Face recognition attendance system using Local Binary Pattern (LBP)

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate

OAS: An Advanced Undergraduate Office Automated System for Automate Bank Work Routing

Office automation systems (OAS) have steadily gained popularity as a result of the information technology industry's fast expansion. The promise of office automation, which is meant to increase productivity in the office setting, has caused organizations in both the public and commercial sectors to grow concerned. This issue becomes quite important due to the rising costs of new and enhanced office automation technologies. As a result, industries like banks have improved their front-end client operations using digital solutions. However, a large number of bank processors still depend on personnel and paper. This level of human processing is expensive, sluggish, and may provide results that are highly inconsistent, have significant mistake rates, and pose security risks. Through this research work, our aim is to consider existing significant opportunities in backoffice automation level increase and develop a pilot OAS to work smart with a higher level of efficiency in the office environment. The proposed solution is enriched with image processing methods to authenticate employees to ensure the security of the banking environment by enabling access control and providing a well-accurate employee attendance handling system through web and mobile app facilities which are enabled by GPS and API call technologies. To maintain on-time accurate customer support service, our study has proposed a smart customer handling facility with the help of an AI chatbot and some IoT device material

인공지능 기반 교육 플랫폼 사용에 대한 중국 교사의 인식

학위논문 (석사) -- 서울대학교 대학원 : 사범대학 교육학과, 2021. 2. 조영환.최근 교육 분야에서 인공지능(AI)의 도입이 큰 관심을 끌고 있다. 특히 AI 기술과 학습 분석이 결합한 인공지능 기반 교육 플랫폼은 지금껏 실현되기 어려웠던 맞춤형 학습(personalized learning)과 적응적 학습(adaptive learning)에 도움이 될 수 있도록 발전하고 있다. 인공지능 기반 교육 플랫폼(AI-based education platform)은 학습자의 행동 추적 등을 통해 이들의 특성을 분석하고 진단을 제공한 뒤 분석 결과를 토대로 학습자에게 인지 수준에 맞는 맞춤형 학습자원과 피드백을 제공한다. 인공지능 기반 교육 플랫폼은 교사와 학생에게 실시간 학습 데이터와 분석 결과, 그리고 피드백을 제공할 수 있어 인공지능 기반 교육 플랫폼이 맞춤형 학습에 긍정적인 의미가 있다는 선행 연구도 있었다. 그럼에도 불구하고, 기존 연구는 모델 개발의 차원에서나 엄밀한 실험실 환경에서 인공지능 기반 교육 플랫폼의 효과를 연구해왔으며, 인공지능 기반 교육 플랫폼에 대한 교사의 인식과 관련된 연구는 드물었다. 교사는 인공지능 교육 기술의 사용자이기 때문에 인공지능 교육 기술의 교육 도입에 있어 교사들의 인식과 의견은 중요하다. 본 연구는 인공지능 기반 교육 플랫폼을 활용하는 것에 대한 교사들의 인식을 탐구하였다. 아래 연구 문제를 다루기 위해 질적 연구를 시행하였다. 첫째, 중국 교사들은 인공지능 기반 교육 플랫폼이 중학교 교육에 활용 있어 어떠한 장점이 있다고 인식하는가? 둘째, 중국 교사들은 인공지능 기반 교육 플랫폼과 중학교 교수 활동 요소 간 어떠한 모순이 있다고 인식하는가? 셋째, 중국 교사들은 인공지능 기반 교육 플랫폼을 중학교 교육에 도입할 때 무엇이 필요하다고 인식하는가? 본 연구는 중국 교사들을 연구대상으로 온라인 심층 면담을 하였다. 문헌 리뷰를 통해 면담 질문지를 설계하되 눈덩이표집법 (snowball sampling)을 통해 중국 중학교 교사 14명을 연구참여자로 선정하였다. 선정된 교사들은 모두 인공지능 기반 교육 플랫폼 사용 경험이 있으며 각 교사를 대상으로 약 1시간 정도 면담을 진행하고 녹음하였다. 면담이 끝난 후 녹음 내용을 전사하였으며, 주제분석을 사용하여 면담 내용을 초기 코드 생성하고 면담 자료 속에서 주제를 도출하였다. 특히 연구 문제 2번의 경우, 인공지능 기반 교육 플랫폼 활용과 교수 학습활동 내 여러 요소 간의 모순을 분석하기 위해 활동이론을 연구의 틀로 이용하였다. 최종적으로 연구문제 1에 대한 주제 4개, 연구문제 2에 대한 주제 6개, 연구문제 3에 대한 주제 4개를 도출하였다. 연구 결과로 교사들은 인공지능 기반 교육 플랫폼의 장점에 대해 즉각적인 피드백 제공, 교수학습 지원, 교사의 업무량 감소 등으로 인식하였고, 인공지능 기반 교육 플랫폼이 다양한 교수학습 자원을 통합할 수 있다고 인식하였다. 아울러 교사들은 인공지능 기반 교육 플랫폼의 사용에 있어 기존의 교수학습 활동과 상충된 부분이 있다는 점을 인식하였다. 교사들은 기존 인공지능 기반 교육 플랫폼의 추천 모델이 차별화된 학생들에게 잘 적용되지 못한다는 것을 인식하였다. 그리고 기존 인공지능 기반 교육 플랫폼이 다양한 학습 자원을 잘 분류되지 못하기 때문에 교사들이 사용하기 불편하다. 