Search results for: HFR video analysis

Authors: Juliza Hidayati, Sawarni Hasibuan

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PT. XYZ is a manufacturing company that produces Crude Palm Oil (CPO). The fierce competition in the global markets not only between companies but also a competition between supply chains. This research aims to analyze the supply chain and value chain of Crude Palm Oil (CPO) in the company. Data analysis method used is qualitative analysis and quantitative analysis. The qualitative analysis describes supply chain and value chain, while the quantitative analysis is used to find out value added and the establishment of the value chain. Based on the analysis, the value chain of crude palm oil (CPO) in the company consists of four main actors that are suppliers of raw materials, processing, distributor, and customer. The value chain analysis consists of two actors; those are palm oil plantation and palm oil processing plant. The palm oil plantation activities include nurseries, planting, plant maintenance, harvesting, and shipping. The palm oil processing plant activities include reception, sterilizing, thressing, pressing, and oil classification. The value added of palm oil plantations was 72.42% and the palm oil processing plant was 10.13%.

Keywords: palm oil, value chain, value added, supply chain

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Authors: Alexiou Dimitra, Fragkaki Maria

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The objective of the paper is the study of geographic, economic and educational variables and their contribution to determine the position of each member-state among the EU-28 countries based on the values of seven variables as given by Eurostat. The Data Analysis methods of Multiple Factorial Correspondence Analysis (MFCA) Principal Component Analysis and Factor Analysis have been used. The cross tabulation tables of data consist of the values of seven variables for the 28 countries for 2014. The data are manipulated using the CHIC Analysis V 1.1 software package. The results of this program using MFCA and Ascending Hierarchical Classification are given in arithmetic and graphical form. For comparison reasons with the same data the Factor procedure of Statistical package IBM SPSS 20 has been used. The numerical and graphical results presented with tables and graphs, demonstrate the agreement between the two methods. The most important result is the study of the relation between the 28 countries and the position of each country in groups or clouds, which are formed according to the values of the corresponding variables.

Keywords: Multiple Factorial Correspondence Analysis, Principal Component Analysis, Factor Analysis, E.U.-28 countries, Statistical package IBM SPSS 20, CHIC Analysis V 1.1 Software, Eurostat.eu Statistics

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: J. R. Wang, W. Y. Cheng, J. S. Yeh, S. W. Chen, Y. M. Ferng, J. H. Yang, W. S. Hsu, C. Shih

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To understand the risk for dry storage concrete casks in the cask loading, transfer, and storage phase, the purpose of this research is to establish the probabilistic risk assessment (PRA) analysis methodology for dry storage concrete casks by using SAPHIRE 8 code. This analysis methodology is used to perform the study of Taiwan nuclear power plants (NPPs) dry storage system. The process of research has three steps. First, the data of the concrete casks and Taiwan NPPs are collected. Second, the PRA analysis methodology is developed by using SAPHIRE 8. Third, the PRA analysis is performed by using this methodology. According to the analysis results, the maximum risk is the multipurpose canister (MPC) drop case.

Keywords: PRA, dry storage, concrete cask, SAPHIRE

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Authors: Shyam Mehraaj, Samanthi E. R. Siriwardana, Shehara A. K. G. H., Wanigasinghe N. T., Wandana R. A. K., Wedage C. V.

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A presentation is a critical tool for conveying not just spoken information but also a wide spectrum of human emotions. The single most effective thing to make the presentation successful is to practice it beforehand. Preparing for a presentation has been shown to be essential for improving emotional control, intonation and prosody, pronunciation, and vocabulary, as well as the quality of the presentation slides. As a result, practicing has become one of the most critical parts of giving a good presentation. In this research, the main focus is to analyze the audio, video, and slides of the presentation uploaded by the presenters. This proposed solution is based on the Natural Language Processing and Computer Vision techniques to cater to the requirement for the presenter to do a presentation beforehand using a mobile responsive web application. The proposed system will assist in practicing the presentation beforehand by identifying the presenters’ emotions, body language, tonality, prosody, pronunciations and vocabulary, and presentation slides quality. Overall, the system will give a rating and feedback to the presenter about the performance so that the presenters’ can improve their presentation skills.

