Search results for: big video data

Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

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Authors: Chiao-Wen Lan, David Gere

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Background and Significance: Performance has a rich history of imparting information and encouraging reflection, yet there is a paucity of literature on applying performance as a method of analysis and not as a medium for health education. This study aimed to apply ethnodrama strategies to the issue of HIV-related stigma in rural China and to use a critical performance as a vehicle for communication of health research. Methods: The program, titled 'STOP STIGMA,' included dance, narratives and original quotes from people living with HIV/AIDS in China, and spectacle such as photographs, set, and props corresponding to the history of HIV in rural China. Results: The performance represented a step away from a completely textual interpretation of data towards a theatrical style that begins to privilege what arts-based research scholars Rossiter and colleagues have termed 'an embodied, theatrical representation of data.' It offered an opportunity to deliver individual and collective stories that represent how HIV-positive people experience living with HIV/AIDS in China, which could play an integral part in the formulation of actions to effect change. Discussion: This method of communicating health research has implications for fostering dialogue among researchers, community members, and medical practitioners. Although arts-based approaches are not new to the scientific community, the integration of dance, video, ethnodrama, and sciences provides opportunities to innovate in non-traditional research dissemination and communication.

Keywords: health communication, HIV/AIDS, stigma, vulnerable populations

Procedia PDF Downloads 90

Authors: Andra Yount

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The purpose of this research project is to understand how focusing on movement can help English language learners acquire better reading, writing, and speaking skills. More specifically, this case study tests how Laban movement analysis, a tool often used in dance and physical education classes, contributes to advanced-level high school students’ English language acquisition at an international Swiss boarding school. This article shares theoretical bases for and findings from a teaching experiment in which LMA categories (body, effort, space, and shape) were adapted and introduced to students to encourage basic language acquisition and also cultural awareness and sensitivity. As part of the participatory action research process, data collection included pseudonym-protected questionnaires and written/video-taped responses to LMA language and task prompts. Responses from 43 participants were evaluated to determine the efficacy of using this system. Participants (ages 16-19) were enrolled in advanced English as an Additional Language (EAL) courses at a private, co-educational Swiss international boarding school. Final data analysis revealed that drawing attention to movement using LMA language as a stimulus creates better self-awareness and understanding/retention of key literary concepts and vocabulary but does not necessarily contribute to greater cultural sensitivity or eliminate the use of problematic (sexist, racist, or classist) language. Possibilities for future exploration and development are also explored.

Keywords: dance, English, Laban, pedagogy

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Authors: Chuan-Ching Sue, Kai-Chun Chuang

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Peer-to-Peer (P2P) live streaming systems still suffer a challenge when thousands of new peers want to join into the system in a short time, called flash crowd, and most of new peers suffer long start-up delay. Recent studies have proposed a slot-based user access control mechanism, which periodically determines a certain number of new peers to enter the system, and a user batch join mechanism, which divides new peers into several tree structures with fixed tree size. However, the slot-based user access control mechanism is difficult for accurately determining the optimal time slot length, and the user batch join mechanism is hard for determining the optimal tree size. In this paper, we propose a structured access control (SAC) mechanism, which constructs new peers to a multi-layer mesh structure. The SAC mechanism constructs new peer connections layer by layer to replace periodical access control, and determines the number of peers in each layer according to the system’s remaining upload bandwidth and average video rate. Furthermore, we propose an analytical model to represent the behavior of the system growth if the system can utilize the upload bandwidth efficiently. The analytical result has shown the similar trend in system growth as the SAC mechanism. Additionally, the extensive simulation is conducted to show the SAC mechanism outperforms two previously proposed methods in terms of system growth and start-up delay.

