Miracle-dz/UniFAR-FTUs huggingface dataset:
What we talk about when we talk about wikidata quality: a literature survey

Launched in 2012, Wikidata has already become a success story. It is a collaborative knowledge graph, whose large community has produced so far data about more than 55 million entities. Understanding the quality of the data in Wikidata is key to its widespread adoption and future development. No study has investigated so far to what extent and which aspects of this topic have been addressed. To fill this gap, we surveyed prior literature about data quality in Wikidata. Our analysis includes 28 papers and categorise by quality dimensions addressed. We showed that a number of quality dimensions has not been yet adequately covered, e.g. accuracy and trustworthiness. Future work should focus on these.
Visualization Needs in Computational Social Sciences

With the advent of digital humanities and computational social sciences, machine learning techniques like topic modeling are increasingly employed by social scientists and humanities scholars. This poses the question what visualization needs these researchers have when confronted with such complex systems. In this paper, we investigate visualization needs in the context of the topic modeling algorithm Latent Dirichlet Allocation and the 950,000 articles of the New York Times corpus. We presented visualizations of how the topics in the newspaper changed over time to seven participants, who fulfilled three tasks with three visualization types. Qualitative interviews with the participants supported our assumptions that visualizations for these tasks need to be visually appealing, intuitively interpretable, and minimizing mental effort.
POIDEN: position and orientation independent deep ensemble network for the classification of locomotion and transportation modes

Sensor-based recognition of locomotion and transportation modes has numerous application domains including urban traffic monitoring, transportation planning, and healthcare. However, the use of a smartphone in a fixed position and orientation in previous research works limited the user behavior a lot. Besides, the performance of naive methods for position-independent cases was not up to the mark. In this research, we have designed a position and orientation independent deep ensemble network (POIDEN) to classify eight modes of locomotion and transportation activities. The proposed POIDEN architecture is constructed of a Recurrent Neural Network (RNN) with LSTM that is assigned the task of selecting optimum general classifiers (random forest, decision tree, gradient boosting, etc.) to classify the activity labels. We have trained the RNN architecture using an intermediate feature set (IFS), whereas, the general classifiers have been trained using a statistical classifier feature set (SCFS). The choice of a classifier by RNN is dependent upon the highest probability of those classifiers to recognize particular activity samples. We have also utilized the rotation of acceleration and magnetometer values from phone coordinate to earth coordinate, proposed jerk feature, and position insensitive features along with parameter adjustment to make the POIDEN architecture position and orientation independent. Our team "Gradient Descent" has presented this work for the "Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge".
SmartLobby: using a 24/7 remote head-eye-tracking for content personalization

In this work, we present the SmartLobby, an intelligent environment system integrated into the lobby of a research institute. The SmartLobby is running 24/7, i.e. it can be used any time by anyone without any preparations. The goal of the system is to conduct research in the domain of human machine cooperation. One important first step towards this goal is a detailed human state modeling and estimation with head-eye-tracking as key component. The SmartLobby mainly integrates state-of-the-art algorithms that enable a thorough analysis of human behavior and state. These algorithms constitute the fundamental basis for the development of higher level system components. Here, we present our system with its various hardware and software components. Thereby, we focus on the head-eye-tracking as a key component to continuously observe persons using the system and customize content shown to them. The results of a multi-week lasting experiment demonstrate the effectiveness of the system.
Temporal factors of listening to music on stress reduction

Listening to music has been studied as a method for combating the rapidly increasing stress levels of adolescents. Previous studies yielded inconsistent results and neglected specific factors including the time relative to the stressor and the duration of time in which participants listened to music. We conducted a survey and lab experiment to investigate the impact of these factors on the stress-reducing effect of music. The survey contained questions regarding music preference, stress, and use of music for stress reduction. In the lab experiment, the math task of the Trier Social Stress Test (TSST) was used to simulate stress in participants. Three experimental groups listened to music for either five minutes before the stressor, five minutes after the stressor, or ten minutes after the stressor; with the control group not listening to any music. Heart rate variability was continuously monitored with a wearable device, Empatica, and used to derive stress levels. The survey received 251 responses and 42 students participated the lab experiment. The results showed that listening to music before the stressor resulted in significantly lower stress levels than listening to music after the stressor (p < 0.01). This finding, contrary to our survey results, revealed that the "preventive" effect of listening to music prior to the stressor was more effective than the "remedial" effect that followed after the stressor.
Understanding the Users’ Understanding of Automated Vehicles

As vehicles get more automated it also becomes harder for the users to fully understand them. Previous research has shown that the users’ understanding affects acceptance and trust in automated vehicle systems. Furthermore, it has also been identified that the users’ conceptual model of automated vehicle systems did not match the designers’ conceptual model of the system. Therefore, it is important to understand the users’ understanding of automated vehicles in order to design the systems. This paper presents a PhD project with the aim to create further knowledge about the development of users’ mental models, and how the design of the interaction between users and vehicle affects this process. The paper first describes an initial experimental study, investigating users’ interpretation of an automated vehicle, and the initial results. Afterwards, the future directions and possible barriers are discussed.
Human-Centered Development of an Activity Diary App for People with Depression

