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Communications of the ACM

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MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

MODELS is the premier conference series for model-based software and systems engineering. Since 1998, MODELS has been covering all aspects of modeling, from languages and methods to tools and applications. Since the inception of the conference, the development of tools to support modeling activities has been an integral part of the research activities with many of these tools evolving into modeling platforms that support the development of new tools. The demonstration of tools at recent MODELS conferences has shown that both researchers and practitioners dedicate more time and effort to developing high-quality tools to be used by the community and industry. It is also accepted that the availability of tools is a primary factor for the adoption of model-driven engineering approaches. With the Tools & Demonstrations track of MODELS 2020, we propose high-quality contributions, ranging across commercial, academic, and corporate research as well as industrial systems.


Route Optimization Model for Order Picking of Distribution Center

This paper studies an optimal model of order picking route to improve picking efficiency and benefit. The model consists of three modules. First of them is the order picking grouping module based on container volume and how many pickings a container can accommodate can be calculated according to the material volume of picking task Second of them is the coordinate model with vertical arrangement warehouse shelves In this part, coordinate model and assorted parameter list can be built according to the location of goods in the warehouse. Third of them is calculating picking route module. This paper also studies how to get the shortest picking route using nearest neighbor strategy to calculate optimal picking order.


Exploring Design Principles for Sharing of Personal Informatics Data on Ephemeral Social Media

People often do not receive the engagement or responses they desire when they share on broad social media platforms. Sharers are hesitant to share trivial accomplishments, and the emphasis on data often results posts that audiences find repetitive or unengaging. Ephemeral social media's focus on self-authored content and sharing trivial accomplishments has the potential to ameliorate these challenges. We explore design principles for incorporating personal informatics data like steps, heart rate, or duration in data-driven stickers as a first step towards integrating these data into ephemeral social media. We examine the effect of a sticker's presentation style, domain, domain-relevance, and background through three surveys with 506 total participants. We uncover the importance of domain-relevant backgrounds and stickers, identify the situational value of stickers styled as analogies, embellished, and badges, and demonstrate that data-driven stickers can make ephemeral content more informative and entertaining, discussing implications for platforms and tools.


ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia

Algorithmic systems---from rule-based bots to machine learning classifiers---have a long history of supporting the essential work of content moderation and other curation work in peer production projects. From counter-vandalism to task routing, basic machine prediction has allowed open knowledge projects like Wikipedia to scale to the largest encyclopedia in the world, while maintaining quality and consistency. However, conversations about how quality control should work and what role algorithms should play have generally been led by the expert engineers who have the skills and resources to develop and modify these complex algorithmic systems. In this paper, we describe ORES: an algorithmic scoring service that supports real-time scoring of wiki edits using multiple independent classifiers trained on different datasets. ORES decouples several activities that have typically all been performed by engineers: choosing or curating training data, building models to serve predictions, auditing predictions, and developing interfaces or automated agents that act on those predictions. This meta-algorithmic system was designed to open up socio-technical conversations about algorithms in Wikipedia to a broader set of participants. In this paper, we discuss the theoretical mechanisms of social change ORES enables and detail case studies in participatory machine learning around ORES from the 5 years since its deployment.


Sochiatrist: Signals of Affect in Messaging Data

Messaging is a common mode of communication, with conversations written informally between individuals. Interpreting emotional affect from messaging data can lead to a powerful form of reflection or act as a support for clinical therapy. Existing analysis techniques for social media commonly use LIWC and VADER for automated sentiment estimation. We correlate LIWC, VADER, and ratings from human reviewers with affect scores from 25 participants. We explore differences in how and when each technique is successful. Results show that human review does better than VADER, the best automated technique, when humans are judging positive affect ($r_s=0.45$ correlation when confident, $r_s=0.30$ overall). Surprisingly, human reviewers only do slightly better than VADER when judging negative affect ($r_s=0.38$ correlation when confident, $r_s=0.29$ overall). Compared to prior literature, VADER correlates more closely with PANAS scores for private messaging than public social media. Our results indicate that while any technique that serves as a proxy for PANAS scores has moderate correlation at best, there are some areas to improve the automated techniques by better considering context and timing in conversations.


