Centre of Social Media Innovations for Communities

Transcription

1 Centre of Social Media Innovations for Communities Mobile Devices and Public Service: Session 4 Public Safety and Information 4 th November 2013 Presented by Roger Zimmermann IIT Bombay

2 SingaporeIDMResearchProgramme Interac5veDigitalMediaProgrammeOffice(IDMPO),fundedbythe Na5onalResearchFounda5on(NRF)wasestablishedinAugust2006 asaninteriagencyoujithostedbythemediadevelopmentauthority (MDA)tocoordinatemul5Iagencyefforts.LongItermvision: DevelopavibrantIDMsector EstablishSingaporeasapreferredloca5onforR&Dandinnova5on Achievebreakthroughsforthenextgenera5onmedia OneofthreeNRFresearchareas(IDM,lifesciences,environmental technologies) NRF IDM:$500mfundingover5+5years SeveralFundingIni0a0ves(IHL,startups,industry): COSMICisoneofaboutadozen fundingof$10mover5years MDA IDMPO COSMIC

3 COSMICMemberIns5tu5ons Na0onalUniversityofSingapore(NUS), NanyangTechnologicalUniversity(NTU)and IndianIns0tuteofTechnology,Bombay(IITB) FundedbytheInterac0veDigitalMediaProgrammeOffice (IDMPO)hostedbytheMediaDevelopmentAuthority(MDA). Researchobjec5ve: To empower people in the community through social media innovations that improve the way they live, work, and play. Specifically, the people targeted are those involved in the informal economy.

4 TargetPopula5on Ramesh Jain, UCI Top 1.5 Billion TOP: Ready and well-served Middle 3 Billion MOP: Ready but not well-served Bottom 2 Billion BOP: Not ready

5 NUS-COSMIC LabSpaces NTU-COSMIC IITB-COSMIC

6 FocusAreas Agriculture E.g., pest monitoring and control E.g., information dissemination Infrastructure E.g., tools for mobile health E.g., easy to use video tools E.g., tools for insect sound classification E.g., tools for enabling social interactions Healthcare E.g., health monitoring and remedial action E.g., disease prevention and control Mo-Buzz

7 Foreachapplica5onarea:Process Innova5onthroughSocialMedia TechnologyInfrastructureforMOP DirectedlargeIscale informa5ongathering ProcessInnova0onthroughSocialMedia DirectedlargeIscale Advancedanaly5csfor sensemaking PersonalizedlargeIscale informa5ondispensing Informa5on gatheringapps Sensemaking apps Informa5on dispensingapps 7

8 COSMIC Agriculture Mobile Pest Management for Underserved Communities NUS Team: Klarissa Chang, Bernard Tan, Chew Lim Tan, Chen-Chieh Feng, Maffee Wan, Roger Zimmerman IITB Team: Ravi Poovaiah, Narendra Shah, Anirudha Joshi Researchers: Avijit Sengupta, Jia Rubio, Michelle Gwee, Yimeng Deng, Yi Wu, Yeow Chuan Ng, Adrian Tan, Wenyong Chua, Rax Suen

9 SocialMediafor ProcessInnova5oninAgriculture Agriculture Industry Annual Income: MOP Farmers US$400land acre, TOP Farmers US$800land acre Low productivity: low crop yields and low quality harvest Adverse environment: low resistance to pestsdiseases Magnitude of Problem Number of MOP Farmers: 406 million (India), 430 million (China), 3.2 billion (World) GDP for Agriculture: US$263 billion (India), US$536 billion (China), US$3585 billion (World) Agricultural Losses due to Pests: 35% crops (India), 38% crops (China), 32% crops (World)

10 COSMICAgriculture:Objec5ves MISSION To leverage on social media for process innovation in agriculture To design, develop and deploy advanced social mediamobile solutions with high consumer value GOALS Enhance information relevance through customized socialnetworking and location-based services Provide predictive information for preventive measures through spatial-temporal analytics Disseminate timely personalized solutions to reduce crop losses and save costs

11 Transla'onal)Research:)) MobilePestManagementforUnderservedCommuni0es कम स$%वध ) त सम$द य. लए म ब इल क ट )ब8धन Mobil Hama Pengelolaan Untuk Terlayani Komunitas Directed large-scale information gathering Info Gathering स:चन सभ क )%=य Informasi Mengumpulkan Interface design Location-based services Advanced analytics for sense making DirectedlargeIscale Sense Making स:चन क समझन Pengertian Pembuatan Spatial-temporal analytics Image recognition Personalized large-scale information dispensing Info Dissemination स:चन )स र Informasi Diseminasi Alert notification Advice recommendations