인공지능 기반 교육 플랫폼을 이용할 때 교사의 지적재산권을 보호하기 위한 명확한 규제가 부족하다고 인식하였다. 이와 함께 학부모들은 인공지능 기반 교육 플랫폼을 사용함으로써 발생할 수 있는 학습자의 인터넷 남용과 시력 저하 문제를 우려하였다. 또 중국의 사회문화적 배경과 교육 특성으로 인해 인공지능 기반 교육 플랫폼을 활용하는 데 학생들의 글씨 쓰기 능력에 영향을 미칠 수 있으며, 학교 내 전자기기 사용 제한도 데이터 수집의 지속성과 효율성에 영향을 미칠 수 있다고 인식하였다. 교사들은 위의 문제들이 인공지능 교육 플랫폼 사용에 대한 규칙 마련과 인공지능 기술을 개선함으로써 완화될 수 있다고 인식하였다. 또한 교사의 실제 요구에 맞게 개발될 수 있도록 인공지능 기반 교육 플랫폼 개발 과정에 교육 전문가와 교사가 참여할 필요가 있다. 본 연구는 중국 교사들이 인공지능 기반 교육 플랫폼에 대한 인식을 탐색하였으며, 인공지능 기반 교육 플랫폼이 교수학습에서의 장점과 문제점을 밝혔다. 아울러 본 연구는 인공지능 기반 교육 플랫폼이 교육 분야에 대규모로 도입될 수 있도록 규칙, 인공지능 기술, 그리고 교육 공학의 차원에서 사용 규범과 기술 개선을 제안하였다. 본 연구를 통해 탐색한 내용이 향후 교육 분야의 인공지능 기반 교육 플랫폼 도입에 활용된다면 인공지능 교육 기술에 관한 연구의 발전에도 기여할 수 있을 것으로 기대된다.In recent years, the introduction of artificial intelligence (AI) in education has attracted widespread attention. In particular, the AI-based education platform based on the combination of AI technology and learning analysis brings new light to the long-standing difficulties in personalized learning and adaptive learning. The AI-based education platform analyzes learners' characteristics by collecting their data and tracking their learning behavior. It then generates cognitive diagnosis for learners and provides them with personalized learning resources and adaptive feedback that match their cognitive level based on systematic analysis. With the help of the AI-based education platform, teachers and students can get real-time educational data and analysis result，as well as the feedback and treatment corresponding to the results. Previous studies have already demonstrated and proved its positive significance to personalized learning. However, these studies mostly start from a model development perspective or in a rigorous laboratory environment. There has been little research on teachers' perceptions of AI-based education platform. As a direct user of AI educational technologies, teachers' perceptions and suggestions are vital for introducing AIEd in education. In this study, the researcher explored teachers' perceptions of using AI-based education platform in teaching. The study conducted qualitative research to address the following research questions: 1) How do Chinese teachers perceive the advantages of AI-based education platforms for teaching and learning in secondary school? 2) How do Chinese teachers perceive the contradictions between AI-based education platforms and the secondary school system? 3）How do Chinese teachers suggest applying AI-based education platforms in secondary school? And it referred to the in-depth online interview with Chinese teachers who had experience with AI-based education platform. Interview questions were constructed through the literature review, and 14 secondary school teachers were selected by the snowball sampling method. The interviews lasted for an average of one hour per teacher and were transcribed from the audio recordings to text documents when finished. Afterward, the data were analyzed using thematic analysis, including generating initial codes, searching and reviewing the categories, and deriving the themes finally. Notably, for research question two, the researcher used the activity theory framework to analyze the contradictions among the use of the AI-based education platform and the various elements of the teaching and learning activities. Finally, four themes for research question 1, six themes for research question 2, and four themes for research question 