Keywords: presentation, self-evaluation, natural learning processing, computer vision

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Authors: C. Treerattanaphan, P. Boonpramuk, P. Singla

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Frequent, continuous speech training has proven to be a necessary part of a successful speech therapy process, but constraints of traveling time and employment dispensation become key obstacles especially for individuals living in remote areas or for dependent children who have working parents. In order to ameliorate speech difficulties with ample guidance from speech therapists, a website has been developed that supports speech therapy and training for people with articulation disorders in the standard Thai language. This web-based program has the ability to record speech training exercises for each speech trainee. The records will be stored in a database for the speech therapist to investigate, evaluate, compare and keep track of all trainees’ progress in detail. Speech trainees can request live discussions via video conference call when needed. Communication through this web-based program facilitates and reduces training time in comparison to walk-in training or appointments. This type of training also allows people with articulation disorders to practice speech lessons whenever or wherever is convenient for them, which can lead to a more regular training processes.

Keywords: web-based remote training program, Thai speech therapy, articulation disorders, speech booster

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Authors: Jeong-Hyun Park, Young-Sik Park, Hyo-Teag Jung

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This paper presents static analysis deployment model for code quality on R&D Projects of SW Development. The proposed model includes the scope of R&D projects and index for static analysis of source code, operation model and execution process, environments and infrastructure system for R&D projects of SW development. There is the static analysis result of pilot project as case study based on the proposed deployment model and environment, and strategic considerations for success operation of the proposed static analysis deployment model for R&D Projects of SW Development. The proposed static analysis deployment model in this paper will be adapted and improved continuously for quality upgrade of R&D projects, and customer satisfaction of developed source codes and products.

Keywords: static analysis, code quality, coding rules, automation tool

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Authors: Akram Sadat Jafari Roodbandi, Alireza Choobineh, Nazanin Hosseini, Vafa Feyzi

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Introduction: Physical fitness and optimum functional movement are essential for efficiently performing job tasks without fatigue and injury. Functional Movement Screening (FMS) tests are used in screening of athletes and military forces. Nurses and emergency medical staff are obliged to perform many physical activities such as transporting patients, CPR operations, etc. due to the nature of their jobs. This study aimed to assess relationship between FMS test score and the prevalence of musculoskeletal disorders (MSDs) in emergency nurses and emergency medical services (EMS) staff. Methods: 134 male and female emergency nurses and EMS technicians participated in this cross-sectional, descriptive-analytical study. After video tutorial and practical training of how to do FMS test, the participants carried out the test while they were wearing comfortable clothes. The final score of the FMS test ranges from 0 to 21. The score of 14 is considered weak in the functional movement base on FMS test protocol. In addition to the demographic data questionnaire, the Nordic musculoskeletal questionnaire was also completed for each participant. SPSS software was used for statistical analysis with a significance level of 0.05. Results: Totally, 49.3% (n=66) of the subjects were female. The mean age and work experience of the subjects were 35.3 ± 8.7 and 11.4 ± 7.7, respectively. The highest prevalence of MSDs was observed at the knee and lower back with 32.8% (n=44) and 23.1% (n=31), respectively. 26 (19.4%) health worker had FMS test score of 14 and less. The results of the Spearman correlation test showed that the FMS test score was significantly associated with MSDs (r=-0.419, p < 0.0001). It meant that MSDs increased with the decrease of the FMS test score. Age, sex, and MSDs were the remaining significant factors in linear regression logistic model with dependent variable of FMS test score. Conclusion: FMS test seems to be a usable screening tool in pre-employment and periodic medical tests for occupations that require physical fitness and optimum functional movements.

