2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)最新文献_第4页

Seeking Nonhuman Advice: Ancient and Modern 寻求非人类的建议:古代和现代

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311038

Zachary S. Hutchinson

引用次数: 0

Edge-Cloud Collaboration Architecture for AI Transformation of SME Manufacturing Enterprises 面向中小制造企业AI转型的边缘云协同架构

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311075

Jeffrey Ing, Jackie Hsieh, Dennis Hou, Janpu Hou, Tuo Liu, Xiaobin Zhang, Yuxi Wang, Yen-Ting Pan

{"title":"Edge-Cloud Collaboration Architecture for AI Transformation of SME Manufacturing Enterprises","authors":"Jeffrey Ing, Jackie Hsieh, Dennis Hou, Janpu Hou, Tuo Liu, Xiaobin Zhang, Yuxi Wang, Yen-Ting Pan","doi":"10.1109/AI4G50087.2020.9311075","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311075","url":null,"abstract":"A new edge-cloud collaboration architecture for small and medium sized enterprises (SME) manufacturers is introduced in this work. Lager manufacturers with sufficient resources already invested heavily in smart manufacturing system. There are rapidly emerging needs to help small and medium sized enterprises manufacturers with limited resources to implement smart and highly adaptable manufacturing systems to compete and sustain in global economy. We present an illustrative case study of how to implement and manage AI projects in practice for SME manufacturers. We demonstrated how our proposed architecture can help accelerate one of the United Nations Sustainable Development Goals, i.e. Goal 9: Industry, Innovation and Infrastructure, by exhibiting the practicality and scalability of our proposed solution. In particular, we elaborate on the key manufacturing issues concerning company-wide resource distribution, problem solving and decision making. It will be demonstrated that more advanced AI systems such as deep learning and deep reinforcement learning emerge naturally with one's quality management system which already in place and come with a well-defined semantics of their process functions in the context of collaborative edge-cloud architecture. Furthermore, equipment used in the smart factory includes manufacturing equipment, functional testing equipment and defect detection equipment. In this work, we will present the management and implementation of on-device AI defect detection and classification to show the feasibility and effectiveness of the edge-cloud collaboration architecture approach.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130092584","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

Artificial Intelligence is Transforming the World Development Indicators 人工智能正在改变世界发展指标

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311079

Abduladhim Ashtaiwi

{"title":"Artificial Intelligence is Transforming the World Development Indicators","authors":"Abduladhim Ashtaiwi","doi":"10.1109/AI4G50087.2020.9311079","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311079","url":null,"abstract":"Many countries and international organizations gather lots of Key Performance Indicators (KPIs) to assess local and/or global development progress. Using these KPI datasets, domain experts apply many statistical tools to extract knowledge and generate recommendations. As the world increasingly approaches globalization, more interdependence development is formed leading to complex development formulas. Artificial Intelligence (AI) is almost transforming all industries, and hence can be used to expand domain expert's capabilities to understand and manage the complex development formulas. This work utilizes AI and Machine Learning (ML) in the World Development Indicators (MDIs) databases to extract data patterns. In summary, this work performs the following: identify the most important indicators, i.e., informative, discriminating and independent indicators. Identify the common properties or characteristics of different development plans and hence cluster them to understand their trend and behavior. Create prediction models to predict the development projection. The created prediction models then can be used to perform guided efficient development plans to attain sustainable developments.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132775070","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: 3

Digital Crop Health Monitoring by Analyzing Social Media Streams 通过分析社交媒体流进行数字作物健康监测

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9310985

Priyamvada Shankar, Christian Bitter, M. Liwicki

{"title":"Digital Crop Health Monitoring by Analyzing Social Media Streams","authors":"Priyamvada Shankar, Christian Bitter, M. Liwicki","doi":"10.1109/AI4G50087.2020.9310985","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9310985","url":null,"abstract":"This paper introduces the idea of using social media streams like Twitter to identify occurrences of crop diseases. Climate change and changes in agriculture practices have contributed to a change in crop disease dynamics leading to an increase in crop damages. Monitoring crop disease occurrences across regions is helpful for farmers to prepare for such adverse situations and make effective use of crop protection products thus ensuring enough produce for the growing population and protection of the environment. We investigate Machine Learning and Natural Language Processing techniques in order to spot agricultural discussions on Twitter; then analyze, categorize, and group them; so they can be used by a stakeholder to identify crop disease incidences, patterns, and trends at the regional scale. Current systems using keyword based search of agricultural diseases do not always yield agriculturally relevant tweets and those that do could talk on a range of sub-topics. Therefore, text classification forms the core component of this work. A two fold classification process is employed, classifying agriculturally relevant tweets from the rest and then performing fine-grained categorization on them. The resulting model for agricultural tweets classification performs with 93% accuracy and the fine grained categorization model that categorizes tweets into 6 categories gives 75% accuracy. A prototype of an interactive web based disease monitoring application is also presented. The location estimation is not always accurate but nonetheless, this work acts as a proof of concept for the introduction of social media as a novel data source in precision farming.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115662519","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