3 were derived. As for the advantages, teachers believe that AI-based education platforms can provide instant feedback, targeted and systematic teaching support, and reduce teachers' workload. At the same time, AI-based education platforms can also integrate teaching resources in different areas. Teachers also recognized that the AI-based education platforms might trigger contradictions in existing teaching activities. They are aware of the situation that the recommended model of the AI-based education platform is not suitable for all levels of students; that a large number of learning resources are not classified properly enough to meet the needs of teachers, and that there lack clear rules and regulations to protect teachers' intellectual property rights when using the platform. Besides, parents are also concerned about the potential risk of internet addiction and vision problems using AI-based education platforms. Moreover, the use of the AI-based education platform may also affect students' ability to write Chinese characters due to the socio-historical background and educational characteristics in China. Furthermore, the restricted use of electronic devices on campus may also impact the consistent and effective education data collection. Teachers believe that these problems can be solved by improving rules and AI technology. Moreover, to make the platform more in line with the actual teaching requirements, teachers and education experts can also be involved in the development process of AI-based education platform. This study explored how Chinese teachers perceive the AI-based education platform and found that the AI-based education platform was conducive to personalized teaching and learning. At the same time, this study put forward some suggestions from the perspective of rules, AI technology, and educational technology, hoping to provide a good value for the future large-scale introduction of AI-based education platforms in education.CHAPTER 1. INTRODUCTION 1 1.1. Problem Statement 1 1.2. Purpose of Research 7 1.3. Definition of Terms 8 CHAPTER 2. LITERATURE REVIEW 10 2.1. AI in Education 10 2.1.1 AI for Learning and Teaching 10 2.1.2 AI-based Education Platform 14 2.1.3 Teachers' Perception on AI-based Education Platform 18 2.2. Activity Theory 20 CHAPTER 3. RESEARCH METHOD 23 3.1. Research Design 23 3.2. Participants 25 3.3. Instrumentation 26 3.3.1 Potential Value of AI System in Education 26 3.4. Data Collection 33 3.5. Data Analysis 34 CHAPTER 4. FINDINGS 36 4.1. Advantages of Using AI-based Education Platform 36 4.1.1 Instant Feedback 37 4.1.2 Targeted and Systematic Teaching Support 42 4.1.3 Educational Resources Sharing 46 4.1.4 Reducing Workload 49 4.2. Tensions of Using AI-based Education Platform 51 4.2.1 Inadequately Meet the Needs of Teachers 52 4.2.2 Failure to Satisfy Low and High Achievers 54 4.2.3 Intellectual Property Violation 56 4.2.4 Guardian's Concern 57 4.2.5 School Rules about the Use of Electronic Devices 58 4.2.6 Implication for Chinese Character Education 59 4.3. Suggestion of Using AI-based Education Platform 61 4.3.1 Improving Rules of Using the AI-based Education Platform 61 4.3.2 Improving Rules of Protecting Teachers Right 62 4.3.3 Improving AI Technology 64 4.3.4 Participatory Design 66 CHAPTER 5. DISCUSSION AND CONCLUSION 68 5.1. Discussion 68 5.2. Conclusion 72 REFERENCE 75 APPENDIX 1 98 APPENDIX 2 100 국문초록 112Maste