Keywords: functional movement, musculoskeletal disorders, health care worker, screening test

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Authors: Ju-young Hwang, Hyo-Gyoung Kwak, Hong Jae Yim

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This paper introduces a numerical analysis method for reinforced-concrete (RC) structures exposed to fire and compares the result with experimental results. The proposed analysis method for RC structure under the high temperature consists of two procedures. First step is to decide the temperature distribution across the section through the heat transfer analysis by using the time-temperature curve. After determination of the temperature distribution, the nonlinear analysis is followed. By considering material and geometrical non-linearity with the temperature distribution, nonlinear analysis predicts the behavior of RC structure under the fire by the exposed time. The proposed method is validated by the comparison with the experimental results. Finally, Prediction model to describe the status of after-cooling concrete can also be introduced based on the results of additional experiment. The product of this study is expected to be embedded for smart structure monitoring system against fire in u-City.

Keywords: RC structures, heat transfer analysis, nonlinear analysis, after-cooling concrete model

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Authors: Mrigakshi Das

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The private sector investments and quantum of money required in this sector critically hinges on the financial risk and returns the sector offers to providers of capital. Therefore, it becomes important to understand financial performance of hospitals. Financial Analysis is useful for decision makers in a variety of settings. Consider the small proprietary hospitals, say, Physicians Clinic. The managers of such clinic need the information that financial statements provide. Attention to Financial Statements of healthcare Organizations can provide answers to questions like: How are they doing? What is their rate of profit? What is their solvency and liquidity position? What are their sources and application of funds? What is their Operational Efficiency? The researcher has studied Financial Statements of 5 Private Healthcare Organizations in Guwahati City.

Keywords: not-for-profit organizations, financial analysis, ratio analysis, profitability analysis, liquidity analysis, operational efficiency, capital structure analysis

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Authors: Nazish Saleem Abbas, Muhammad Haris Jamil, Hamid Sharif

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Digital watermarking is a technique that allows an individual to add and hide secret information, copyright notice, or other verification message inside a digital audio, video, or image. Today, with the advancement of technology, modern healthcare systems manage patients’ diagnostic information in a digital way in many countries. When transmitted between hospitals through the internet, the medical data becomes vulnerable to attacks and requires security and confidentiality. Digital watermarking techniques are used in order to ensure the authenticity, security and management of medical images and related information. This paper proposes a watermarking technique that embeds a watermark in medical images imperceptibly and securely. In this work, digital watermarking on medical images is carried out using the Least Significant Bit (LSB) with the Discrete Cosine Transform (DCT). The proposed methods of embedding and extraction of a watermark in a watermarked image are performed in the frequency domain using LSB by XOR operation. The quality of the watermarked medical image is measured by the Peak signal-to-noise ratio (PSNR). It was observed that the watermarked medical image obtained performing XOR operation between DCT and LSB survived compression attack having a PSNR up to 38.98.

Keywords: watermarking, image processing, DCT, LSB, PSNR

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Authors: Sanjay Adhikesaven

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Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open-source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