A Tiny CNN Architecture for Identifying Bat Species from Echolocation Calls 从回声定位呼叫中识别蝙蝠物种的微型CNN架构

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311084

I. Zualkernan, J. Judas, Taslim Mahbub, Azadan Bhagwagar, Priyanka Chand

{"title":"A Tiny CNN Architecture for Identifying Bat Species from Echolocation Calls","authors":"I. Zualkernan, J. Judas, Taslim Mahbub, Azadan Bhagwagar, Priyanka Chand","doi":"10.1109/AI4G50087.2020.9311084","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311084","url":null,"abstract":"Effective monitoring of bat populations will contribute towards the United Nations' SGD 15 which is tied to maintaining biodiversity and SGD 3 which is about maintaining good health and well-being. Bat species are particularly sensitive to anthropogenic pressures and monitoring bat populations trends can serve as a good indicator of an ecosystem's health. Bats have also been linked to at least 60 strains of viruses (including Covid-19) that can infect humans. Monitoring bats can help contribute towards achieving both SGD goals. However, monitoring bats is a difficult and resource-consuming task. This paper investigates how monitoring can be enhanced by automatically identifying bats using audio data from their echolocation calls. Such a system will ease assessing their populations status, trends and habitats. A Convolutional Neural Network (CNN) was developed to identify eight different bat species based on their vocalizations. Alternative CNN models using Short-Term Fourier Transforms (STFTs), Mel-spectrograms Filter banks (MSFB), and Mel Frequency Cepstral Coefficients (MFCC) were developed. The CNN models were optimized using Hyperband. The best model used MSFB features and had only 220K parameters (0.892 MB) and can easily be embedded into a small handheld device. Using 10-fold testing, the best CNN model had an average Accuracy of 97.51% and Average F1-score of 0.9578.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131787531","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

The Strategic Approach for Successful Realistic Improvements in Practical Vehicle Routing Algorithms 实用车辆路径算法成功改进的策略方法

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311034

E. Žunić, D. Donko

{"title":"The Strategic Approach for Successful Realistic Improvements in Practical Vehicle Routing Algorithms","authors":"E. Žunić, D. Donko","doi":"10.1109/AI4G50087.2020.9311034","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311034","url":null,"abstract":"Vehicle Routing Problem (VRP) is the process of set selection of the most convenient route in a network of roads vehicles are supposed to drive along when serving customers. Although vehicle problems solutions are being researched and improved in science, this problem is also important in industry, and the reason is the potential reduction of the shipping cost. Transport management is the central problem in logistics of one company, and the choice of optimal routes is one of the crucial functions in that process. However, as much as routes are algorithmically optimal, and as much as they include predefined limitations, there are some factors in the realistic environment which perhaps are not adequately treated during the creating the given routes. The innovative approach of adjustment of most of the parameters and factors necessary for the VRP algorithms being used in reality is presented in this work. It is based on the principle of successful feasibility of the given routs in realistic environment. The feasibility of the routes on the realistic example of one of the greatest distribution companies in Bosnia and Herzegovina has been significantly increased by introducing the realistic settings and improvements by comparative results before and after the introduction of the suggested modifications.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128354369","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

Mapping New Informal Settlements Using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis 使用机器学习和时间序列卫星图像绘制新的非正式定居点:在委内瑞拉移民危机中的应用

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-08-27 DOI: 10.1109/AI4G50087.2020.9311041

I. Tingzon, Niccolo Dejito, Ren Avell Flores, Rodolfo De Guzman, L. Carvajal, Katerine Zapata Erazo, Ivan Enrique Contreras Cala, Jeffrey Villaveces, Daniela Rubio, R. Ghani