Keywords: peer-to-peer, live video streaming system, flash crowd, start-up delay, access control

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Authors: Bo Ning Lyu

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Blog assisted language learning (BALL) has been trialed by educators in language teaching with the development of Web 2.0 technology. Understanding the development trend of related research helps grasp the whole picture of the use of blog in language education. This paper reviews current research related to blogs enhanced language learning based on bibliometric analysis, aiming at (1) identifying the most frequently used keywords and their co-occurrence, (2) clustering research topics based on co-citation analysis, (3) finding the most frequently cited studies and authors and (4) constructing the co-authorship network. 330 articles were searched out in Web of Science, 225 peer-viewed journal papers were finally collected according to selection criteria. Bibexcel and VOSviewer were used to visualize the results. Studies reviewed were published between 2005 to 2016, most in the year of 2014 and 2015 (35 papers respectively). The top 10 most frequently appeared keywords are learning, language, blog, teaching, writing, social, web 2.0, technology, English, communication. 8 research themes could be clustered by co-citation analysis: blogging for collaborative learning, blogging for writing skills, blogging in higher education, feedback via blogs, blogging for self-regulated learning, implementation of using blogs in classroom, comparative studies and audio/video blogs. Early studies focused on the introduction of the classroom implementation while recent studies moved to the audio/video blogs from their traditional usage. By reviewing the research related to BALL quantitatively and objectively, this paper reveals the evolution and development trends as well as identifies influential research, helping researchers and educators quickly grasp this field overall and conducting further studies.

Keywords: blog, bibliometric analysis, language learning, literature review

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Authors: Yash Jain

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The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

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Authors: Tünde Puskás, Anita Andersson

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This study is part of a research project about whether religion is considered as part of Swedish cultural heritage in Swedish preschools. Our aim in this paper is to explore how a Swedish preschool balance between keeping the education non-confessional and at the same time teaching children about a particular tradition, Easter.The paper explores how in a Swedish preschool with a religious profile teachers balance between keeping education non-confessional and teaching about a tradition with religious roots. The point of departure for the theoretical frame of our study is that practical considerations in pedagogical situations are inherently dilemmatic. The dilemmas that are of interest for our study evolve around formalized, intellectual ideologies, such us multiculturalism and secularism that have an impact on everyday practice. Educational dilemmas may also arise in the intersections of the formalized ideology of non-confessionalism, prescribed in policy documents and the common sense understandings of what is included in what is understood as Swedish cultural heritage. In this paper, religion is treated as a human worldview that, similarly to secular ideologies, can be understood as a system of thought. We make use of Ninian Smart's theoretical framework according to which in modern Western world religious and secular ideologies, as human worldviews, can be studied from the same analytical framework. In order to be able to study the distinctive character of human worldviews Smart introduced a multi-dimensional model within which the different dimensions interact with each other in various ways and to different degrees. The data for this paper is drawn from fieldwork carried out in 2015-2016 in the form of video ethnography. The empirical material chosen consists of a video recording of a specific activity during which the preschool group took part in an Easter play performed in the local church. The analysis shows that the policy of non-confessionalism together with the idea that teaching covering religious issues must be purely informational leads in everyday practice to dilemmas about what is considered religious. At the same time what the adults actually do with religion fulfills six of seven dimensions common to religious traditions as outlined by Smart. What we can also conclude from the analysis is that whether it is religion or a cultural tradition that is thought through the performance the children watched in the church depends on how the concept of religion is defined. The analysis shows that the characters of the performance themselves understood religion as the doctrine of Jesus' resurrection from the dead. This narrow understanding of religion enabled them indirectly to teach about the traditions and narratives surrounding Easter while avoiding teaching religion as a belief system.

Keywords: non-confessional education, preschool, religion, tradition

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Authors: M. Naidoo

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Artificial intelligence often requires large amounts of good quality data. Within important fields, such as healthcare, the training of AI systems predominately relies on health and personal data; however, the usage of this data is complicated by various layers of law and ethics that seek to protect individuals’ privacy rights. This research seeks to establish the challenges AI and data sciences pose to (i) informational rights, (ii) privacy rights, and (iii) data protection. To solve some of the issues presented, various methods are suggested, such as embedding values in technological development, proper balancing of rights and interests, and others.

Keywords: artificial intelligence, data science, law, policy

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Authors: Kevin William Taylor