Keywords: computer vision, deep learning, workplace safety, automation

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Authors: Judson P. LaGrone

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Simulation-based education in curricula was once a luxurious component of nursing programs but now serves as a vital element of an individual’s learning experience. A debriefing occurs after the simulation scenario or clinical experience is completed to allow the instructor(s) or trained professional(s) to act as a debriefer to guide a reflection with a purpose of acknowledging, assessing, and synthesizing the thought process, decision-making process, and actions/behaviors performed during the scenario or clinical experience. Debriefing is a vital component of the simulation process and educational experience to allow the learner(s) to progressively build upon past experiences and current scenarios within a safe and welcoming environment with a guided dialog to enhance future practice. The aim of this integrative review was to assess current practices of debriefing models in simulation-based education for health care professionals and students. The following databases were utilized for the search: CINAHL Plus, Cochrane Database of Systemic Reviews, EBSCO (ERIC), PsycINFO (Ovid), and Google Scholar. The advanced search option was useful to narrow down the search of articles (full text, Boolean operators, English language, peer-reviewed, published in the past five years). Key terms included debrief, debriefing, debriefing model, debriefing intervention, psychological debriefing, simulation, simulation-based education, simulation pedagogy, health care professional, nursing student, and learning process. Included studies focus on debriefing after clinical scenarios of nursing students, medical students, and interprofessional teams conducted between 2015 and 2020. Common themes were identified after the analysis of articles matching the search criteria. Several debriefing models are addressed in the literature with similarities of effectiveness for participants in clinical simulation-based pedagogy. Themes identified included (a) importance of debriefing in simulation-based pedagogy, (b) environment for which debriefing takes place is an important consideration, (c) individuals who should conduct the debrief, (d) length of debrief, and (e) methodology of the debrief. Debriefing models supported by theoretical frameworks and facilitated by trained staff are vital for a successful debriefing experience. Models differed from self-debriefing, facilitator-led debriefing, video-assisted debriefing, rapid cycle deliberate practice, and reflective debriefing. A reoccurring finding was centered around the emphasis of continued research for systematic tool development and analysis of the validity and effectiveness of current debriefing practices. There is a lack of consistency of debriefing models among nursing curriculum with an increasing rate of ill-prepared faculty to facilitate the debriefing phase of the simulation.

Keywords: debriefing model, debriefing intervention, health care professional, simulation-based education

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Authors: D. Beziakina, E. Bulgakova

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This study continues the research on Tuvinian intonation and presents a general cross-dialect analysis of intonation of Tuvinian declarative utterances, specifically the character of the tone movement in order to test the hypothesis about the prevalence of level tone in some Tuvinian dialects. The results of the analysis of basic pitch characteristics of Tuvinian speech (in general and in comparison with two other Turkic languages - Uzbek and Azerbaijani) are also given in this paper. The goal of our work was to obtain the ranges of pitch parameter values typical for Tuvinian speech. Such language-specific values can be used in speaker identification systems in order to get more accurate results of ethnic speech analysis. We also present the results of a cross-dialect analysis of declarative intonation in the poorly studied Tuvinian language.

Keywords: speech analysis, statistical analysis, speaker recognition, identification of person

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Amir Mukhtar, Dr. Likun Xia

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An improved, robust and efficient visual target tracking algorithm using particle filtering is proposed. Particle filtering has been proven very successful in estimating non-Gaussian and non-linear problems. In this paper, the particle filter is used with color feature to estimate the target state with time. Color distributions are applied as this feature is scale and rotational invariant, shows robustness to partial occlusion and computationally efficient. The performance is made more robust by choosing the different (YIQ) color scheme. Tracking is performed by comparison of chrominance histograms of target and candidate positions (particles). Color based particle filter tracking often leads to inaccurate results when light intensity changes during a video stream. Furthermore, background subtraction technique is used for size estimation of the target. The qualitative evaluation of proposed algorithm is performed on several real-world videos. The experimental results demonstrate that the improved algorithm can track the moving objects very well under illumination changes, occlusion and moving background.

Keywords: tracking, particle filter, histogram, corner points, occlusion, illumination

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Dong Wook Lee, Adrian Mistreanu

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The authors have studied a method for analyzing containment and penetration using an explicit nonlinear Finite Element Analysis. This method may be used in the stage of concept design for the protection of external configurations or components of aircraft engines and nuclear power plant structures. This paper consists of the modeling method, the results obtained from the method and the comparison of the results with those calculated from simple analytical method. It shows that the containment capability obtained by proposed method matches well with analytically calculated containment capability.

Keywords: computer aided engineering, containment analysis, finite element analysis, impact analysis, penetration analysis

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Authors: Anuta Mukherjee, Saswati Mukherjee

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Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results.

