2020 2nd International Conference on Advancements in Computing (ICAC)最新文献

Generating 2.5D Motion Graphics from 2D Designs 从2D设计生成2.5D动态图形

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357147

Indunil Ranawake, Sathira Guruge, A. Ahamed, Dharshana Kasthurirathne

{"title":"Generating 2.5D Motion Graphics from 2D Designs","authors":"Indunil Ranawake, Sathira Guruge, A. Ahamed, Dharshana Kasthurirathne","doi":"10.1109/ICAC51239.2020.9357147","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357147","url":null,"abstract":"As of the year 2020, the video production industry is worth 31 billion dollars in the United States alone, with more than 6000 businesses and 57000 employees, and keeps growing. The global computer animation market size is anticipated to reach USD 28.30 billion by 2025, according to Grand View Research, Inc. Such significant growth demands the tech industry to introduce better tools for making animations. In this paper, we propose our contribution, specifically for User Interface designers in the field of motion graphics. Ul/UX design is found to be one of the top 5 most trending opportunities for motion designers, and our proposed system allows them to generate a 2.5D animation based on a 2D Futuristic User Interface (FUI) design. The ultimate goal is to reduce the production time of FUI animations and minimize the cost of responding to the client's changes. Obtaining clients' feedback directly on animations rather than still images would improve the client's involvement in production, resulting in greater confidence and loyalty. We implement several image processing techniques such as thinning and pixel clustering for pre-processing the 2D designs to segment the given design into an array of atomic shapes. Since the thinned shapes ensure that any pixel in the design does not have adjacent pixels which are also adjacent to each other, it is possible to utilize mathematical means to approximate the shapes. Our system converts a given 2D design to a collection of animated lines and arcs distributed in 3D space that eventually can be exported to the industry-standard tool, Adobe After Effects.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116632790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Methodology for Coping with Uncertain Information Contained in Natural Language Instructions in a Robotic System 机器人系统中自然语言指令中不确定信息的处理方法

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357141

H.M.Y. Lakshan Waruna Bandara, Dilanka Senali Wijesekera, H.M.T. Dhananjaya Bandara Herath, Dinesha Lakmali Kodagoda, S. Rajapaksha

{"title":"Methodology for Coping with Uncertain Information Contained in Natural Language Instructions in a Robotic System","authors":"H.M.Y. Lakshan Waruna Bandara, Dilanka Senali Wijesekera, H.M.T. Dhananjaya Bandara Herath, Dinesha Lakmali Kodagoda, S. Rajapaksha","doi":"10.1109/ICAC51239.2020.9357141","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357141","url":null,"abstract":"Intelligent service robots are currently being developed to provide services and assistance in different sectors including domestic and household context. Typically, the service tasks of a domestic service robot involve direct interaction with humans. Humans typically express their ideas through voice communication. However, communication through natural language is imprecise because it tends to contain uncertain and unknown information. Therefore, understanding uncertain terms contained in natural language is a crucial capability that an intelligent service robot should possess. Hence, this project which is named as IntelBot is aimed at developing a methodology to cope with uncertain and unknown words contained in a natural language command given to a domestic service robot. In brief, the proposed system can interpret uncertain commands related to speed such as “go very fast” and the uncertain commands related to time such as “go later”. Additionally, if the robot is instructed to identify an object which is regarded to be unknown, as an example “cup” it can interpret and identify that particular object. And for the entire system, a user-friendly interface is developed for the easy control of the robot and the demonstration of the functionalities.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120995876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Sinhala Conversational Interface for Appointment Management and Medical Advice 僧伽罗语会话界面预约管理和医疗咨询