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This work draws upon research in the fields of games development and mental health treatments to assess the influence that interactive entertainment has on the populous, and the potential of technology to affect areas of psychiatric assessment and treatment. It will use studies to establish the evolving direction of interactive media in the development of ‘digital self-identity,’ and how this can be incorporated into treatment to the benefit of psychiatry. It will determine that this approach will require collaborative production between developers and psychiatrists in order to ensure precise goals are met, improving the success of serious gaming for psychiatric assessment and treatment. Analysis documents the reach of video games across a growing global community of gamers, highlighting cases of the positives and negatives of video game usage. The games industry is largely oblivious to the psychological negatives, with psychiatrists encountering new conditions such as gaming addiction, which is now recognized by the World Health Organization. With an increasing amount of gamers worldwide, and an additional time per day invested in online gaming and character development, the concept of virtual identity as a means of expressing the id needs further study to ensure successful treatment. In conclusion, the assessment and treatment of game-related conditions are currently reactionary, and while some mental health professionals have begun utilizing interactive technologies to assist with the assessment and treatment of conditions, this study will determine how the success of these products can be enhanced. This will include collaboration between software developers and psychiatrists, allowing new avenues of skill-sharing in interactive design and development. Outlining how to innovate approaches to engagement will reap greater rewards in future interactive products developed for psychiatric assessment and treatment.

Keywords: virtual reality, virtual identity, interactivity, psychiatry

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Authors: Jing Zhao, Yoshiharu Ishikawa, Chuan Xiao, Kento Sugiura

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In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data.

Keywords: simulation data, data summarization, spatial histograms, exploration, visualization

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Authors: U. Pruthviraj, Praveen Kumar R. A. K. Athul, K. V. Gangadharan, S. Rao Shrikantha

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An open source based autonomous unmanned marine surface vehicle (UMSV) is developed for some of the marine applications such as pollution control, environmental monitoring and thermal imaging. A double rotomoulded hull boat is deployed which is rugged, tough, quick to deploy and moves faster. It is suitable for environmental monitoring, and it is designed for easy maintenance. A 2HP electric outboard marine motor is used which is powered by a lithium-ion battery and can also be charged from a solar charger. All connections are completely waterproof to IP67 ratings. In full throttle speed, the marine motor is capable of up to 7 kmph. The motor is integrated with an open source based controller using cortex M4F for adjusting the direction of the motor. This UMSV can be operated by three modes: semi-autonomous, manual and fully automated. One of the channels of a 2.4GHz radio link 8 channel transmitter is used for toggling between different modes of the USMV. In this electric outboard marine motor an on board GPS system has been fitted to find the range and GPS positioning. The entire system can be assembled in the field in less than 10 minutes. A Flir Lepton thermal camera core, is integrated with a 64-bit quad-core Linux based open source processor, facilitating real-time capturing of thermal images and the results are stored in a micro SD card which is a data storage device for the system. The thermal camera is interfaced to an open source processor through SPI protocol. These thermal images are used for finding oil spills and to look for people who are drowning at low visibility during the night time. A Real Time clock (RTC) module is attached with the battery to provide the date and time of thermal images captured. For the live video feed, a 900MHz long range video transmitter and receiver is setup by which from a higher power output a longer range of 40miles has been achieved. A Multi-parameter probe is used to measure the following parameters: conductivity, salinity, resistivity, density, dissolved oxygen content, ORP (Oxidation-Reduction Potential), pH level, temperature, water level and pressure (absolute).The maximum pressure it can withstand 160 psi, up to 100m. This work represents a field demonstration of an open source based autonomous navigation system for a marine surface vehicle.

Keywords: open source, autonomous navigation, environmental monitoring, UMSV, outboard motor, multi-parameter probe