Keywords: sentiment analysis, twitter, collision theory, discourse analysis

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Authors: Tillie Curran

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This co-production project aimed for an expansive exploration of disabled young people’s hopes and dreams in the context of unprecedented societal changes. The research questions developed with disabled young people acting as peer researchers, ask ‘what does a good life look like now, and, what are your hopes and dreams for the future?’ Disabled children’s childhood studies and an asset-based approach placed the voice of disabled young people at the centre of the research process and inviting participants to ‘think big’! Over 18 months, academics, members of a Centre for Independent Living and peer researchers, came together to facilitate knowledge cafes with fifty disabled young people aged between 14 and 25 in a college and youth club setting. Methods used included trigger questions, photos voice, video, and cartooning. The peer researchers also investigated how house robots and connected autonomous vehicles might support their future aspirations and sense of freedom in this new era with a trip to the university robotic laboratory. Key themes arising from participants’ hopes and dream were about ‘being responsible’, ‘loving’, ‘freedom and happiness’ and a ‘strong sense of self and togetherness’ and suggest alternative narratives and rich visions of the future possibilities for disabled young people. The five key messages peer researchers produced for the report emphasised freedom to define their futures, desires to make the world a better place, to belong and have the chance of their own family life. Thematic analysis, production of the report and impact activities were all co-produced and as the project progressed peer researchers increasingly demonstrated a role as ‘change makers’ and have formed a young people’s co-production group going on into the future. Discussion of the project highlights the factors that made these processes successful and the ethical dilemmas encountered in the context of normalcy. Finally, we consider the implications for all involved as we rethink ‘the future’, not in terms of normative ideals or trajectories, or seeing service ‘transition’ as an end, but in terms of disabled young people’s contribution, participation, freedoms, and possibilities.

Keywords: co-production, disability, robotic, youth

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Authors: J. R. Wang, H. C. Chen, C. Shih, S. W. Chen, J. H. Yang, Y. Chiang

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In this study, we focus on the establishment of the analysis model for Maanshan PWR nuclear power plant (NPP) by using RADTRAD and SNAP codes with the FSAR, manuals, and other data. In order to evaluate the cumulative dose at the Exclusion Area Boundary (EAB) and Low Population Zone (LPZ) outer boundary, Maanshan NPP RADTRAD/SNAP model was used to perform the analysis of the DBA LOCA case. The analysis results of RADTRAD were similar to FSAR data. These analysis results were lower than the failure criteria of 10 CFR 100.11 (a total radiation dose to the whole body, 250 mSv; a total radiation dose to the thyroid from iodine exposure, 3000 mSv).

Keywords: RADionuclide, transport, removal, and dose estimation (RADTRAD), symbolic nuclear analysis package (SNAP), dose, PWR

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Authors: Michael Gidey Gebru

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Most of Data Envelopment Analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp Data Envelopment Analysis into Data Envelopment Analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the Data Envelopment Analysis model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units' efficiency. Finally, the developed Data Envelopment Analysis model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, Data Envelopment Analysis, fuzzy, higher education, input, output

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Authors: Julie A. LaRose

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Native American youth, ages 10-24, have the highest rate of suicide in the United States. The hopelessness experienced by the native American youth is linked to psychosocial reasons more than biological or intrapsychic reasons. Two significant social determinants of health that diminish their hope include historical trauma and cultural loss. Intergenerational grief is caused by historical trauma from hundreds of years of colonization, broken treaties, and forced migration, leading to land, resources, and sovereignty loss. Forced acculturation through boarding schools that native children were required to attend led to the loss of traditions and culture. The result is hopelessness. This paper reviewed peer-reviewed research literature, government reports, non-government organizations reports, and video and written publications by Native Americans. Building hope through healing historical trauma and embracing cultural traditions may reduce suicide rates among Native American youth.