Class attendance monitoring system using NFC technology

A Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Mobile Telecommunications and Innovation (MSc. MTI)The ubiquitous nature of ICT has made it possible to create innovations which have contributed to efficiency across different industries. As such, ICT has been applied in different areas of educational management, from content creation and distribution to electronic assessments. More recently, it has been applied in the monitoring of class attendance in different institutions across the world. Local institutions still rely on manual monitoring of this information, and as such prompted this research which seeks to find a lasting solution to this problem through the application of ICT. NFC technology involves wireless communication between digital devices through the use of electromagnetic signals. This technology has been used for a variety of purposes, especially in electronic payments. The properties of NFC tags which allow identification information to be written on them has made it possible for the technology to be used in place of biometric technologies. NFC has therefore emerged as a means through which identification can be done using a device with an NFC tag and read through an NFC reader. This research presents preliminary information relating to the class attendance monitoring process. Informed by the growing need for automation in this process, this document has addressed the challenges and opportunities for change around manual class attendance monitoring. Relevant literature was reviewed in an effort to differentiate a solution that has be applied to the current local situation. The qualitative method was used to obtain information on the challenges faced with the current manual system, and thereafter the prototyping methodology used to develop the solution which is based on the NFC technology. The intention of this research paper was to revolutionize the ways in which attendance information is collected within institutions of higher learning

A Survey of Smart Classroom Literature

Recently, there has been a substantial amount of research on smart classrooms, encompassing a number of areas, including Information and Communication Technology, Machine Learning, Sensor Networks, Cloud Computing, and Hardware. Smart classroom research has been quickly implemented to enhance education systems, resulting in higher engagement and empowerment of students, educators, and administrators. Despite decades of using emerging technology to improve teaching practices, critics often point out that methods miss adequate theoretical and technical foundations. As a result, there have been a number of conflicting reviews on different perspectives of smart classrooms. For a realistic smart classroom approach, a piecemeal implementation is insufficient. This survey contributes to the current literature by presenting a comprehensive analysis of various disciplines using a standard terminology and taxonomy. This multi-field study reveals new research possibilities and problems that must be tackled in order to integrate interdisciplinary works in a synergic manner. Our analysis shows that smart classroom is a rapidly developing research area that complements a number of emerging technologies. Moreover, this paper also describes the co-occurrence network of technological keywords using VOSviewer for an in-depth analysis

Artificial Intelligence and Education. Guidance for Policy-makers

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts

Protecting Privacy in Indian Schools: Regulating AI-based Technologies' Design, Development and Deployment

Education is one of the priority areas for the Indian government, where Artificial Intelligence (AI) technologies are touted to bring digital transformation. Several Indian states have also started deploying facial recognition-enabled CCTV cameras, emotion recognition technologies, fingerprint scanners, and Radio frequency identification tags in their schools to provide personalised recommendations, ensure student security, and predict the drop-out rate of students but also provide 360-degree information of a student. Further, Integrating Aadhaar (digital identity card that works on biometric data) across AI technologies and learning and management systems (LMS) renders schools a ‘panopticon’. Certain technologies or systems like Aadhaar, CCTV cameras, GPS Systems, RFID tags, and learning management systems are used primarily for continuous data collection, storage, and retention purposes. Though they cannot be termed AI technologies per se, they are fundamental for designing and developing AI systems like facial, fingerprint, and emotion recognition technologies. The large amount of student data collected speedily through the former technologies is used to create an algorithm for the latter-stated AI systems. Once algorithms are processed using machine learning (ML) techniques, they learn correlations between multiple datasets predicting each student’s identity, decisions, grades, learning growth, tendency to drop out, and other behavioural characteristics. Such autonomous and repetitive collection, processing, storage, and retention of student data without effective data protection legislation endangers student privacy. The algorithmic predictions by AI technologies are an avatar of the data fed into the system. An AI technology is as good as the person collecting the data, processing it for a relevant and valuable output, and regularly evaluating the inputs going inside an AI model. An AI model can produce inaccurate predictions if the person overlooks any relevant data. However, the state, school administrations and parents’ belief in AI technologies as a panacea to student security and educational development overlooks the context in which ‘data practices’ are conducted. A right to privacy in an AI age is inextricably connected to data practices where data gets ‘cooked’. Thus, data protection legislation operating without understanding and regulating such data practices will remain ineffective in safeguarding privacy. The thesis undergoes interdisciplinary research that enables a better understanding of the interplay of data practices of AI technologies with social practices of an Indian school, which the present Indian data protection legislation overlooks, endangering students’ privacy from designing and developing to deploying stages of an AI model. The thesis recommends the Indian legislature frame better legislation equipped for the AI/ML age and the Indian judiciary on evaluating the legality and reasonability of designing, developing, and deploying such technologies in schools