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/icac51239.2020.9357155

D. Rajapakshe, K. N. B. Kudawithana, U. L. N. P. Uswatte, N. Nishshanka, A. V. S. Piyawardana, K. Pulasinghe

{"title":"Sinhala Conversational Interface for Appointment Management and Medical Advice","authors":"D. Rajapakshe, K. N. B. Kudawithana, U. L. N. P. Uswatte, N. Nishshanka, A. V. S. Piyawardana, K. Pulasinghe","doi":"10.1109/icac51239.2020.9357155","DOIUrl":"https://doi.org/10.1109/icac51239.2020.9357155","url":null,"abstract":"This paper proposes an intelligent conversational user interface to assist Sinhala speaking users to make appointments with doctors and to obtain medical advices. This Sinhala Conversational Interface for Appointment Management and Medical Advice (SCI-AMMA) consists of Speech Recognition unit, Query Processing unit, Dialog Management unit, Voice Synthesizer unit, and User Information Management unit to handle user requests and maintain a meaningful dialogue. The SCI-AMMA gets the users' speech utterances and recognize the language content of it for further processing. Language content is further processed using query processing unit to identify users' intent. To fulfil the users' intent, a reply is generated from Dialogue Management Unit. This reply/answer will be delivered to the user by means of a voice synthesizer. The proposed system is successfully implemented using state of the art technology stack including Flutter, Python, Protégé and Firebase. Performance of the system is demonstrated using several sample scenarios/dialogues.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115567236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning 基于传感器数据的机器学习健康预测集成框架

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357134

K. N. Jayaweera, K. M. C Kallora, N. A. C K Subasinghe, L. Rupasinghe, C. Liyanapathirana

{"title":"An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning","authors":"K. N. Jayaweera, K. M. C Kallora, N. A. C K Subasinghe, L. Rupasinghe, C. Liyanapathirana","doi":"10.1109/ICAC51239.2020.9357134","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357134","url":null,"abstract":"According to recent studies, the majority of the world's population shows a lack of concern in their health. As a consequence, the non-communicable disease rate has increased dramatically. Amongst these diseases, heart diseases have caused the most catastrophic situations. Apart from the busy lifestyle, studies also show that stress is another factor that causes these diseases. Therefore, the focus of our research is to provide a user-friendly health monitoring system that causes minimum disturbance to its users. However, many studies have focused on predicting health; very few have focused on its usability. The objective of our research is to predict the possibility of cardiac arrests and the presence of stress in real-time using a wearable device prototype. The system uses biometric signals obtained from the photoplethysmogram sensor embedded in the wearable device to perform real-time predictions. We trained three models using random forest, k-nearest neighbor, and logistic regression classification algorithms to predict sudden cardiac arrests with accuracies 99.93%, 99.10%, and 94.47%, respectively. Further, we trained three additional models to predict stress using the same algorithms with accuracies 99.87%, 96.83%, and 65.00%, respectively. Thus, the results of this study show that an integrated framework, capable of predicting different health-related conditions, through sensor data collected from wearable sensors, is feasible.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129538970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain based Patients' detail management System 基于区块链的患者详细信息管理系统

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/icac51239.2020.9357163

K. Abeywardena, Buddhima Attanayaka, Kabilashan Periyasamy, S. Gunarathna, Uththara Prabhathi, Saduni Kudagoda

{"title":"Blockchain based Patients' detail management System","authors":"K. Abeywardena, Buddhima Attanayaka, Kabilashan Periyasamy, S. Gunarathna, Uththara Prabhathi, Saduni Kudagoda","doi":"10.1109/icac51239.2020.9357163","DOIUrl":"https://doi.org/10.1109/icac51239.2020.9357163","url":null,"abstract":"In the data technology revolution, electronic medical records are a standard way to store patients' information in hospitals. Although some hospital systems using server-based patient detail management systems, they need a large amount of storage to store all the patients' medical reports, therefore affecting the scalability. At the same time, they are facing several difficulties, such as interoperability concerns, security and privacy issues, cyber-attacks to the centralized storage and maintaining adhering to medical policies. Proposed Flexi Medi is a private blockchain based patient detail management system which is expected to address the above problems. Solution proposes a distributed secure ledger to permits efficient system access and systems retrieval, which is secure and immutable. The improved consensus mechanism achieves the consensus of the data without large energy utilization and network congestion. Moreover, Flexi Medi achieves high data security principles based on a combination of hybrid access control mechanism, public key cryptography, and a secure live health condition monitoring mechanism. The proposed solution results in successfully deployed smart contracts according to the roles of the system, real time patient health monitoring with more scalable and access controlled system. The overall objective of this solution is to bring the entire medical industry into a common platform using a decentralized approach to store, share medical details while eliminating the need to maintain printed medical records.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130153864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

