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Designing Commercial Therapeutic Robots for Privacy Preserving Systems and Ethical Research Practices within the Home

Jun 29, 2016
Elaine Sedenberg, John Chuang, Deirdre Mulligan

The migration of robots from the laboratory into sensitive home settings as commercially available therapeutic agents represents a significant transition for information privacy and ethical imperatives. We present new privacy paradigms and apply the Fair Information Practices (FIPs) to investigate concerns unique to the placement of therapeutic robots in private home contexts. We then explore the importance and utility of research ethics as operationalized by existing human subjects research frameworks to guide the consideration of therapeutic robotic users -- a step vital to the continued research and development of these platforms. Together, privacy and research ethics frameworks provide two complementary approaches to protect users and ensure responsible yet robust information sharing for technology development. We make recommendations for the implementation of these principles -- paying particular attention to specific principles that apply to vulnerable individuals (i.e., children, disabled, or elderly persons)--to promote the adoption and continued improvement of long-term, responsible, and research-enabled robotics in private settings.

* International Journal of Social Robotics, (2016), 1-13 

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RGB-D-based Action Recognition Datasets: A Survey

Jan 21, 2016
Jing Zhang, Wanqing Li, Philip O. Ogunbona, Pichao Wang, Chang Tang

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-\'{a}-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols.

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Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions

May 01, 2022
Mihir Parmar, Swaroop Mishra, Mor Geva, Chitta Baral

In recent years, progress in NLU has been driven by benchmarks. These benchmarks are typically collected by crowdsourcing, where annotators write examples based on annotation instructions crafted by dataset creators. In this work, we hypothesize that annotators pick up on patterns in the crowdsourcing instructions, which bias them to write similar examples that are then over-represented in the collected data. We study this form of bias, termed instruction bias, in 14 recent NLU benchmarks, showing that instruction examples often exhibit concrete patterns, which are propagated by crowdworkers to the collected data. This extends previous work (Geva et al., 2019) and raises a new concern of whether we are modeling the dataset creator's instructions, rather than the task. Through a series of experiments, we show that, indeed, instruction bias can lead to overestimation of model performance, and that models struggle to generalize beyond biases originating in the crowdsourcing instructions. We further analyze the influence of instruction bias in terms of pattern frequency and model size, and derive concrete recommendations for creating future NLU benchmarks.

* 11 pages 

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Service resource allocation problem in the IoT driven personalized healthcare information platform

Apr 05, 2022
Ji Fang, Vincent CS Lee, Haiyan Wang

With real-time monitoring of the personalized healthcare condition, the IoT wearables collect the health data and transfer it to the healthcare information platform. The platform processes the data into healthcare recommendations and then delivers them to the users. The IoT structures in the personalized healthcare information service allows the users to engage in the loop in servitization more convenient in the COVID-19 pandemic. However, the uncertainty of the engagement behavior among the individual may result in inefficient of the service resource allocation. This paper seeks an efficient way to allocate the service resource by controlling the service capacity and pushing the service to the active users automatically. In this study, we propose a deep reinforcement learning method to solve the service resource allocation problem based on the proximal policy optimization (PPO) algorithm. Experimental results using the real world (open source) sport dataset reveal that our proposed proximal policy optimization adapts well to the users' changing behavior and with improved performance over fixed service resource policies.

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Automatic Detection of Expressed Emotion from Five-Minute Speech Samples: Challenges and Opportunities

Mar 30, 2022
Bahman Mirheidari, André Bittar, Nicholas Cummins, Johnny Downs, Helen L. Fisher, Heidi Christensen

We present a novel feasibility study on the automatic recognition of Expressed Emotion (EE), a family environment concept based on caregivers speaking freely about their relative/family member. We describe an automated approach for determining the \textit{degree of warmth}, a key component of EE, from acoustic and text features acquired from a sample of 37 recorded interviews. These recordings, collected over 20 years ago, are derived from a nationally representative birth cohort of 2,232 British twin children and were manually coded for EE. We outline the core steps of extracting usable information from recordings with highly variable audio quality and assess the efficacy of four machine learning approaches trained with different combinations of acoustic and text features. Despite the challenges of working with this legacy data, we demonstrated that the degree of warmth can be predicted with an $F_{1}$-score of \textbf{61.5\%}. In this paper, we summarise our learning and provide recommendations for future work using real-world speech samples.