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Authors: Gail Joseph

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In the United States, to advance the quality and education requirements of PreK teachers, there are concerns regarding barriers for existing early childhood educators to access formal degrees and ongoing professional development. Barriers exist related to affordability and access. Affordability is a key factor that impacts teachers access to degree programs. The lack of financial resources makes it difficult for many qualified candidates to begin, and complete, degree programs. Even if funding was not an issue, accessibility remains a pressing issue in higher education. Some common barriers include geography, long work hours, lack of professional community, childcare, and clear articulation agreements. Greater flexibility is needed to allow all early childhood professionals to pursue college coursework that takes into consideration the many competing demands on their schedules. For these busy professionals, it is particularly important that professional development opportunities are available “on demand” and are seen as relevant to their work. Courses that are available during non-traditional hours make attendance more accessible, and professional development that is relevant to what they need to know and be able to do to be effective in their current positions increase access to and the impact of ongoing professional education. EarlyEdU at the University of Washington provides institutes of higher education and state professional development systems with free comprehensive, competency based college courses based on the latest science of how to optimize child learning and outcomes across developmental domains. The coursework embeds an intentional teaching framework which requires teachers to know what to do in the moment, see effective teaching in themselves and others, enact these practices in the classroom, reflect on what works and what does not, and improve with thoughtful practices. Reinforcing the Intentional Teaching Framework in EarlyEdU courses is the Coaching Companion, an open source, web-based video annotation learning tool that supports coaching in higher education by enabling students to view and refine their teaching practices. The tool is integrated throughout EarlyEdU courses. With the Coaching Companion, students see upload teaching interactions on video and then reflect on the degree to which they incorporate evidence-based practices. Coaching Companion eliminates the traditional separation of theory and practice in college-based teacher preparation. Together, the Intentional Teaching Framework and the Coaching Companion transform the course instructor into a job-embedded coach. The instructor watches student interactions with children on video using the Coaching Companion and looks specifically for interactions defined in course assignments, readings, and lectures. Based on these observations, the instructor offers feedback and proposes next steps. Developed on federal and philanthropic funds, all EarlyEdU courses and the Coaching Companion are available for free to 2= and 4-year colleges and universities with early childhood degrees, as well as to state early learning and education departments to increase access to high quality professional development. We studied the impact of the Coaching Companion in two courses and demonstrated a significant increase in the quality of teacher-child interactions as measured by the PreK CLASS quality teaching assessment. Implications are discussed related to policy and practice.

Keywords: education technology, distance education, early childhood education, professional development

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Authors: T. Nagata, S. Xaphakdy, P. Souvannavong, P. Chanthamaly, K. Sithavong, C. H. Lee, S. Phommathat, V. Srithilat, P. Sengdala, B. Phetarnousone, B. Siharath, X. Chemcheng, T. Yamaguchi, A. Suenaga, S. Kashima

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National University of Laos (NUOL) requested Japan International Cooperation Agency (JICA) volunteers to begin a physics experiments class using multi media. However, there are issues. NUOL had no physics experiment class, no space for physics experiments, experiment materials were not used for many years and were scattered in various places, and there is no projector and laptop computer in the unit. This raised the question: How do authors begin the physics experiments class using multimedia? To solve this problem, the JICA took some steps, took stock of what was available and reviewed the syllabus. The JICA then revised the experiment materials to assess what was available and then developed textbooks for experiments using them; however, the question remained, what about the multimedia component of the course? Next, the JICA reviewed Physics teacher Pavy Souvannavong’s YouTube channel, where he and his students upload video reports of their physics classes at NUOL using their smartphones. While they use multi-media, almost all the videos recorded were of class presentations. To improve the multimedia style, authors edited the videos in the style of another YouTube channel, “Science for Lao,” which is a science education group made up of Japan Overseas Cooperation Volunteers (JOCV) in Laos. They created the channel to enhance science education in Laos, and hold regular monthly meetings in the capital, Vientiane, and at teacher training colleges in the country. They edit the video clips in three parts, which are the materials and procedures part including pictures, practice footage of the experiment part, and then the result and conclusion part. Then students perform experiments and prepare for presentation by following the videos. The revised experiment presentation reports use PowerPoint presentations, material pictures and experiment video clips. As for providing textbooks and submitting reports, the students use the e-Learning system of “Moodle” of the Information Technology Center in Dongdok campus of NUOL. The Korean International Cooperation Agency (KOICA) donated those facilities. The authors have passed the process of the revised materials, developed textbooks, the PowerPoint slides presented by students, downloaded textbooks and uploaded reports, to begin the physics experiments class using multimedia. This is the practice research report for beginning a physics experiments class using multimedia in the physics unit at the Department of Natural Science, Faculty of Education, at the NUOL.

Keywords: NUOL, JICA, KOICA, physics experiment materials, smartphone, Moodle, IT center, Science for Lao

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Authors: Vahid Bairami Rad

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Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented.

Keywords: spatial data base, knowledge discovery database, data mining, spatial relationship, predictive data mining

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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: B. Suraj Aravind, M. H. M. Krishna Prasad

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There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring, web click streams analysis, sensor data, data from satellites etc. Data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. This paper proposes to introduce an improved data stream association rule mining algorithm by eliminating the limitation of resources. For this, the concept of cloud computing is used. Inclusion of this may lead to additional unknown problems which needs further research.

