Miracle-dz/UniFAR-FTUs huggingface dataset:
iVR

Smartphone localization is essential to a wide range of applications in shopping malls, museums, office buildings, and other public places. Existing solutions relying on radio fingerprints and/or inertial sensors suffer from large location errors and considerable deployment efforts. We observe an opportunity in the recent trend of increasing numbers of security surveillance cameras installed in indoor spaces to overcome these limitations and revisit the problem of smartphone localization with a fresh perspective. However, fusing vision-based and radio-based systems is non-trivial due to the absence of absolute location, incorrespondence of identification and looseness of sensor fusion. This study proposes iVR, an integrated vision and radio localization system that achieves sub-meter accuracy with indoor semantic maps automatically generated from only two surveillance cameras, superior to precedent systems that require manual map construction or plentiful captured images. iVR employs a particle filter to fuse raw estimates from multiple systems, including vision, radio, and inertial sensor systems. By doing so, iVR outputs enhanced accuracy with zero start-up costs, while overcoming the respective drawbacks of each individual sub-system. We implement iVR on commodity smartphones and validate its performance in five different scenarios. The results show that iVR achieves a remarkable localization accuracy of 0.7m, outperforming the state-of-the-art systems by >70%.
A sequence-to-sequence model for cell-ID trajectory prediction

It is expensive to collect trajectory data on a mobile phone by continuously pinpointing its location, which limits the application of trajectory data mining (e.g., trajectory prediction). In this poster, we propose a method for trajectory prediction by collecting cell-id trajectory data without explicit locations. First, it exploits the spatial correlation between cell towers based on graph embedding technique. Second, it employs the sequence-to-sequence (seq2seq) framework to train the prediction model by designing a novel spatial loss function. Experiment results based on real datasets have demonstrated the effectiveness of the proposed method.
ShoesHacker

The past few years have witnessed the rise of smart shoes, the wearable devices that measure foot force or track foot motion. However, people are not aware of the possible privacy leakage from in-shoe force sensors. In this paper, we explore the possibility of locating an indoor victim based on the force signals leaked from smart shoes. We present ShoesHacker, an attack scheme that reconstructs the corridor map of the building that the victim walks in based on force data only. The corridor map enables the attacker to recognize the building, and thus locate the victim on a global map. To handle the lack of training data, we design the stair landing detection algorithm, based on which we extract training data when victims are walking in stairwells. We estimate the trajectory of each walk, and propose the path merging algorithm to merge the trajectories. Moreover, we design a metric to quantify the similarity between corridor maps, which makes building recognition possible. Our experimental results show that, the building recognition accuracy reaches 77.5% in a 40-building dataset, and the victim can be located with an average error lower than 6 m, which reveals the danger of privacy leakage through smart shoes. CCS Concepts: • Information systems~Mobile information processing systems; Location based services; • Human-centered computing~Mobile devices; Ubiquitous and mobile computing systems and tools; • Security and privacy~Domain-specific security and privacy architectures.
CityAtmosphere: VR image to glimpse wishes in the air

This paper presents a novel virtual reality demonstration program that provides people immersive urban experience, i.e. helps people understand characteristics of the city atmosphere from big data analysis on GPS location logs and search query logs. In contrast to the other demonstration systems that show the characteristics of areas of interests by using tag cloud, or VR systems using 3D computer graphics, our system synthesizes both the functionalities by showing 3D point clouds, where each particle shows search queries made by people staying at the area of interests. This synthesis invokes people feel the atmosphere of the city while traditional VR systems could not offer it. To make demonstration system effective, a new feature extraction process on search query logs is proposed by focusing on the users who visit the area of interests. In the demonstration at the conference, visitors could enjoy immersive urban experience with VR headset. Furthermore, the paper also shows the empirical evaluation on our new feature representation from search query logs.
Face Recognition Assistant for People with Visual Impairments

Although there are many face recognition systems to help individuals with visual impairments (VIPs) recognize other people, almost all require a database with the pictures and names of the people who should be tracked. These solutions would not be able to help VIPs recognize people they might not know well. In this work, we investigate the requirements and challenges that must be addressed in the design of a face recognition system for helping VIPs recognize people with whom they have weak-ties. We first conducted a formative study with eight visually impaired people. Using insights learned from the formative study, we developed a research prototype that runs on a mobile phone worn around the user's neck. The developed prototype is a wearable face recognition system that opportunistically captures and stores undistorted face images and contextual information about the user's interaction with each person to a database, without the user intervention, as she interacts with new people. We then used this prototype application as a technology probe---asking VIP participants to use the device in a realistic scenario in which they meet and re-encounter several new people. We analyze and report feedback collected from VIPs about the design and use of such a service.
Analyzing rich-club behavior in open source projects