Data and Power: Archival Appraisal Theory as a Framework for Data Preservation

Digital data pervades everyday life, from personal photos shared on social media to voice commands for Amazon Alexa. A widespread industry culture of 'move fast and break things,' however, has compelled data management practices that prioritize profit over preservation. This paper draws from archival theories of appraisal to foreground control, power, subjectivity, and emotion in computing practices that treat data storage as a neutral or objective cost-center. We draw on postmodern archival appraisal theory that recognizes the archive as a powerful and subjective curator of identity and memory. The theoretical basis of archival decision practices, in turn, establishes the value of the archival record and thus the need to save it. With three primary issues of appraisal theory as a framework, we report on an interview study with adults (N=17), ages 51-72, who are in a transitional life-stage that focuses them on their experiences and memories that are worth keeping or discarding. We sketch implications for data management paths that forefront legacy, life transitions, precarity, and control.


Can you Turn it Off?: The Spatial and Social Context of Mobile Disturbance

Contemporary mobile devices continuously interrupt people with notifications in various and changing physical environments. As different places can have different social setting, understanding how disturbing an interruption might be to people around the user is not a straightforward task. To understand how users perceive disturbance in their social environment, we analyze the results of a 3-week user study with 50 participants using the experience sampling method and log analysis. We show that perceptions of disturbance are strongly related to the social norms surrounding the place, such as whether the place is considered private or public, even when controlling for the number of people around the user. Furthermore, users' perceptions of disturbance are also related to the activity carried out on the phone, and the subjective perceptions of isolation from other people in the space. We conclude the paper by discussing how our findings can be used to design new mobile devices that are aware of the social norms and their users' environmental context.


AI at the Disco: Low Sample Frequency Human Activity Recognition for Night Club Experiences

Human activity recognition (HAR) has grown in popularity as sensors have become more ubiquitous. Beyond standard health applications, there exists a need for embedded low cost, low power, accurate activity sensing for entertainment experiences. We present a system and method of using a deep neural net for HAR using low-cost accelerometer-only sensor running at 0.8Hz to preserve battery power. Despite these limitations, we demonstrate an accuracy at 94.79% over 6 activity classes with an order of magnitude less data. This sensing system conserves power further by using a connectionless reading---embedding accelerometer data in the Bluetooth Low Energy broadcast packet---which can deliver over a year of human-activity recognition data on a single coin cell battery. Finally, we discuss the integration of our HAR system in a smart-fashion wearable for a live two night deployment in an instrumented night club.


Fast Distributed kNN Graph Construction Using Auto-tuned Locality-sensitive Hashing

The k-nearest-neighbors (kNN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. Approximate solutions have been described in the literature using diverse techniques, among which Locality-sensitive Hashing (LSH) is a promising alternative that still has unsolved problems. We present Variable Resolution Locality-sensitive Hashing, an algorithm that addresses these problems to obtain an approximate kNN graph at a significantly reduced computational cost. Its usability is greatly enhanced by its capacity to automatically find adequate hyperparameter values, a common hindrance to LSH-based methods. Moreover, we provide an implementation in the distributed computing framework Apache Spark that takes advantage of the structure of the algorithm to efficiently distribute the computational load across multiple machines, enabling practitioners to apply this solution to very large datasets. Experimental results show that our method offers significant improvements over the state-of-the-art in the field and shows very good scalability as more machines are added to the computation.


Private and communication-efficient edge learning: a sparse differential gaussian-masking distributed SGD approach

With the rise of machine learning (ML) and the proliferation of smart mobile devices, recent years have witnessed a surge of interest in performing ML in wireless edge networks. In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing. Toward this end, we propose a new distributed stochastic gradient method with sparse differential Gaussian-masked stochastic gradients (SDM-DSGD) for non-convex distributed edge learning. Our main contributions are three-fold: i) We theoretically establish the privacy and communication efficiency performance guarantee for our SDM-DSGD method, which outperforms all existing works; ii) We propose a generalized differential-coded DSGD update, which enables a much lower transmit probability for gradient sparsification, and provides an [EQUATION] convergence rate; and iii) We reveal theoretical insights and offer practical design guidelines for the interactions between privacy preservation and communication efficiency - two conflicting performance goals. We conduct extensive experiments with a variety of learning models on MNIST and CIFAR-10 datasets to verify our theoretical findings.