Keywords: data stream, association rule mining, cloud computing, frequent itemsets

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Authors: Oktia Woro Kasmini Handayani, Sri Ratna Rahayu, Efa Nugroho, Bertakalswa Hermawati

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In Indonesia, from 2007 until 2013, the prevalence of overnutrition in children under five years and school age tends to increase. Clean and Health Life Behavior of school children supporting nutrition status still below the determined target. On the other side, school institution is an ideal place to educate and form health behavior, that should be initiated as early as possible (Early Age Education/PAUD level). The objective of this research was to find out the effectivity of education model through partnership with school institution in urban region, city of Semarang, Central Java Province, Indonesia. The research used quantitative approach supported with qualitative data. The population consist of all mother having school children of ages 3-5 years within the research region; sampling technique was purposive sampling, as many as 237 mothers. Research instrument was Clean and Health Life Behavior evaluation questionaire, and video as education media. The research used experimental design. Data analysis used effectivity criteria from Sugiyono and 2 paired sampel t test. Education model optimalization in the effort to improve nutrition status indicates t test result with signification < 0.05 (there was significant effect before and after model intervention), with effectivity test result of 79% (effective), but still below expected target which is 80%. Education model need to be utilized and optimallized the implementation so that expected target reached.

Keywords: nutrition status, early age education, clean dan health life behavior, education model

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Authors: Ganetsou Evanthia, Koutsikos Emmanouil, Austin Anna Maria

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Information technology has long served the needs of individuals for learning and entertainment, but much less for children in sickness. The aim of the proposed online video game is to provide immersive learning opportunities as well as essential social and emotional scenarios for hospital-bound children with long-term illness. Online self-paced courses on chosen school subjects, including specialised software and multisensory assessments, aim at enhancing children’s academic achievement and sense of inclusion, while doctor minigames familiarise and educate young patients on their medical conditions. Online ethical dilemmas will offer children opportunities to contemplate on the importance of medical procedures and following assigned medication, often challenging for young patients; they will therefore reflect on their condition, reevaluate their perceptions about hospitalisation, and assume greater personal responsibility for their progress. Children’s emotional and psychosocial needs are addressed by engaging in social conventions, such as interactive, daily, collaborative mini games with other hospitalised peers, like virtual competitive sports games, weekly group psychodrama sessions, and online birthday parties or sleepovers. Social bonding is also fostered by having a virtual pet to interact with and take care of, as well as a virtual nurse to discuss and reflect on the mood of the day, engage in constructive dialogue and perspective taking, and offer reminders. Access to the platform will be available throughout the day depending on the patient’s health status. The program is designed to minimise escapism and feelings of exclusion, and can flexibly be adapted to offer post-treatment and a support online system at home.

Keywords: long-term illness, children, hospital, interactive games, cognitive, socioemotional development

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Authors: Tosin Ige

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Ethics must be a condition of the world, like logic. (Ludwig Wittgenstein, 1889-1951). As important as data mining is, it possess a significant threat to ethics, privacy, and legality, since data mining makes it difficult for an individual or consumer (in the case of a company) to control the accessibility and usage of his data. This research focuses on Current issues and the latest research and development on Privacy preserving data mining methods as at year 2022. It also discusses some advances in those techniques while at the same time highlighting and providing a new technique as a solution to an existing technique of privacy preserving data mining methods. This paper also bridges the wide gap between Data mining and the Web Application Programing Interface (web API), where research is urgently needed for an added layer of security in data mining while at the same time introducing a seamless and more efficient way of data mining.

Keywords: data, privacy, data mining, association rule, privacy preserving, mining technique

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Authors: A. Alexandru, C. A. Alexandru, D. Coardos, E. Tudora

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Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented.

Keywords: big data, big data analytics, Hadoop, cloud

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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: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: Winfred Okoe Addy, Jean Jacques Dominique Beraud

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Access control systems (ACs) are some of the most important components in safety areas. Inaccuracies of regulatory frameworks make personal policies and remedies more appropriate than standard models or protocols. This problem is exacerbated by the increasing complexity of software, such as integrated Big Data (BD) software for controlling large volumes of encrypted data and resources embedded in a dedicated BD production system. This paper proposes a general access control strategy system for the diffusion of Big Data domains since it is crucial to secure the data provided to data consumers (DC). We presented a general access control circulation strategy for the Big Data domain by describing the benefit of using designated access control for BD units and performance and taking into consideration the need for BD and AC system. We then presented a generic of Big Data access control system to improve the dissemination of Big Data.