The network of collaborations in an open source project can reveal relevant emergent properties that influence its prospects of success. In this work, we analyze open source projects to determine whether they exhibit a rich-club behavior, i.e., a phenomenon where contributors with a high number of collaborations (i.e., strongly connected within the collaboration network) are likely to cooperate with other well-connected individuals. The presence or absence of a rich-club has an impact on the sustainability and robustness of the project. For this analysis, we build and study a dataset with the 100 most popular projects in GitHub, exploiting connectivity patterns in the graph structure of collaborations that arise from commits, issues and pull requests. Results show that rich-club behavior is present in all the projects, but only few of them have an evident club structure. We compute coefficients both for single source graphs and the overall interaction graph, showing that rich-club behavior varies across different layers of software development. We provide possible explanations of our results, as well as implications for further analysis.
KnuckleTouch: Enabling Knuckle Gestures on Capacitive Touchscreens using Deep Learning

While mobile devices have become essential for social communication and have paved the way for work on the go, their interactive capabilities are still limited to simple touch input. A promising enhancement for touch interaction is knuckle input but recognizing knuckle gestures robustly and accurately remains challenging. We present a method to differentiate between 17 finger and knuckle gestures based on a long short-term memory (LSTM) machine learning model. Furthermore, we introduce an open source approach that is ready-to-deploy on commodity touch-based devices. The model was trained on a new dataset that we collected in a mobile interaction study with 18 participants. We show that our method can achieve an accuracy of 86.8% on recognizing one of the 17 gestures and an accuracy of 94.6% to differentiate between finger and knuckle. In our evaluation study, we validate our models and found that the LSTM gesture recognizing archived an accuracy of 88.6%. We show that KnuckleTouch can be used to improve the input expressiveness and to provide shortcuts to frequently used functions.
Towards a taxonomy of interactive continual and multimodal learning for the internet of things

With advances in Internet of Things many opportunities arise if the challenges of continual learning in a multimodal setting can be tackled. One common issue in Online Learning is to obtain labelled data, as this generally is costly. Active Learning is a popular approach to collect labelled data efficiently, but in general includes unrealistic assumptions. In this work we present a first step towards a taxonomy of Interactive Learning strategies in a multimodal and dynamic setting. By relaxing assumptions of standard Active Learning, the strategies become better suited for real-world settings and can achieve better performance.
Article quality classification on Wikipedia: introducing document embeddings and content features

The quality of articles on the Wikipedia platform is vital for its success. Currently, the assessment of quality is performed manually by the Wikipedia community, where editors classify articles into pre-defined quality classes. However, this approach is hardly scalable and hence, approaches for the automatic classification have been investigated. In this paper, we extend this previous line of research on article quality classification by extending the set of features with novel content and edit features (e.g., document em-beddings of articles). We propose a classification approach utilizing gradient boosted trees based on this novel, extended set of features extracted from Wikipedia articles. Based on an established dataset containing Wikipedia articles and quality classes, we show that our approach is able to substantially outperform previous approaches (also including recent deep learning methods). Furthermore, we shed light on the contribution of individual features and show that the proposed features indeed capture the quality of an article well.
Do you have a source for that?: understanding the challenges of collaborative evidence-based journalism

WikiTribune is a pilot news service, where evidence-based articles are co-created by professional journalists and a community of volunteers using an open and collaborative digital platform. The WikiTribune project is set within an evolving and dynamic media landscape, operating under principles of openness and transparency. It combines a commercial for-profit business model with an open collaborative mode of production with contributions from both paid professionals and unpaid volunteers. This descriptive case study captures the first 12-months of WikiTribune's operations to understand the challenges and opportunities within this hybrid model of production. We use the rich literature on Wikipedia to understand the WikiTribune case and to identify areas of convergence and divergence, as well as avenues for future research. Data was collected on news articles with a focus on the time it takes for an article to reach published status, the number and type of contributors typically involved, article activity and engagement levels, and the types of topics covered.
BinarySwipes: Fast List Search on Small Touchscreens