{"title":"Mapping New Informal Settlements Using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis","authors":"I. Tingzon, Niccolo Dejito, Ren Avell Flores, Rodolfo De Guzman, L. Carvajal, Katerine Zapata Erazo, Ivan Enrique Contreras Cala, Jeffrey Villaveces, Daniela Rubio, R. Ghani","doi":"10.1109/AI4G50087.2020.9311041","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311041","url":null,"abstract":"Since 2014, nearly 2 million Venezuelans have fled to Colombia to escape an economically devastated country during what is one of the largest humanitarian crises in modern history. Non-government organizations and local government units are faced with having to identify, assess, and monitor rapidly growing migrant communities in order to provide urgent humanitarian aid. However, with many of these displaced populations living in informal settlements areas across the country, locating migrant settlements over large territories can be a major challenge. To address this problem, we propose a novel approach for rapidly and cost-effectively locating new and emerging informal settlements using machine learning and publicly accessible Sentinel-2 time series satellite imagery. We demonstrate the effectiveness of the approach in identifying potential Venezuelan migrant settlements that have emerged between 2015 and 2020 in Colombia. Finally, we emphasize the importance of post-classification verification and present a two-step validation approach consisting of (1) remote validation using Google Earth and (2) on-the-ground validation through the Premise App, a mobile crowdsourcing platform.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128580533","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

SSM-Net for Plants Disease Identification in Low Data Regime 低数据体制下植物病害鉴定ssm网络

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-05-27 DOI: 10.1109/AI4G50087.2020.9311073

Shruti Jadon

{"title":"SSM-Net for Plants Disease Identification in Low Data Regime","authors":"Shruti Jadon","doi":"10.1109/AI4G50087.2020.9311073","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311073","url":null,"abstract":"Plant disease detection is an essential factor in increasing agricultural production. Due to the difficulty of disease detection, farmers spray various pesticides on their crops to protect them, causing great harm to crop growth and food standards. Deep learning can offer critical aid in detecting such diseases. However, it is highly inconvenient to collect a large volume of data on all forms of the diseases afflicting a specific plant species. In this paper, we propose a new metrics-based few-shot learning SSM net architecture, which consists of stacked siamese and matching network components to address the problem of disease detection in low data regimes. We demonstrated our experiments on two datasets: mini-leaves diseases and sugarcane diseases dataset. We have showcased that the SSM-Net approach can achieve better decision boundaries with an accuracy of 92.7% on the mini-leaves dataset and 94.3% on the sugarcane dataset. The accuracy increased by 10% and 5% respectively, compared to the widely used VGG16 transfer learning approach. Furthermore, we attained F1 score of 0.90 using SSM Net on the sugarcane dataset and 0.91 on the mini-leaves dataset. Our code implementation is available on Github: https://github.com/shrutijadon/PlantsDiseaseDetection.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129013577","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: 14

An Ecosystem Approach to Ethical AI and Data Use: Experimental Reflections 伦理人工智能和数据使用的生态系统方法:实验反思

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-05-12 DOI: 10.1109/AI4G50087.2020.9311069

M. Findlay, J. Seah

{"title":"An Ecosystem Approach to Ethical AI and Data Use: Experimental Reflections","authors":"M. Findlay, J. Seah","doi":"10.1109/AI4G50087.2020.9311069","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311069","url":null,"abstract":"While we have witnessed a rapid growth of ethics documents meant to guide artificial intelligence (AI) development, the promotion of AI ethics has nonetheless proceeded with little input from AI practitioners themselves. Given the proliferation of AI for Social Good initiatives, this is an emerging gap that needs to be addressed in order to develop more meaningful ethical approaches to AI use and development. This paper offers a methodology-a ‘shared fairness' approach-aimed at identifying AI practitioners' needs when it comes to confronting and resolving ethical challenges and to find a third space where their operational language can be married with that of the more abstract principles that presently remain at the periphery of their work experiences. We offer a grassroots approach to operational ethics based on dialog and mutualised responsibility: this methodology is centred around conversations intended to elicit practitioners perceived ethical attribution and distribution over key value-laden operational decisions, to identify when these decisions arise and what ethical challenges they confront, and to engage in a language of ethics and responsibility which enables practitioners to internalise ethical responsibility. The methodology bridges responsibility imbalances that rest in structural decision-making power and elite technical knowledge, by commencing with personal, facilitated conversations, returning the ethical discourse to those meant to give it meaning at the sharp end of the ecosystem. Our primary contribution is to add to the recent literature seeking to bring AI practitioners' experiences to the fore by offering a methodology for understanding how ethics manifests as a relational and interdependent sociotechnical practice in their work.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134361378","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: 6