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Reasons, Rewards, Regrets: Privacy Considerations in Location Sharing As an Interactive Practice
Reasons, Rewards, Regrets: Privacy Considerations in Location Sharing as an Interactive Practice Sameer Patil, Greg Norcie, Apu Kapadia Adam J. Lee School of Informatics and Computing Department of Computer Science Indiana University University of Pittsburgh 901 E 10th St 210 S Bouquet St Bloomington, IN 47408 USA Pittsburgh, PA 15260 USA {patil, gnorcie, kapadia}@indiana.edu [email protected] ABSTRACT systems focused on enabling collaborators to locate each Rapid growth in the usage of location-aware mobile phones other (e.g., [25, 36]). Such systems typically required orga- has enabled mainstream adoption of location-sharing ser- nizations to install their own location-mapping infrastruc- vices (LSS). Integration with social-networking services ture, either developed in-house by the organization or pur- (SNS) has further accelerated this trend. To uncover how chased from companies such as Ubisense, which offers a 3D localization infrastructure. To scale globally without such these developments have shaped the evolution of LSS usage, 1 we conducted an online study (N = 362) aimed at under- custom infrastructure, services such as Dodgeball allowed standing the preferences and practices of LSS users in the users to send their current locations as text messages to US. We found that the main motivations for location sharing the service, which then alerted friends if they opportunis- were to connect and coordinate with one's social and pro- tically happened to be near each other. Eventually WiFi fessional circles, to project an interesting image of oneself, and GPS-based localization built into smartphones led to and to receive rewards offered for `checking in.' Respon- the development of various stand-alone location-sharing ser- dents overwhelmingly preferred sharing location only upon vices (LSS) such as Foursquare (https://www.foursquare. -
Domestic Biometric Data Operator
Domestic Biometric Data Operator Sector: IT–ITeS Class XI Domestic Biometric Data Operator (Job Role) Qualification Pack: Ref. Id. SSC/Q2213 Sector: Information Technology and Information Technology enabled Services (IT-ITeS) 171105 NCERT ISBN 978-81-949859-6-9 Textbook for Class XI 2021-22 Cover I_IV.indd All Pages 19-Mar-21 12:56:47 PM ` ` 100/pp.112 ` 90/pp.100 125/pp.136 Code - 17918 Code - 17903 Code - 17913 ISBN - 978-93-5292-075-4 ISBN - 978-93-5292-074-7 ISBN - 978-93-5292-083-9 ` 100/pp.112 ` 120/pp. 128 ` 85/pp. 92 Code - 17914 Code - 17946 Code - 171163 ISBN - 978-93-5292-082-2 ISBN - 978-93-5292-068-6 ISBN - 978-93-5292-080-8 ` 65/pp.68 ` 85/pp.92 ` 100/pp.112 Code - 17920 Code - 171111 Code - 17945 ISBN - 978-93-5292-087-7 ISBN - 978-93-5292-086-0 ISBN - 978-93-5292-077-8 For further enquiries, please visit www.ncert.nic.in or contact the Business Managers at the addresses of the regional centres given on the copyright page. 2021-22 Cover II_III.indd 3 10-Mar-21 4:37:56 PM Domestic Biometric Data Operator (Job Role) Qualification Pack: Ref. Id. SSC/Q2213 Sector: Information Technology and Information Technology enabled Services (IT-ITeS) Textbook for Class XI 2021-22 Prelims.indd 1 19-Mar-21 2:56:01 PM 171105 – Domestic Biometric Data Operator ISBN 978-81-949859-6-9 Vocational Textbook for Class XI First Edition ALL RIGHTS RESERVED March 2021 Phalguna 1942 No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the prior PD 5T BS permission of the publisher. -
Location-Based Services: Industrial and Business Analysis Group 6 Table of Contents