Keywords: culture loss, historical trauma, Native American, suicide, suicide rates

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Authors: Kazuaki Shiraishi, Narumitsu Asai, Tsukasa Kitahara, Sosuke Mieno, Takaharu Kameoka

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The purpose of this research is to facilitate productivity advances in agriculture. To accomplish this, we developed an automatic three-dimensional (3D) recording system for growth of field crops that consists of a number of inexpensive modules: a very low-cost stereo camera, a couple of ZigBee wireless modules, a Raspberry Pi single-board computer, and a third generation (3G) wireless communication module. Our system uses an inexpensive Web stereo camera in order to keep total costs low. However, inexpensive video cameras record low-resolution images that are very noisy. Accordingly, in order to resolve these problems, we adopted a deep learning method. Based on the results of extended period of time operation test conducted without the use of an external power supply, we found that by using Super-Resolution Convolutional Neural Network method, our system could achieve a balance between the competing goals of low-cost and superior performance. Our experimental results showed the effectiveness of our system.

Keywords: 3D plant data, automatic recording, stereo camera, deep learning, image processing

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Authors: Jayakrishnan Narayanan, Sengamalam Periyasamy Dhanavel

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Indian classical (Karnatik) music videos across various media platforms have followed an audio-visual pattern that conforms to its socio-cultural and quasi-religious identity. The present paper analyzes the semiotic variations between ‘pure Karnatik music videos’ and ‘independent/contemporary-collaborative music videos’ posted on social media by young professional Karnatik musicians. The paper analyzes these media texts by comparing their various structural sememes namely, the title, artists, music, narrative schemata, visuals, lighting, sound, and costumes. The paper argues that the pure Karnatik music videos are marked by the presence of certain recurring mythological or third level signifiers and that these signifiers and codes are marked by their conspicuous absence in the independent music videos produced by the same musicians. While the music and the musical instruments used in both these sets of music videos remain similar, the meaning that is abducted by the beholder in each case is entirely different. The paper also attempts to study the identity conflicts that are projected through these music videos and the extent to which the cultural connotations of Karnatik music govern the production of its music videos.

Keywords: abduction, identity, media semiotics, music video

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Authors: Komon Paisal

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The purposes of this research were (1) to create a learning activity for constructivism, (2) study the Mathematical Analysis courses learning achievement, and (3) study students’ attitude toward the learning activity for constructivism. The samples in this study were divided into 2 parts including 3 Mathematical Analysis courses instructors of Suan Sunandha Rajabhat University who provided basic information and attended the seminar and 17 Mathematical Analysis courses students who were studying in the academic and engaging in the learning activity for constructivism. The research instruments were lesson plans constructivism, subjective Mathematical Analysis courses achievement test with reliability index of 0.8119, and an attitude test concerning the students’ attitude toward the Mathematical Analysis courses learning activity for constructivism. The result of the research show that the efficiency of the Mathematical Analysis courses learning activity for constructivism is 73.05/72.16, which is more than expected criteria of 70/70. The research additionally find that the average score of learning achievement of students who engaged in the learning activities for constructivism are equal to 70% and the students’ attitude toward the learning activity for constructivism are at the medium level.

Keywords: constructivism, learning management, mathematics analysis courses, learning activity

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Authors: Felipe Magalhaes Lemos

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Functional behavior analysis is a procedure that has been studied for several decades by behavior analysts. In Brazil, we still have few studies in the area, so it was decided to carry out a systematic review of the articles published in the area by Brazilians. A search was done on the following scientific article registration sites: PsycINFO, ERIC, ISI Web of Science, Virtual Health Library. The research includes (a) peer-reviewed studies that (b) have been carried out in Brazil containing (c) functional assessment as a pre-treatment through (d) experimental procedures, direct or indirect observation and measurement of behavior problems (e) demonstrating a relationship between environmental events and behavior. During the review, 234 papers were found; however, only 9 were included in the final analysis. Of the 9 articles extracted, only 2 presented functional analysis procedures with manipulation of environmental variables, while the other 7 presented different procedures for a descriptive behavior assessment. Only the two studies using "functional analysis" used graphs to demonstrate the prevalent function of the behavior. Other studies described procedures and did not make clear the causal relationship between environment and behavior. There is still confusion in Brazil regarding the terms "functional analysis", "descriptive assessment" and "contingency analysis," which are generally treated in the same way. This study shows that few articles are published with a focus on functional analysis in Brazil.

