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Communications of the ACM

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NanoCom '20: Proceedings of the 7th ACM International Conference on Nanoscale Computing and Communication

The main goals of the 7th ACM International Conference on Nanoscale Computing and Communication (ACM NanoCom 2020), are to increase the visibility of this growing research area to the wider computing and communication research communities as well as bring together researchers from diverse disciplines that can foster and develop new paradigms for nanoscale devices. Due to the highly inter-disciplinary nature of this field of research, the conference aims to attract researchers and academics from various areas of study such as electrical and electronic engineering, computer science, biology, chemistry, physics, mathematics, bioengineering, biotechnology, materials science, nanotechnology, who have an interest in computing and communications at the nanoscale.


The use of AI in public services: results from a preliminary mapping across the EU

Artificial Intelligence is a new set of technologies which has grasped the attention of many in society due to its potential. These technologies could also provide great benefits to public administrations when adopted. This paper acts as a first landscaping analysis to indicate, classify and understand current AI-implementations in public services. By conducting a desk research based on available documents describing AI projects, 85 AI applications in the public sector in selected European countries have been identified and reviewed. The preliminary analysis suggests that most AI initiatives are started with efficiency goals in mind, and they occur mainly in the general public service policy area. Findings of this preliminary landscape analysis set the basis for further more in depth research and recommendations for policy.


A Generalized Robinson-Foulds Distance for Clonal Trees, Mutation Trees, and Phylogenetic Trees and Networks

Cancer evolution is often modeled by clonal trees (whose nodes are labeled by multiple somatic mutations) or mutation trees (where nodes are labeled by single somatic mutations). Clonal trees are generated from sequence data with different computational methods that may produce different clone phylogenies, rendering their analysis and comparison necessary to infer mutation order and clone origin during tumor progression. In this paper, we present a distance metric for multi-labeled trees that generalizes the Robinson-Foulds distance for phylogenetic trees, allows for a similarity assessment at much higher resolution, and can be applied to trees and networks with different sets of node labels. The generalized Robinson-Foulds distance can be computed in time quadratic in the size of the input multisets of multisets of node labels, and is a metric for clonal trees, mutation trees, phylogenetic trees, and several classes of phylogenetic networks.


GROOT: a real-time streaming system of high-fidelity volumetric videos

We present GROOT, a mobile volumetric video streaming system that delivers three-dimensional data to mobile devices for a fully immersive virtual and augmented reality experience. The system design for streaming volumetric videos should be fundamentally different from conventional 2D video streaming systems. First, the amount of data required to deliver the 3D volume is considerably larger than conventional videos with frames of 2D images, even compared to high-resolution 2D or 360° videos. Second, the 3D data representation, which encodes the surface of objects within the volume, is a sparse and unorganized data structure with varying scales, whereas a conventional video is composed of a sequence of images with the fixed-size 2D grid structure. GROOT is a streaming framework with a novel data structure that enables not only real-time transmission and decoding on mobile devices but also continuous on-demand user view adaptation. Specifically, we modify the conventional octree to introduce the independence of leaf nodes with minimal memory overhead, which enables parallel decoding of highly irregular 3D data. We also developed a suite of techniques to compress color information and filter out 3D points outside of a user's view, which efficiently minimizes the data size and decoding cost. Our extensive evaluation shows that GROOT achieves more stable but faster frame rates compared to any previous method to stream and visualize volumetric videos on mobile devices.


Predicting Monthly Pageview of Wikipedia Pages by Neighbor Pages

Predicting traffic has been important for websites' daily services. Developing efficient models for Wikipedia's page traffic would deepen our knowledge about people's behavior on Wikipedia and potentially for other crowdsourcing pages. The current project attempted to experiment with incorporating time series data from a linked page trying to improve the prediction accuracy of future traffic of a page. The current study experimented with three timeseries models. The baseline model uses the monthly traffic of 2019 of a page to predict the monthly traffic of January of 2020. The random neighbor model randomly selects a page which has a hyperlink to the focal page and uses the 2019 data of the focal page and the neighboring page to predict the monthly traffic of January of 2020. The similar neighbor model also uses data from the focal and a neighboring page, but the neighbor is selected based on its content similarity to the focal page. The results show that prediction with a similar neighbor model has better prediction performance than with the Random neighbor model on popular pages. The baseline model has the best performance with the smallest MSE, MAE, and MAPE, while the random neighbor model and similar neighbor model have much larger MSE than the Baseline model.