Location-based Services Industrial and Business Analysis Group 6 Huanhuan WANG Bo WANG Xinwei YANG Han LIU Location-based Services: Industrial and Business Analysis Group 6 Table of Contents I. Executive Summary ................................................................................................................................................. 2 II. Introduction ............................................................................................................................................................ 3 III. Analysis ................................................................................................................................................................ 3 IV. Evaluation Model .................................................................................................................................................. 4 V. Model Implementation ........................................................................................................................................... 6 VI. Evaluation & Impact ........................................................................................................................................... 12 VII. Conclusion ........................................................................................................................................................ 16 1 Location-based Services: Industrial and Business Analysis Group 6 I. Executive Summary The objective of the report is to analyze location-based services (LBS) from the industrial -
Glenbard West Student Handbook
2021 - 2022 CASTLE KEYS GLENBARD WEST HIGH SCHOOL Glenbard West High School Glenbard District Office 670 Crescent Blvd. 596 Crescent Blvd. Glen Ellyn, IL 60137 Glen Ellyn, IL 60137 Telephone (630) 469-8600 Telephone (630) 469-9100 Fax (630) 469-8615 Fax (630) 469-9107 www.glenbardwesths.org www.glenbard87.org SCHOOL ADMINISTRATION Dr. Peter Monaghan Principal Mr. Christopher Mitchell Assistant Principal, Student Services Ms. Stacy Scumaci Assistant Principal, Operations Dr. Rebecca Sulaver Assistant Principal, Instruction Mr. Joe Kain Assistant Principal, Athletics Mr. Jordan Poll Dean of Students Mr. Peter Baker Dean of Students Ms. Celeste Rodriguez Dean of Students BOARD OF EDUCATION Ms. Judith Weinstock, President Ms. Margaret DeLaRosa, Vice President Mr. Robert Friend, Parliamentarian Mr. Kermit Eby Mr. John Kenwood Ms. Martha Mueller Ms.Mireya Vera Dr. David Larson, Superintendent Table of Contents ATHLETICS A-42 Coaching Staff A-42 Interscholastic A-42 ATTENDANCE A-35 CONDUCT AND DISCIPLINE A-37 Behavior Intervention Assignment (B.I.A.) A-41 Bullying/Harassment/Intimidation A-38 Bus Conduct A-38 Detention A-41 Disciplinary Consequences A-40 Dress Code A-22 Extended Day Detention (E.D.D.) A-41 Electronic Devices A-31 Out of School Suspension A-41 Restorative Intervention Alternative (R.I.A.) A-41 Search & Seizure A-42 DISTRICT 87 INFORMATION AND BOARD A - 51 POLICIES SCHOOL COUNSELING SERVICES A-27 Counselor Assignments A-27 STUDENT ACTIVITIES A-43 HEALTH CENTER A-28 P.E. Medicals A-28 LIBRARY MEDIA DEPARTMENT A-29 Elliott Library -
Crystal Reports Activex Designer
Quiz List—Reading Practice Page 1 Printed Wednesday, March 18, 2009 2:36:33PM School: Churchland Academy Elementary School Reading Practice Quizzes Quiz Word Number Lang. Title Author IL ATOS BL Points Count F/NF 9318 EN Ice Is...Whee! Greene, Carol LG 0.3 0.5 59 F 9340 EN Snow Joe Greene, Carol LG 0.3 0.5 59 F 36573 EN Big Egg Coxe, Molly LG 0.4 0.5 99 F 9306 EN Bugs! McKissack, Patricia C. LG 0.4 0.5 69 F 86010 EN Cat Traps Coxe, Molly LG 0.4 0.5 95 F 9329 EN Oh No, Otis! Frankel, Julie LG 0.4 0.5 97 F 9333 EN Pet for Pat, A Snow, Pegeen LG 0.4 0.5 71 F 9334 EN Please, Wind? Greene, Carol LG 0.4 0.5 55 F 9336 EN Rain! Rain! Greene, Carol LG 0.4 0.5 63 F 9338 EN Shine, Sun! Greene, Carol LG 0.4 0.5 66 F 9353 EN Birthday Car, The Hillert, Margaret LG 0.5 0.5 171 F 9305 EN Bonk! Goes the Ball Stevens, Philippa LG 0.5 0.5 100 F 7255 EN Can You Play? Ziefert, Harriet LG 0.5 0.5 144 F 9314 EN Hi, Clouds Greene, Carol LG 0.5 0.5 58 F 9382 EN Little Runaway, The Hillert, Margaret LG 0.5 0.5 196 F 7282 EN Lucky Bear Phillips, Joan LG 0.5 0.5 150 F 31542 EN Mine's the Best Bonsall, Crosby LG 0.5 0.5 106 F 901618 EN Night Watch (SF Edition) Fear, Sharon LG 0.5 0.5 51 F 9349 EN Whisper Is Quiet, A Lunn, Carolyn LG 0.5 0.5 63 NF 74854 EN Cooking with the Cat Worth, Bonnie LG 0.6 0.5 135 F 42150 EN Don't Cut My Hair! Wilhelm, Hans LG 0.6 0.5 74 F 9018 EN Foot Book, The Seuss, Dr. -