Smartwatches and other wearables generally have small screens, thereby complicating touch-based interaction. Selection from a long list, e.g. to locate a contact or a music track, is particularly cumbersome due to the limited interaction space. We present BinarySwipes, an interaction technique based on binary search which is designed to speed up list search tasks on space-constrained screens. We evaluate a prototypical implementation of BinarySwipes on a smartwatch. Results from our evaluation with 21 participants show improved performance over a plain linear search on lists with 100, 200 and 500 entries, but also increased mental load on the users.
Smart eyewear enabled interactive pet toy for users with limited mobility

Smart eyewear that detects eye movements and head motions have been applied to studies on detecting oneâĂŹs mental state [5], engagement level in social interactions, and measuring concentration [4]. However, applications for these smart eyewear that offers intriguing and novel interactive experience has seldom been used as wearable device-control. As an application of this type of wearable, we used J! NS MEME that enables head motion data to control smart devices with hands-free operation in a pet toy system to facilitate human-animal interaction for people in cases of limited mobility either due to physical disability or the lack of enough space allowance. For our study, we primarily focused on exploring interaction possibilities between pet owners, who experience physical limitations due to the latter context, and their pets. The study result shows a promising start for opening up new interaction opportunities for our targeted audience in the context of physical limitation; however, the impact on those with physical disabilities is still arguable as it is yet to be evaluated. Nevertheless, since smart eyewear can act as an unobtrusive and useful body extension for people with limited mobility, we believe that it can be an alternative input option that can be applied not just to human-animal interaction but also to wider domains such smart devices and home systems for these users.
SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction

With the rapid progress of AI in both academia and industry, Deep Learning has been widely introduced into various areas in drug discovery to accelerate its pace and cut R&D costs. Among all the problems in drug discovery, molecular property prediction has been one of the most important problems. Unlike general Deep Learning applications, the scale of labeled data is limited in molecular property prediction. To better solve this problem, Deep Learning methods have started focusing on how to utilize tremendous unlabeled data to improve the prediction performance on small-scale labeled data. In this paper, we propose a semi-supervised model named SMILES-BERT, which consists of attention mechanism based Transformer Layer. A large-scale unlabeled data has been used to pre-train the model through a Masked SMILES Recovery task. Then the pre-trained model could easily be generalized into different molecular property prediction tasks via fine-tuning. In the experiments, the proposed SMILES-BERT outperforms the state-of-the-art methods on all three datasets, showing the effectiveness of our unsupervised pre-training and great generalization capability of the pre-trained model.
micro-Stress EMA

High levels of stress during pregnancy increase the chances of having a premature or low-birthweight baby. Perceived self-reported stress does not often capture or align with the physiological and behavioral response. But what if there was a self-report measure that could better capture the physiological response? Current perceived stress self-report assessments require users to answer multi-item scales at different time points of the day. Reducing it to one question, using microinteraction-based ecological momentary assessment (micro-EMA, collecting a single in situ self-report to assess behaviors) allows us to identify smaller or more subtle changes in physiology. It also allows for more frequent responses to capture perceived stress while at the same time reducing burden on the participant. We propose a framework for selecting the optimal micro-EMA that combines unbiased feature selection and unsupervised Agglomerative clustering. We test our framework in 18 women performing 16 activities in-lab wearing a Biostamp, a NeuLog, and a Polar chest strap. We validated our results in 17 pregnant women in real-world settings. Our framework shows that the question "How worried were you? " results in the highest accuracy when using a physiological model. Our results provide further in-depth exposure to the challenges of evaluating stress models in real-world situations.
Eldertainment or functional necessity?: how virtual agents affect the home lives of people with dementia using the quality of life (QOL-AD) scale

A large literature evaluates how virtual agents impact the lives of people with dementia using perceptions of technology. We assess how a home virtual agent from "Living Well with Anne" impacts the quality of life of elderly with dementia rather than only their perceptions of the technology. Assessing impact on life alongside technology perception is pertinent given the importance of a person's perceived quality of daily home life and that positive technology perception does not always lead to actual use. We propose an approach to evaluate assistive technology for elderly people with dementia by assessing impact on life using semi-structured interviews and the QOL-AD scale. A preliminary proof-of-concept study tests whether perceptions of a virtual agent, actual use of the agent and participants' quality of life are related, and whether a virtual agent improves quality of life.
Design and formative evaluation of cognitive assessment apps for wearable technologies

