam Soomro, Ross Sanders

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The demands on cricketers are increasing with more matches being played in a shorter period of time with a greater intensity. A ten year report on injury incidence for Australian elite cricketers between the 2000- 2011 seasons revealed an injury incidence rate of 17.4%.1. In the 2009–10 season, 24 % of Australian fast bowlers missed matches through injury. 1 Injury rates are even higher in junior cricketers with an injury incidence of 25% or 2.9 injuries per 100 player hours reported. 2 Traditionally, injury surveillance has relied on the use of paper based forms or complex computer software. 3,4 This makes injury reporting laborious for the staff involved. The purpose of this presentation is to describe a smartphone based mobile application as a means of improving injury surveillance in cricket. Methods: The researchers developed CricPredict mobile App for the Android platforms, the world’s most widely used smartphone platform. It uses Qt SDK (Software Development Kit) as IDE (Integrated Development Environment). C++ was used as the programming language with the Qt framework, which provides us with cross-platform abilities that will allow this app to be ported to other operating systems (iOS, Mac, Windows) in the future. The wireframes (graphic user interface) were developed using Justinmind Prototyper Pro Edition Version (Ver. 6.1.0). CricPredict enables recording of injury and training status conveniently and immediately. When an injury is reported automated follow-up questions include site of injury, nature of injury, mechanism of injury, initial treatment, referral and action taken after injury. Direct communication with the player then enables assessment of severity and diagnosis. CricPredict also allows the coach to maintain and track each player’s attendance at matches and training session. Workload data can also be recorded by either the player or coach by recording the number of balls bowled or played in a day. This is helpful in formulating injury rates and time lost due to injuries. All the data are stored at a secured password protected data server. Outcomes and Significance: Use of CricPredit offers a simple, user friendly tool for the coaching or medical staff associated with teams to predict, record and report injuries. This system will assist teams to capture injury data with ease thus allowing better understanding of injuries associated with cricket and potentially optimize the performance of such cricketers.

Keywords: injury, cricket, surveillance, smartphones, mobile

Procedia PDF Downloads 459

Authors: Jiahao Cui

Abstract:

In the past centuries, due to climate change and intense whaling, the global whale population has dramatically declined. Among the various whale species, the fin whale experienced the most drastic drop in number due to its popularity in whaling. Under this background, identifying fin whale calls could be immensely beneficial to the preservation of the species. This paper uses feature extraction to process the input audio signal, then a network based on AlexNet and three networks based on the ResNet model was constructed to classify fin whale calls. A mixture of the DOSITS database and the Watkins database was used during training. The results demonstrate that a modified ResNet network has the best performance considering precision and network complexity.

Keywords: convolutional neural network, ResNet, AlexNet, fin whale preservation, feature extraction

Procedia PDF Downloads 123