An AI based Chatbot to Self-Learn and Self-Assess Performance in Ordinary Level Chemistry 基于人工智能的聊天机器人在普通水平化学中自我学习和自我评估

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357131

Atheeq Mahroof, Vinu Gamage, K. Rajendran, Satyagit Rajkumar, S. Rajapaksha, D. Wijendra

{"title":"An AI based Chatbot to Self-Learn and Self-Assess Performance in Ordinary Level Chemistry","authors":"Atheeq Mahroof, Vinu Gamage, K. Rajendran, Satyagit Rajkumar, S. Rajapaksha, D. Wijendra","doi":"10.1109/ICAC51239.2020.9357131","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357131","url":null,"abstract":"Education is one of the fast-growing fields in the global perspective. Advancement of technology can be used in this sector to provide an effective and a valuable education system. In general, the students are more attracted to displays rather than the textbooks. In Sri Lanka, there is an inadequacy of resources and teachers cannot provide one on one attention to the students. Sri Lanka is not equipped with any platform to self-learn or self-evaluate their performance using an application either. Fortunately, “Edubot” acts as a solution for the stated research gap by providing a self-learning and self-evaluating AI based chatbot platform for Ordinary Level students in Chemistry domain. The self-learning component will provide the students a classroom environment by providing interactive tutorials. Explanatory responses would be given by Edubot by capturing doubts raised by the students and the self-evaluating component will provide an exam-based environment in which the Edubot auto generates the question and answers. The research finding shows that each component has an accuracy of more than 70 percent and helps to achieve the main goal of increasing the resources available to the ordinary level students in the Chemistry domain. This would then lead to an increase in the pass rate of the chemistry subject in the G.C.E Ordinary Level exam.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127013017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Automated Diabetic Retinopathy Screening With Montage Fundus Images 用蒙太奇眼底图像自动筛选糖尿病视网膜病变

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357137

Sarala Kumari, N. Padmakumara, Waruni Palangoda, Chanuka Balagalla, P. Samarasingha, Aruna Fernando, N. Pemadasa

{"title":"Automated Diabetic Retinopathy Screening With Montage Fundus Images","authors":"Sarala Kumari, N. Padmakumara, Waruni Palangoda, Chanuka Balagalla, P. Samarasingha, Aruna Fernando, N. Pemadasa","doi":"10.1109/ICAC51239.2020.9357137","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357137","url":null,"abstract":"Diabetic retinopathy (DR), also known as diabetic eye disease is one of the major causes of blindness in the active population. The longer a person has diabetes, higher the chances of developing DR. This research paper is an attempt towards finding an automatic way to staging DR using montage eye images through artificial intelligence (AI). Convolutional neural networks (CNNs) play a big role in DR detection. Using transfer learning and hyper-parameter tuning DR staging is analyzed through different models. VGG16 gave the highest classification accuracies for the stages Proliferative DR (PDR) & Non-proliferative DR (NPDR). The highest level of NPDR is severe DR which achieved 94.9% classification accuracy (CA) & special features like cotton wool & laser treatment performed at 83.3% CA for each. Moreover, by using patient's history data such as age, right eye & left eye value accuracies & diabetic diagnosed year, system can predict the DR stages. That predictive model has achieved the best CA of 94 % by using Xgboost classifier. Overall, a fully functional app has been developed to detect DR stages with Montage Fundus images using AI.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125993905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Water-Gate Controlling System for Paddy Fields 水田水闸自动控制系统

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357312

W.A.K.L. Sanjula, K.T.W. Kavinda, M.A.K. Malintha, W. Wijesuriya, S. Lokuliyana, R. de Silva