Parks & Recreation
Summer 2021 Activity Guide MAPLE GROVE Parks & Recreation maplegrovemn.gov • 763-494-6500 • 12951 Weaver Lake Rd • Maple Grove MN 55369 MAPLE GROVE FARMERS MARKET good food for everyone SNAP & EBT Accepted! THURSDAY MAY 13 through THURSDAYSPM PM 33-7PM 7 OCT 21 3-6PM In October 12951 12951 Weaver Lake Rd. Maple Grove, MN 55369 www.maplegrovefarmersmarket.com Connect With Us... PARKS AND RECREATION BOARD Parks and Recreation Board office ............................ 763-494-6500 Chair, Bill Lewis [email protected] John Ferm ................................... [email protected] Ken Helvey ............................. [email protected] Deb Syhre ................................. [email protected] Kelly Cunningham [email protected] Debbie Coss [email protected] Andy Mielke .......................... [email protected] Parks and Recreation Board Members Council Rep, Phil Leith ....................... [email protected] L to R: A. Mielke, D. Syhre, B. Lewis, K. Helvey, J. Ferm, D. Coss, K. Cunningham Park Board Meetings Maple Grove Parks and Recreation Board Office • Regular meetings of the Park Board are held the 3rd 12951 Weaver Lake Road Thursday of every month at the Government Center Maple Grove, MN Council Chambers at 7:00 p.m. and can be viewed online. 763-494-6500 maplegrovemn.gov/about/boards-and-commissions Monday through Friday 8am-4:30pm Parks and Recreation Board Staff Recreation Registration Hours Director ........................................................................Chuck -
Spring & Summer 2021
Spring & Summer 2021 newton community education notes Spring & Summer 2021 From the executive Director: It’s no secret; 2020 was a tough year. Many of us are fatigued by our collective and individual struggles; anxious about health, work, and the future. The time-honored tradition of turning the page from one year to reveal the opportunities of the next was punctuated by the recent presidential inauguration. We viewers were riveted as a diminutive young poet seized our attention, gracefully—through her words and gestures — echoing the (presidential) themes of unity and hope. Amanda Gorman’s Kids Classes words were powerful indeed, but equally impressive to me was learning that community- based education helped hone her craft. While struggling to overcome a serious speech Spring Classes .........................................2 impediment, Gorman participated in writing workshops at a Los Angeles community-based education center. So there it is… Community Education opens doors, including ones leading Spring Sports ..........................................4 to the White House! We’re committed to opening doors for all members of our community. To that end, you’ll notice that we have added an option to donate to our scholarship fund Spring Classes for High Schoolers ..........11 when you register for a class. If you can, please contribute and help make our programs accessible to all. Summer Programs ................................12 In addition, we are grasping all aspects of this historic moment by hosting the first annual NCE Catalog Cover Art Contest. Neighbors and friends are invited to submit art under the Summer Programs for High Schoolers ...28 theme: “What does struggle, hope, and finding joy look like to you?” Selected works from the contest will be used as NCE catalog covers for 2021-2022. -
Taxonomy of Mobile Web Applications from a Taxonomy and Business Analysis for Mobile Web Applications
Chapter 3: Taxonomy of Mobile Web Applications from A Taxonomy and Business Analysis for Mobile Web Applications By Kevin Hao Liu Ph.D. Computer Science Victoria University Submitted to the System Design and Management Program in Partial Fulfillment of the Requirements for the Degree of Master of Science in Management and Engineering At the Massachusetts Institute of Technology February 2009 © 2009 Kevin H Liu. All rights reserved The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. ABSTRACT Mobile web applications refer to web applications on mobile devices, aimed at personalizing, integrating, and discovering mobile