* Submitted to Interspeech 2022 

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Personal Knowledge Graphs: Use Cases in e-learning Platforms

Mar 16, 2022
Eleni Ilkou

Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia. Inspired by the widely recent usage of PKGs in the medical domain to represent patient data, this PhD proposal aims to adopt a similar technique in the educational domain in e-learning platforms by deploying PKGs to represent users and learners. We propose a novel PKG development that relies on ontology and interlinks to Linked Open Data. Hence, adding the dimension of personalisation and explainability in users' featured data while respecting privacy. This research design is developed in two use cases: a collaborative search learning platform and an e-learning platform. Our preliminary results show that e-learning platforms can get benefited from our approach by providing personalised recommendations and more user and group-specific data.

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Dynamic Inference

Nov 30, 2021
Aolin Xu

Traditional statistical estimation, or statistical inference in general, is static, in the sense that the estimate of the quantity of interest does not change the future evolution of the quantity. In some sequential estimation problems however, we encounter the situation where the future values of the quantity to be estimated depend on the estimate of its current value. Examples include stock price prediction by big investors, interactive product recommendation, and behavior prediction in multi-agent systems. We may call such problems as dynamic inference. In this work, a formulation of this problem under a Bayesian probabilistic framework is given, and the optimal estimation strategy is derived as the solution to minimize the overall inference loss. How the optimal estimation strategy works is illustrated through two examples, stock trend prediction and vehicle behavior prediction. When the underlying models for dynamic inference are unknown, we can consider the problem of learning for dynamic inference. This learning problem can potentially unify several familiar machine learning problems, including supervised learning, imitation learning, and reinforcement learning.

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Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management

Nov 11, 2021
Alexandre Parmentier, Robin Cohen, Xueguang Ma, Gaurav Sahu, Queenie Chen

In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modeling. In particular, we propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. We focus on demonstrating how clustering of similar users enables a critical new functionality: supporting more personalized, and thus more accurate predictions for users. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset. We then discuss how improving the detection of trusted relationships in social media can assist in supporting online users in their battle against the spread of misinformation and rumours, within a social networking environment which has recently exploded in popularity. We conclude with a reflection on a particularly vulnerable user base, older adults, in order to illustrate the value of reasoning about groups of users, looking to some future directions for integrating known preferences with insights gained through data analysis.

* 28 pages 

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Multiplication-Avoiding Variant of Power Iteration with Applications

Oct 22, 2021
Hongyi Pan, Diaa Badawi, Runxuan Miao, Erdem Koyuncu, Ahmet Enis Cetin

Power iteration is a fundamental algorithm in data analysis. It extracts the eigenvector corresponding to the largest eigenvalue of a given matrix. Applications include ranking algorithms, recommendation systems, principal component analysis (PCA), among many others. In this paper, We introduce multiplication-avoiding power iteration (MAPI), which replaces the standard $\ell_2$-inner products that appear at the regular power iteration (RPI) with multiplication-free vector products which are Mercer-type kernel operations related with the $\ell_1$ norm. Precisely, for an $n\times n$ matrix, MAPI requires $n$ multiplications, while RPI needs $n^2$ multiplications per iteration. Therefore, MAPI provides a significant reduction of the number of multiplication operations, which are known to be costly in terms of energy consumption. We provide applications of MAPI to PCA-based image reconstruction as well as to graph-based ranking algorithms. When compared to RPI, MAPI not only typically converges much faster, but also provides superior performance.

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Anti-degenerated UWB-LiDAR Localization for Automatic Road Roller in Tunnel

Sep 22, 2021
Bingqi Shen, Yuyin Chen, Huiyong Yang, Jianbo Zhan, Yichao Sun, Rong Xiong, Shuwei Dai, Yue Wang

The automatic road roller, as a popular type of construction robot, has attracted much interest from both the industry and the research community in recent years. However, when it comes to tunnels where the degeneration issues are prone to happen, it is still a challenging problem to provide an accurate positioning result for the robot. In this paper, we aim to deal with this problem by fusing LiDAR and UWB measurements based on optimization. In the proposed localization method, the directions of non-degeneration will be constrained and the covariance of UWB reconstruction will be introduced to improve the accuracy of localization. Apart from these, a method that can extract the feature of the inner wall of tunnels to assist positioning is also presented in this paper. To evaluate the effectiveness of the proposed method, three experiments with real road roller were carried out and the results show that our method can achieve better performance than the existing methods and can be applied to automatic road roller working inside tunnels. Finally, we discuss the feasibility of deploying the system in real applications and make several recommendations.

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