Limitations Of Artificial Intelligence

Artificial Intelligence is a groundbreaking technology that is now an established field. It is being used to mimic human capabilities such as speaking, listening, learning, and planning by using different algorithms to process data and produce results depending on the information provided by the user. Artificial Intelligence has been used in several industries when it comes to data processing and decision making. Artificial Intelligence has been invented to help decision and solutionmaking processes using a problem-solving approach. The development of Artificial Intelligence software provides efficiency and acceleration on different kinds of workflows, which will help organizations increase their profit and reduce wastage and costs due to poor productivity. There are already many applications that Artificial Intelligence powers; some of these are Web Search, Cybersecurity, and Machine Translations. All people are now having the benefit of using Artificial Intelligence, and it is beneficial for humanity. Artificial Intelligence has many positive aspects as it produces substantial results in people\u27s daily lives and businesses today; some of the most common Artificial Intelligence technologies used by the industry are robots and Virtual Assistants. Artificial Intelligence are powered by Natural Language Processing (NLP) and Speech Recognition Platform (SRP), but it is not limited to these two (2); many factors need to be considered, but these branches help in interpretation and manipulation of the commands stipulated. Indeed, Artificial Intelligence is rapidly advancing, and many organizations are willing to try and test out what is available in the market. However, others are not convinced with the Artificial Intelligence as there are alleged ethical issues that might cause accountability in a particular manner. This thesis will explain how Artificial Intelligence is used in different fields like Law, Medicine, the Military, and others while discussing the limitations present

Towards the improvement of sheep welfare: Exploring the use of qualitative behavioural assessment (QBA) for the monitoring and assessment of sheep