contents in user contexts. This thesis presents a comprehensive study of mobile web applications by proposing a new taxonomy for mobile web applications, and conducting a business analysis in the field of mobile web applications. The thesis reviews the current surrounding environment for mobile web applications, namely, web 2.0 and 3.0, wireless communication technology, and Smartphone platform. The recent entry and success of Apple’s iPhone greatly enhanced the public awareness of the Smartphone technology. Google’s release of open-source Android platform and T-Mobile’s deployment of Android-powered “Dream” Smartphone not only intensify the competition among suppliers, but also provide an open-source foundation for mobile web applications. This thesis introduces a new mobile web application taxonomy to systematically study the values and the groupings of the mobile web applications. By introducing features and categories, the taxonomy provides a framework so the related companies and businesses can be comparatively analyzed and summarized. -
Mining Version Histories for Detecting Code Smells
W&M ScholarWorks Arts & Sciences Articles Arts and Sciences 2015 Mining Version Histories for Detecting Code Smells Fabio Palomba Andrea De Lucia Gabriele Bavota Denys Poshyvanyk College of William and Mary Follow this and additional works at: https://scholarworks.wm.edu/aspubs Recommended Citation Palomba, F., Bavota, G., Di Penta, M., Oliveto, R., Poshyvanyk, D., & De Lucia, A. (2014). Mining version histories for detecting code smells. IEEE Transactions on Software Engineering, 41(5), 462-489. This Article is brought to you for free and open access by the Arts and Sciences at W&M ScholarWorks. It has been accepted for inclusion in Arts & Sciences Articles by an authorized administrator of W&M ScholarWorks. For more information, please contact [email protected]. 462 IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 41, NO. 5, MAY 2015 Mining Version Histories for Detecting Code Smells Fabio Palomba, Student Member, IEEE, Gabriele Bavota, Member, IEEE Computer Society, Massimiliano Di Penta, Rocco Oliveto, Member, IEEE Computer Society, Denys Poshyvanyk, Member, IEEE Computer Society, and Andrea De Lucia, Senior Member, IEEE Abstract—Code smells are symptoms of poor design and implementation choices that may hinder code comprehension, and possibly increase change- and fault-proneness. While most of the detection techniques just rely on structural information, many code smells are intrinsically characterized by how code elements change over time. In this paper, we propose Historical Information for Smell deTection (HIST), an approach exploiting change history information to detect instances of five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy. We evaluate HIST in two empirical studies. -
Arxiv:1705.06950V1 [Cs.CV] 19 May 2017
The Kinetics Human Action Video Dataset Will Kay Joao˜ Carreira Karen Simonyan Brian Zhang [email protected] [email protected] [email protected] [email protected] Chloe Hillier Sudheendra Vijayanarasimhan Fabio Viola [email protected] [email protected] [email protected] Tim Green Trevor Back Paul Natsev Mustafa Suleyman [email protected] [email protected] [email protected] [email protected] Andrew Zisserman [email protected] Abstract purposes, including multi-modal analysis. Our inspiration in providing a dataset for classification is ImageNet [18], We describe the DeepMind Kinetics human action video where the significant benefits of first training deep networks dataset. The dataset contains 400 human action classes, on this dataset for classification, and then using the trained with at least 400 video clips for each action. Each clip lasts network for other purposes (detection, image segmenta- around 10s and is taken from a different YouTube video. The tion, non-visual modalities (e.g. sound, depth), etc) are well actions are human focussed and cover a broad range of known. classes including human-object interactions such as play- The Kinetics dataset can be seen as the successor to the ing instruments, as well as human-human interactions such two human action video datasets that have emerged as the as shaking hands. We describe the statistics of the dataset, standard benchmarks for this area: HMDB-51 [15] and how it was collected, and give some baseline performance UCF-101 [20]. These datasets have served the commu- figures for neural network architectures trained and tested nity very well, but their usefulness is now expiring. -