Keywords: access control, security, Big Data, domain

Procedia PDF Downloads 108

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: 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: Abe D. Desta

Abstract:

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: Suwarno, D. Jongudomkarn

Abstract:

Family Health Assessment (FHA) is a key process to identify the family health needs, family health problems, and family health history. Assessing the family health is not only from the assessment tool but also from health care provider especially Nurse. Nurses’ have duties to assess the family as holistic view and they have to increase their capacities (knowledge, skills and experiences) in FHA. Thus, the continuing nursing education-training program on using the KKU FHA Tool was aimed to enhance the participant nurses’ capacities in (FHA) based on such tool. The aim of this study was to evaluate the KKU FHA Tool training program on PCU nurses’ capacity before and after training program in Primary Care Unit Bantul, Yogyakarta. The Quasi-Experiment with one group pre-, post-test design as a research design with convenient sampling technique and one group pre- post test formula for Nurses who work in Six PCU Bantul, Yogyakarta as much as fourteen respondents. The research processes were used training program with module, video and handbook KKU FHA Tool, KKU FHA tool form and capacities questionnaires. It was analyzed by descriptive data, Kolmogorov-Smirnov and Paired Sample t-test. The overall comparing analysis of paired sample t-test revealed that the mean values of pre-test were 3.35 with SD 0.417, post-test was 3.86 with SD 0.154 and post-test in later two weeks was 4.00 with SD 0.243. It was found that the p value of among the pre-test, the intermediate post-test and the post–test in later two weeks were 0.000. The p value of the intermediate post-test and post-test in later two weeks was 0.053. KKU FHA Tool training program in PCU Bantul Yogyakarta was enhanced the participant nurses’ capacities significantly. In conclusion, we are recommending KKU FHA Tool forms have to develop and implement with qualitative research as complementary data in PCU Bantul Yogyakarta by Focus Group Discussion.

Keywords: family health assessment, KKU FHA tool, training program, nurses capacities

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

Abstract:

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: M. Kharrat, G. Moreau, Z. Aboura

Abstract:

The latest advances in the weaving industry, combined with increasingly sophisticated means of materials processing, have made it possible to produce complex 3D composite structures. Mainly used in aeronautics, composite materials with 3D architecture offer better mechanical properties than 2D reinforced composites. Nevertheless, these materials require a good understanding of their behavior. Because of the complexity of such materials, the damage mechanisms are multiple, and the scenario of their appearance and evolution depends on the nature of the exerted solicitations. The AE technique is a well-established tool for discriminating between the damage mechanisms. Suitable sensors are used during the mechanical test to monitor the structural health of the material. Relevant AE-features are then extracted from the recorded signals, followed by a data analysis using pattern recognition techniques. In order to better understand the damage scenarios of interlock composite materials, a multi-instrumentation was set-up in this work for tracking damage initiation and development, especially in the vicinity of the first significant damage, called macro-damage. The deployed instrumentation includes video-microscopy, Digital Image Correlation, Acoustic Emission (AE) and micro-tomography. In this study, a multi-variable AE data analysis approach was developed for the discrimination between the different signal classes representing the different emission sources during testing. An unsupervised classification technique was adopted to perform AE data clustering without a priori knowledge. The multi-instrumentation and the clustered data served to label the different signal families and to build a learning database. This latter is useful to construct a supervised classifier that can be used for automatic recognition of the AE signals. Several materials with different ingredients were tested under various solicitations in order to feed and enrich the learning database. The methodology presented in this work was useful to refine the damage threshold for the new generation materials. The damage mechanisms around this threshold were highlighted. The obtained signal classes were assigned to the different mechanisms. The isolation of a 'noise' class makes it possible to discriminate between the signals emitted by damages without resorting to spatial filtering or increasing the AE detection threshold. The approach was validated on different material configurations. For the same material and the same type of solicitation, the identified classes are reproducible and little disturbed. The supervised classifier constructed based on the learning database was able to predict the labels of the classified signals.

Keywords: acoustic emission, classifier, damage mechanisms, first damage threshold, interlock composite materials, pattern recognition

Procedia PDF Downloads 132