12 KeyProducts(mPest,VillageTree) Monitor and Identify Pests SocialComponents IndividualsasSensors E.g. I saw whiteflies on my rice crop Location: Pabal Village, Maharashtra Time: :13pm Customized networking services to collect individual reports, images, location and time AutomatedComponents Image recognition Environment sensor Predict Pest Infestation Type of Crop 1. Corn 2. Wheat 3. Barley 4. Oats mpest VillageTree Mobilepest management applica5on Forecast trends using collective knowledge Spa5alITemporalAnaly5cs Day1 Day2 Prevent and Control Pests SocialComponents PersonalizedAdvice E.g. Please spray acephate 2g per lit water to the red area immediately Customized networking services to provide personalized recommendations AutomatedComponents Sound wave emission

13 OngoingandPastFieldStudies India Pabal (Vigyan Ashram) China - Jilin Farmer Using Our App India Panvel Indonesia - Salatiga Image Icon-based Smart Phone App India Tharad, Gujerat India Neral (Saguna Baug) Text Audio-based Feature Phone App

14 Industry Collaborators: Agri-Food & Veterinary Authority of Singapore (AVA) IconTextImage-based Smart Phone Application for Commercial Farms, Community Growers and AVA Personnel IBM Research, India (Spoken Web) Audio-based Feature Phone Application for Local Farmers International Rice Research Institute (IRRI) Data Warehousing (pending) Indian NGO and Government Agency: Vigyan Ashram (Pabal) KVK (Baramati) Ongoing Field Studies: India (Neral, Nasik, Pabal, Baramati, Tharad) China (Jilin, ) Indonesia (Salatiga) Singapore For more information, please contact us: Dr. Klarissa Chang (Project Leader) Department of Information Systems National University of Singapore Tel: (65) changtt@comp.nus.edu.sg

15 InsectSoundClassifica5on and GeoreferencedMobileVideo Presentedby PrincipalInves5gator:APRogerZimmermann Media Development Authority

16 InsectSoundClassifica5on SystemFramework Mobileapp(Android) Server(cloudprocessing) Threespectralfeaturesextracted Trainedmachinelearningmodeltopredictclass

17 InsectSoundClassifica5on TechnicalApproach Exploitspectralfeaturesofinsectsounds Typicalspectralfeatures(STFT,MelIspectrumandMFCC) Classifica5on:mappingfeaturetoinsectclass SVM Improvesystemreliabilityviasequencedetec5on SVM hmm Sokclassifica5onwithlikelihoodinforma5on Generateanewvector;eachdimensioncorrespondsto oneclass

18 Accomplishments AndroidApp Androidappwithcloudserverbackend Record(client):microphone Play(client) Recognize (clientserver)

19 Accomplishments UCRInsectContest UCRInsectClassifica5onContestheldbyUCR,providing 45,000audiodatasamples NUSteamachieved4 th placeoverallinphasei

20 GeoVidMo5va5on Trends UserIgeneratedvideo(UGV)contentisgrowing rapidly. Mobiledevicesmakeiteasytocapturevideo. Challenge Videoiss5lldifficulttomanageandsearch. SensorKRichVideo Loca5onanddirec5oninforma5on cannowbecollectedthrougha numberofsensors (e.g.,gpsandcompass).

21 Viewing Direction Path Viewable Scene Locations Compass WebsitewithAPIathnp:geovid.org GPS

22 Benefits Easyandfastsearchofvideos ShowmeMarinaBaySandsfromthesouth ShowmetheFlyerfromvariousdirec5ons Automa5corganiza5onofvideos(events, loca5ons,taggingwithkeywords, )

23 Video Demos Direc0onalSearch SurroundSearch

24 Directional Search

25 Surround Search

26 Accomplishments (1) MeritAwardinthemYprize)Global) Developer)ChallengebyYTL Communica5ons,Malaysia Collabora5onwiththeIntegrated MediaSystemsCenter(IMSC),USC NATOSummit(May 12), SchoolofMediaArts PBSNewsHour(Jan 13) MediaQsystem integra5on

27 ThankYou Q&A COSMICCoIDirectors: hnp: hnp:cosmic.nus.edu.sg