Cognitive functioning is a crucial aspect of the individual's mental health and it affects human's daily activities. We have developed the Ubiquitous Cognitive Assessment Tool (UbiCAT) including three cognitive assessment apps on the Fitbit smartwatch. In this paper, we present the design and formative evaluation of the UbiCAT apps conducted with 5 participants who had a background in design and/or human-computer interaction. Moreover, we investigated the adoption of the wearable devices by our participants.
Dwelling on Wikipedia: investigating time spent by global encyclopedia readers

Much existing knowledge about global consumption of peer-produced information goods is supported by data on Wikipedia page view counts and surveys. In 2017, the Wikimedia Foundation began measuring the time readers spend on a given page view (dwell time), enabling a more detailed understanding of such reading patterns. In this paper, we validate and model this new data source and, building on existing findings, use regression analysis to test hypotheses about how patterns in reading time vary between global contexts. Consistent with prior findings from self-report data, our complementary analysis of behavioral data provides evidence that Global South readers are more likely to use Wikipedia to gain in-depth understanding of a topic. We find that Global South readers spend more time per page view and that this difference is amplified on desktop devices, which are thought to be better suited for in-depth information seeking tasks.
Towards a real-time unsupervised estimation of predictive model degradation

Automating predictive machine learning entails the capability of properly triggering the update of the trained models. To this aim, the degradation of predictive models has to be continuously evaluated over time to detect data distribution drifts between the original training set and the new data. Traditionally, prediction performance is used as a degradation metric. However, prediction quality indices require ground-truth class labels to be known for the newly classified data, making them unsuitable for real-time applications, as ground-truth labels might be totally absent or be available only later. In this paper, we propose a novel unsupervised methodology to automatically detect prediction-quality degradation of machine learning models. Thanks to the unsupervised approach and a novel scalable estimation technique, we provide an effective and efficient solution to the above-mentioned problem with soft real-time constraints. Specifically, our approach is able to detect class-based concept drift, i.e., when new data contain samples that do not fit the set of class labels known by the currently-trained predictive model. Experiments on synthetic and real-world public datasets show the effectiveness of the proposed methodology in automatically detecting and describing concept drift caused by changes in the class-label data distributions. Thanks to its scalability performance, the proposed approach is suitable for soft real-time applications such as predictive maintenance, Industry 4.0, and text mining.
Attention computing: overview of mobile sensing applied to measuring attention

The measurement of participant attention is a frequent by-product of mobile sensing-based studies, which typically focus on user interruptibility or the effectiveness of notification deliveries. We note that, despite the popularity of interruptibility research within our discipline, research focused on attention is surprisingly scarce. This omission may be due to (a combination of) methodological, technological, or disciplinary constraints. In this paper, we argue how attention levels can be effectively measured with existing technologies and methodologies by adapting continuous measurements of attention fluctuations. Many clinically researched technologies, as well as sensing-based analysis methods, could be leveraged for this purpose. This paper invites co-researchers to assess the use of novel ways to measure attention in their future endeavours.
IMU-Kinect: a motion sensor-based gait monitoring system for intelligent healthcare

Gait rehabilitation is a common method of postoperative recovery after the user sustains an injury or disability. However, traditional gait rehabilitations are usually performed under the supervision of rehabilitation specialists, meaning the patients can not receive adequate care continuously. In this paper, we propose IMU-Kinect, a novel system to remotely and continuously monitor the gait rehabilitation via the wearable kit. This system consists of a wearable hardware platform and a user-friendly software application. The hardware platform is composed of four Inertial Measurement Units (IMU), which are attached on the shanks and thighs of the human body. The software application is able to estimate the rotation and displacement of these sensors, then reconstruct the gait movements and calculate the gait parameters according to the geometric model of human lower limbs. Based on IMU-Kinect system, the users of gait rehabilitation just need to walk normally by wearing the IMU-Kinect kit, and then the rehabilitation specialists can analyze the status of postoperative recovery by remotely viewing the animations about users' gait movements and charts of the general gait parameters. Extend experiments in real environment show that our system can efficiently track the gait movements with 9% rotation and displacement error.
“I Don't Understand…” Issues in Self-Quantifying Commuting

In the context of addressing global warming issues one of the possible approaches is to provide individuals with tools that support behavior change toward greener practices, as for example in commuting. This paper illustrates the results of a study that we conducted on the effectiveness of self-tracking of commuting data where participants received daily feedback on the financial costs and CO2 emissions associated to their mobility practices. In the results, we describe situations where users either misunderstood or did not accept the data and the models utilized to represent them, highlighting a limitation that diary instruments (and underlying models) of this type would have in supporting people to reflect upon and possibly change their mobility choices.