Challenges faced by sheep in Australia in terms of disease, injury and management may compromise not only health and productivity but also welfare. These challenges represent a growing concern for both producers and the public. Hence there is an obvious need for the development of measures to allow producers, who may have limited access to stock or are constrained by time and/or resource availability, to monitor their sheep. There is a clear benefit to producers being able to readily identify animals whose welfare might be compromised and thus are in need of further care. However, the assessment of animal welfare is challenging under commercial conditions and to date, few measures are available to help producers recognise animals in compromised welfare states. Qualitative behavioural assessment (QBA) is an approach that captures the expressive behaviour of an animal, through the integration and summary of details of behavioural events, posture, and movement. In this way, QBA represents a valuable tool that offers insight into the physical and physiological aspects of animal welfare, and when used in conjunction with other key measures helps to provide a more complete and comprehensive picture of an animal’s welfare state. Furthermore, QBA should be used together with other welfare measures, where it has been proposed to guide the interpretation of welfare data. As a welfare tool, QBA has been applied to assess the behavioural expression in numerous livestock species including pigs and cattle, however, this methodology is less well studied in sheep and more work is needed to validate QBA for practical application. The aim of the research described in this thesis was to investigate whether the QBA methodology could be applied to assess the welfare of sheep subject to various welfare issues relevant to the Australian sheep industry. To this end, over four experimental chapters, QBA was applied to video footage captured of sheep in various states of compromised welfare, including those suffering from common injury and diseases; lameness, inappetence, flystrike, and gastro-intestinal parasitism, and those experiencing pain caused by routine husbandry procedures (ear tagging, castration, mulesing, and tail docking). Moreover, in two experimental chapters (Chapters 4 & 6), video footage was captured of sheep in positive welfare states (reduced gastro-intestinal parasite burden, and habituation to human presence). This video footage was also analysed quantitatively and other welfare measures including those of health/disease status, physical condition and locomotive activity were collected for validation purposes in each study. Over four experimental chapters, it was demonstrated that observers, blind to experimental procedures and treatments, can reach a significant consensus in their interpretation and assessment of the behavioural expression of sheep, and that these assessments can relate meaningfully to the welfare state of the animal. In Chapter 3, observers were able to distinguish between flystruck and non-flystruck sheep using the QBA methodology, and the behavioural expression scores given to each sheep corresponded to the severity of strike and the condition of the wool. In Chapter 4, observers identified differences in the behavioural expression of sheep that related to the severity of gastro-intestinal parasitism (subclinical v. clinical). Moreover, it was discovered that the treatment of sheep to lessen gastro-intestinal parasite burden altered the behavioural expression of parasitised sheep. A significant consensus was also reached amongst observers in the assessment of lambs subject to routine husbandry procedures (ear tagging, castration, mulesing, and tail docking) in Chapter 5. Observers were able to distinguish lambs that were subject to these painful husbandry procedures and were administered either a placebo or analgesics (Tri-Solfen® and meloxicam), from the control lambs which were only restrained. Hence suggesting that the pain caused by these husbandry procedures alters the behavioural patterns and demeanour of lambs in a way that is identifiable to observers using the QBA methodology. Lastly, when observers viewed video footage of sheep traversing a walk-over-weigh (WoW) apparatus in Chapter 6, they were able to distinguish sheep that were either lame or habituated to the test apparatus and human presence, from the control animals. However, in this Chapter, observers were not able to distinguish between all treatment groups evaluated based on their behavioural expression, specifically differences in the demeanour of inappetent and control sheep was not evident, nor were observers able to distinguish between lame and habituated sheep. In summary, the research presented in this thesis indicates that assessments of behavioural expression can be used under most of those conditions investigated to distinguish sheep in poor welfare states due to injury or disease, from those that are healthy. Furthermore, it appears that observers can reliably identify differences in behavioural expression related to positive welfare states. This work has detailed the behavioural expression of sheep as perceived by observers and has led to a greater understanding of the behavioural expression of sheep in different welfare states. It appears that through the assessment of demeanour or body language, QBA offers both relevant and valid assessments which may help producers gain an insight into the welfare state of their sheep. It is suggested that when used in conjunction with other select behavioural measures, QBA may represent a valuable tool for producers to improve the welfare of sheep in their care

Machine Vision: How Algorithms are Changing the Way We See the World

Humans have used technology to expand our limited vision for millennia, from the invention of the stone mirror 8,000 years ago to the latest developments in facial recognition and augmented reality. We imagine that technologies will allow us to see more, to see differently and even to see everything. But each of these new ways of seeing carries its own blind spots. In this illuminating book, Jill Walker Rettberg examines the long history of machine vision. Providing an overview of the historical and contemporary uses of machine vision, she unpacks how technologies such as smart surveillance cameras and TikTok filters are changing the way we see the world and one another. By analysing fictional and real-world examples, including art, video games and science fiction, the book shows how machine vision can have very different cultural impacts, fostering both sympathy and community as well as anxiety and fear. Combining ethnographic and critical media studies approaches alongside personal reflections, Machine Vision is an engaging and eye-opening read. It is suitable for students and scholars of digital media studies, science and technology studies, visual studies, digital art and science fiction, as well as for general readers interested in the impact of new technologies on society.publishedVersio