RL-DARTS: Differentiable Architecture Search for Reinforcement Learning
RL-DARTS: Differentiable Architecture Search for Reinforcement Learning Yingjie Miao∗, Xingyou Song∗, Daiyi Peng Summer Yue, Eugene Brevdo, Aleksandra Faust Google Research, Brain Team Abstract We introduce RL-DARTS, one of the first applications of Differentiable Architec- ture Search (DARTS) in reinforcement learning (RL) to search for convolutional cells, applied to the Procgen benchmark. We outline the initial difficulties of applying neural architecture search techniques in RL, and demonstrate that by simply replacing the image encoder with a DARTS supernet, our search method is sample-efficient, requires minimal extra compute resources, and is also compati- ble with off-policy and on-policy RL algorithms, needing only minor changes in preexisting code. Surprisingly, we find that the supernet can be used as an actor for inference to generate replay data in standard RL training loops, and thus train end-to-end. Throughout this training process, we show that the supernet gradually learns better cells, leading to alternative architectures which can be highly competi- tive against manually designed policies, but also verify previous design choices for RL policies. 1 Introduction and Motivation Over the last decade, recent advancements in deep reinforcement learning have heavily focused on algorithmic improvements [25, 71], data augmentation [51, 35], infrastructure upgrades [15, 27], and even hyperparameter tuning [72, 16]. Automating such methods for RL have given rise to the field of Automated Reinforcement Learning (AutoRL). Surprisingly however, one relatively underdeveloped and unexplored area is in automating large-scale policy architecture design. Recently, works in larger-scale RL suggest that designing a policy’s architecture can be just as important as the algorithm or quality of data, if not more, for various metrics such as generalization, transferrability and efficiency. -
Implicit Bias
IMPLICIT BIAS STATE OF THE SCIENCE: IMPLICIT BIAS REVIEW 2014 As a university-wide, interdisciplinary research institute, the Kirwan Institute for the Study of Race and Ethnicity works to deepen understanding of the causes of—and solutions to—racial and ethnic disparities worldwide and to bring about a society that is fair and just for all people. Our research is designed to be actively used to solve problems in society. Research and staff expertise are shared through an extensive network of colleagues and partners, ranging from other researchers, grassroots social justice advocates, policymakers, and community leaders nationally and globally, who can quickly put ideas into action. BIA ULTIMATELY, WE BELIEVE OUR DECISIONS ARE CONSISTENT WITH OUR CONSCIOUS BELIEFS, WHEN IN FACT, OUR UNCONSCIOUS IS BIARUNNING THE SHOW AS Howard Ross, 2008, p. 11 State of the Science: Implicit Bias Review 2014 Cheryl Staats Research Associate II With funding from the W. K. Kellogg Foundation /KirwanInstitute | www.KirwanInstitute.osu.edu Dear Reader, Early last year, the Kirwan Institute for the Study of Race & Ethnicity published its first issue of theState of the Science: Implicit Bias Review to help raise awareness of 30 years of findings from neurology and social and cognitive psychology showing that hidden biases operating largely under the scope of human consciousness influence the way that we see and treat others, even when we are determined to be fair and objective. This important body of research has enormous potential for helping to reduce unwanted disparities in every realm of human life. THE RESPONSE TO KIRWAN’S State of the Science report was overwhelmingly enthusiastic.