Optimal QoS aware multiple paths web service composition using heuristic algorithms and data mining techniques

The goal of QoS-aware service composition is to generate optimal composite services that satisfy the QoS requirements defined by clients. However, when compositions contain more than one execution path (i.e., multiple path's compositions), it is difficult to generate a composite service that simultaneously optimizes all the execution paths involved in the composite service at the same time while meeting the QoS requirements. This issue brings us to the challenge of solving the QoS-aware service composition problem, so called an optimization problem. A further research challenge is the determination of the QoS characteristics that can be considered as selection criteria. In this thesis, a smart QoS-aware service composition approach is proposed. The aim is to solve the above-mentioned problems via an optimization mechanism based upon the combination between runtime path prediction method and heuristic algorithms. This mechanism is performed in two steps. First, the runtime path prediction method predicts, at runtime, and just before the actual composition, execution, the execution path that will potentially be executed. Second, both the constructive procedure (CP) and the complementary procedure (CCP) heuristic algorithms computed the optimization considering only the execution path that has been predicted by the runtime path prediction method for criteria selection, eight QoS characteristics are suggested after investigating related works on the area of web service and web service composition. Furthermore, prioritizing the selected QoS criteria is suggested in order to assist clients when choosing the right criteria. Experiments via WEKA tool and simulation prototype were conducted to evaluate the methods used. For the runtime path prediction method, the results showed that the path prediction method achieved promising prediction accuracy, and the number of paths involved in the prediction did not affect the accuracy. For the optimization mechanism, the evaluation was conducted by comparing the mechanism with relevant optimization techniques. The simulation results showed that the proposed optimization mechanism outperforms the relevant optimization techniques by (1) generating the highest overall QoS ratio solutions, (2) consuming the smallest computation time, and (3) producing the lowest percentage of constraints violated number

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

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502