Use of Intelligent Voice Assistants by Older Adults with Low Technology Use

Voice assistants embodied in smart speakers (e.g., Amazon Echo, Google Home) enable voice-based interaction that does not necessarily rely on expertise with mobile or desktop computing. Hence, these voice assistants offer new opportunities to different populations, including individuals who are not interested or able to use traditional computing devices such as computers and smartphones. To understand how older adults who use technology infrequently perceive and use these voice assistants, we conducted a 3-week field deployment of the Amazon Echo Dot in the homes of seven older adults. While some types of usage dropped over the 3-week period (e.g., playing music), we observed consistent usage for finding online information. Given that much of this information was health-related, this finding emphasizes the need to revisit concerns about credibility of information with this new interaction medium. Although features to support memory (e.g., setting timers, reminders) were initially perceived as useful, the actual usage was unexpectedly low due to reliability concerns. We discuss how these findings apply to other user groups along with design implications and recommendations for future work on voice-user interfaces.


Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders

Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational creativity or the recombination, adaptation, and reuse of ideas and concepts between and across domains. In this paper, we present a PCGML approach for level generation that is able to recombine, adapt, and reuse structural patterns from several domains to approximate unseen domains. We extend prior work involving example-driven Binary Space Partitioning for recombining and reusing patterns in multiple domains, and incorporate Variational Autoencoders (VAEs) for generating unseen structures. We evaluate our approach by blending across 7 domains and subsets of those domains. We show that our approach is able to blend domains together while retaining structural components. Additionally, by using different groups of training domains our approach is able to generate both 1) levels that reproduce and capture features of a target domain, and 2) levels that have vastly different properties from the input domain.


Do Game Bots Dream of Electric Rewards?: The universality of intrinsic motivation

The purpose of this paper is to draw together theories, ideas, and observations related to rewards, motivation, and play to develop and question our understanding and practice of designing reward-based systems and technology. Our exploration includes reinforcement, rewards, motivational theory, flow, play, games, gamification, and machine learning. We examine the design and psychology of reward-based systems in society and technology, using gamification and machine learning as case studies. We propose that the problems that exist with reward-based systems in our society are also present and pertinent when designing technology. We suggest that motivation, exploration, and play are not just fundamental to human learning and behaviour, but that they could transcend nature into machine learning. Finally, we question the value and potential harm of the reward-based systems that permeate every aspect of our lives and assert the importance of ethics in the design of all systems and technology.


Explaining factors affecting telework adoption in South African organisations pre-COVID-19

The COVID-19 pandemic of 2020 saw governments across the world mandating telework for entire populations thereby bringing the topic of telework into sharp focus. Telework is a well-researched topic which dates as far back as five decades ago. While telework provides many indisputable benefits to organisations, society and individuals, it has not achieved the anticipated widespread adoption. While telework studies have examined multiple aspects, few studies have examined organisational factors which affect telework adoption. This study is an empirical investigation of telework adoption, using a set of factors identified in the literature in organisations in a South African context. These factors in prior studies were found to enable or prevent an organisation from adopting telework. The question thus asked in this study was “Which factors enable or prevent the adoption of telework within South African organisations?” A survey with 104 valid responses was analysed using Statistica. The theoretical contribution of the study is a validated model of factors influencing the adoption of telework.


Adaptive Weighted Finite Mixture Model: Identifying the Feature-Influence of Real Estate

It is significant for real estate investors to understand how the construction environments and building characteristics impact the housing unit price. However, it is challenging for identifying the complex feature-influence from construction environments and building characteristics. It is also hard to alleviate the heterogeneity of real estate. In this article, we propose a framework named Adaptive Weighted Finite Mixture Model to identify the feature-influence and simultaneously alleviate the ill effect of heterogeneity. Applying this framework, we can predict the housing unit price based on the corresponding features. Besides, we discover that the feature-influence exists in the dissimilarity among similar cities. Specifically, we adaptively learn the weights of features to identify the feature-influence, and we model the estimation of the housing unit price with the feature-influence into a finite mixture model. We utilize the Principle Component Analysis algorithm to obtain a low-dimensional representation of housing features. The low-dimensional representation reduces the computational cost of model learning, and it avoids a potential catastrophe of the singular matrix inversion during the process of learning model parameters, which are estimated by the Expectation Maximization algorithm. To avoid the blind search for the latent group number used in the proposed framework, we employ the pre-clustering result as a guide of the searching range of the latent group numbers. We conduct numerous experiments on three real datasets from Shenyang, Changchun, and Harbin, which are the three provincial capital cities that have similar geography, economics, and cultures. The experimental results demonstrate the effectiveness of the proposed framework.