Keywords: behavior, contingency, descriptive assessment, functional analysis

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Authors: Theoni Soublis

Abstract:

The COVID-19 pandemic created a severe interruption in teaching and learning in K-12 schools. It is essential that educational researchers, teachers, and administrators understand the long term effects that COVID-19 had on a variety of stakeholders in education. This investigation aims to analyze the research since the beginning of the pandemic that focuses specifically on teacher retention, resilience, and training. The results of this investigation will help to inform future research in order to better understand how the institution of education can continue to be prepared and to better prepare for future significant shifts in the modalities of instruction. The results of this analysis will directly impact the field of education as it will broaden the scope of understanding regarding how COVID- 19 impacted teaching and learning. The themes that will emerge from the data analysis will directly inform policy makers, administrators, and researchers about how to best implement training and curriculum design in order to support teacher effectiveness this in the classroom. Educational researchers have written about how teacher morale plummeted and how many teachers reported early burnout and higher stress levels. Teachers’ stress and anxiety soared during the COVID-19 pandemic, but so has their resilience and dedication to the field of education. This research aims to understand how public-school teachers overcame teaching obstacles presented to them during COVID-19. Research has been conducted to identify a variety of information regarding the impact the pandemic has had on K-12 teachers, students, and families. This research aims to understand how teachers continued to pursue their teaching objectives without significant training of effective online instruction methods. Not many educators even heard of the video conferencing platform Zoom before the spring of 2020. Researchers are interested in understanding how teachers used their expertise, prior knowledge, and training to institute immediate and effective online learning environments, what types of relationships did teachers build with students while teaching 100% remotely, and how did relationships change with students while teaching remotely? Furthermore, did the teacher-student relationship propel teacher resolve to be successful while teaching during a pandemic. Recent world events have significantly impacted the field of public-school teaching. The pandemic forced teachers to shift their paradigm about how to maintain high academic expectations, meet state curriculum standards, and assess students learning gains to make data-informed decisions while simultaneously adapting modes of instruction through multiple outlets with little to no training on remote, synchronous, asynchronous, virtual, and hybrid teaching. While it would be very interesting to study how teaching positively impacted students learning during the pandemic, I am more interested in understanding how teaches stayed the course and maintained their mental health while dealing with the stress and pressure of teaching during COVID-19.

Keywords: teacher retention, COVID-19, teacher education, teacher moral

Procedia PDF Downloads 85

Authors: Muhammad Waqar Aziz, Syed Abdul Baqi Shah

Abstract:

The execution time analysis is fundamental to the successful design and execution of real-time embedded software. In such analysis, the Worst-Case Execution Time (WCET) of a program is a key measure, on the basis of which system tasks are scheduled. The WCET analysis of embedded software is also needed for system understanding and to guarantee its behavior. WCET analysis can be performed statically (without executing the program) or dynamically (through measurement). Traditionally, research on the WCET analysis assumes sequential code running on single-core platforms. However, as computation is steadily moving towards using a combination of parallel programs and multi-core hardware, new challenges in WCET analysis need to be addressed. In this article, we report our experiences of performing the WCET analysis of Parallel Embedded Software (PES) running on multi-core platform. The primary purpose was to investigate how WCET estimates of PES can be computed statically, and how they can be derived dynamically. Our experiences, as reported in this article, include the challenges we faced, possible suggestions to these challenges and the workarounds that were developed. This article also provides observations on the benefits and drawbacks of deriving the WCET estimates using the said methods and provides useful recommendations for further research in this area.

Keywords: embedded software, worst-case execution-time analysis, static flow analysis, measurement-based analysis, parallel computing

Procedia PDF Downloads 324