{"title":"Automated Water-Gate Controlling System for Paddy Fields","authors":"W.A.K.L. Sanjula, K.T.W. Kavinda, M.A.K. Malintha, W. Wijesuriya, S. Lokuliyana, R. de Silva","doi":"10.1109/ICAC51239.2020.9357312","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357312","url":null,"abstract":"The Internet of things (IoT) is an attractive technology being used in almost every aspect of the world today. It allows connecting any of the objects that want to communicate with each other and to transfer data without human interaction. In this paper proposed system is discussed on canal automation for a smart irrigation system using IoT concepts. Automated Water Gate Controlling System collects few environmental factors through a smart module embedded with sensors to communicate with the water gate of the paddy field. Cloud computing is used to store a large amount of data gathered by the sensor module. All real-time sensed data are processed and demonstrated on a web dashboard with a convenient graphical user interface along with a rain and reservoir water level prediction analysis to the users. Automation supports distance monitoring and controlling by allowing minimum human intervention. This paper provides a solution for traditional irrigation systems to confront rural farming interferences and global climatic changes via a cloud-based automated wireless communication system to water their paddy fields, monitor them and smartly control them.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"82 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131924126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

CEYLAGRO: Information Technological Approach for an Optimized and Centralized Agriculiture Platform CEYLAGRO:优化和集中农业平台的信息技术途径

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357313

T. Kaushalya, B. Y. S. Wijewardana, A. Karunasena, M. G. G. Kavishika, S. Gamage, L. Weerasinghe

{"title":"CEYLAGRO: Information Technological Approach for an Optimized and Centralized Agriculiture Platform","authors":"T. Kaushalya, B. Y. S. Wijewardana, A. Karunasena, M. G. G. Kavishika, S. Gamage, L. Weerasinghe","doi":"10.1109/ICAC51239.2020.9357313","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357313","url":null,"abstract":"Sri Lankan Agriculture sector can be considered as a crucial component as it contributes 18% of country GDP. As native farmers still cling to inapplicable traditional theorems and practices to track customer's vegetable consumption trends, they failed to assure a “good price” for their harvest. Also, the plants are prone to many diseases and pests' attacks which causes loss of the harvest. Unreliable problem identification, poor knowledge on application of fertilizers and pesticides have caused the farmers to lose their profits. As a solution to mitigate these problems, this study has built a computerized system with a vegetable price prediction system and a plant disease, pest identification system. Taking Potato as an example, the parameters of the time series model were analyzed through experiment and has built the price predictor using ARIMA model. Also, with advanced Image processing and CNN techniques Plant disease, pest identifier has built. Desirable results of the entire system have been achieved with more than 94%-97% rate of accuracy. The ultimate goal of this study is to achieve the optimal growth of the sector by navigating the users for a quality and effective decision making by reliable market trends and problem identification.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132061394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Learning Approach to Outbreak related Tweet Detection 爆发相关Tweet检测的深度学习方法

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/icac51239.2020.9357274

B. Jayawardhana, R. Rajapakse

{"title":"A Deep Learning Approach to Outbreak related Tweet Detection","authors":"B. Jayawardhana, R. Rajapakse","doi":"10.1109/icac51239.2020.9357274","DOIUrl":"https://doi.org/10.1109/icac51239.2020.9357274","url":null,"abstract":"Due to the popularity of social media around the world, people use to report and discuss real-world events, personal health complications, and disaster situations through these platforms. These social media data streams can be used to track and detect different types of outbreaks. A mechanism is needed to identify outbreak-related tweets to predict the outbreak in advance. In this paper, we propose a deep learning model that can detect tweets related to different outbreaks Epidemics, Public Disorders, and Disasters. GloVe (Global Vectors for Word Representation) embeddings are used as the feature extraction technique as it can capture the semantic meanings of the tweets. Long Short-term Memory (LSTM) which is a specialized Recurrent Neural Network architecture is used as the classification algorithm. In the process, first, outbreak-related tweets were manually collected and curated. Pretrained GloVe word embeddings of 100 dimensions were then used to represent the words of the tweets. As the next step, a Deep Learning Model was trained by using LSTM technique on the curated dataset. Finally, the performance of the model was evaluated using a different dataset. With the results, it can be concluded that the proposed deep learning model is an accurate approach for outbreak-related tweet detection.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132381891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1