As part of some treatments of depression, patients are asked to keep an activity diary - currently on a sheet of paper - in which they log their activities, and their emotions or mood. However, this way of keeping an activity diary has many disadvantages. A mobile application seems like a better solution. But how must an app for patients with depression be designed, to facilitate quick and effortless capture of activities, and emotions and/or mood? We used a human-centered development process with multiple design iterations and evaluations to develop an activity diary app for people with depression. In the process differing perspectives of therapists and patients became apparent, as well as within the group of therapists themselves. The resulting app was well-received by patients and therapists.
PDMove: Towards Passive Medication Adherence Monitoring of Parkinson's Disease Using Smartphone-based Gait Assessment

The medicine adherence in Parkinson’s disease (PD) treatment has attracted tremendous attention due to the critical consequences it can lead to otherwise. As a result, clinics need to ensure that the medicine intake is performed on time. Existing approaches, such as self-report, family reminder, and pill counts, heavily rely on the patients themselves to log the medicine intake (hereafter, patient involvement). Unfortunately, PD patients usually sufer from impaired cognition or memory loss, which leads to the so-called medication non-adherence, including missed doses, extra doses, and mistimed doses. These instances can nullify the treatment or even harm the patients. In this paper, we present PDMove, a smartphone-based passive sensing system to facilitate medication adherence monitoring without the need for patient involvement. Speciically, PDMove builds on the fact that PD patients will present gait abnormality if they do not follow medication treatment. To begin with, PDMove passively collects gait data while putting the smartphone in the pocket. Afterward, the gait preprocessor helps extract gait cycle containing the Parkinsonism-related biomarkers. Finally, the medicine intake detector consisting of a multi-view convolutional neural network predicts the medicine intake. In this way, PDMove enables the medication adherence monitoring. To evaluate PDMove, we enroll 247 participants with PD and collect more than 100,000 gait cycle samples. Our results show that smartphone-based gait assessment is a feasible approach to the AI-care strategy to monitor the medication adherence of PD patients.
Who Do You Want to Talk to?: User-centered Design for Human-like Robot Faces

Having a social robot as interaction partner will increase within the next years. In public spaces robots are already tested as information guide and assistance. However, the barrier of talking to a robot is still existent and appearance plays an important role for first contact. If robots are perceived as friendly and trustworthy, it might be easier to start talking to them. In this paper, we want to investigate the idea of a user-centered robot face design. We want to figure out, if users like designing robot faces on their own, how the faces are designed and perceived afterwards. The pilot study showed an open-minded attitude of participants towards the design and overall users were satisfied with their created faces and enjoyed talking to the robot afterwards. The results showed a diverse outcome of created face models and most of the faces were perceived as felicitous. The pilot study could give a first overview how potential users can be involved into the development and design process to find out about preferences.
Visualizing health with emotion polarity history using voice

Maintaining mental health as well as physical health is essential for our daily lives. We think that we could use a "mood" meter every day or regularly to see our own mental health condition similarly as in the case of weight meters. When performing emotion recognition using human speech, one of linguistic information and prosodic features included in speech is often used. However, by capturing both sides of speech, which is a means of human communication, it is considered that emotion recognition can be more accurately realized. Based on the background, we have developed PNViz, Positive-and-Negative Polarity Visualizer, an application running on an Android phone, to show the state of positive-ness of mental health by recording a short voice message. PNViz consists of the smartphone application and an analyzing server where the recorded voice is processed with both lexical and phonetic analyses and calculates a score ranging from -1 to 1. The calculated score is continuously logged and shown to the user and thus it is expected to encourage the user to take refreshing breaks or holidays.
Turn Your Head Half Round: VR Rotation Techniques for Situations With Physically Limited Turning Angle

Rotational tracking enables Virtual Reality (VR) users to turn their head around freely 360° while looking around the environment. However, there are situations when physical head rotation is only possible for not more than a certain range, e. g. , when the user sits in a bus or plane while she is wearing a VR headset. For these situations, rotation gains were introduced to decouple virtual and real rotations. We present two more techniques that allow 360° virtual turning in a physically limited space: Dynamic Rotation Gains and Scrolling. We conducted an experiment to compare those three rotation techniques and a baseline condition regarding VR sickness, spatial orientation, and usability. We found a significant underestimation of rotation angles for the dynamic rotation gains which might mean that this technique is more subtle than others. Furthermore, usability was higher and VR sickness lower for the dynamic rotation gains while scrolling caused the highest VR sickness. Finally, we conducted a confirmatory study to prove the applicability of dynamic rotation gains in an actual VR experience and got promising feedback.
Evaluating Pointing Modes and Frames of Reference for Remotely Supporting an Augmented Reality User in a Collaborative (Virtual) Environment: Evaluation within the Scope of a Remote Consultation Session

With the availability of powerful and affordable Augmented Reality (AR) devices, scenarios have become popular in which people wearing AR devices are supported by remote experts. These experts often use 2D peripherals to access the video feed of the 3D head mounted device (HMD) and to augment it with verbal or digital information. This raises the question whether tools that work for these scenarios also work for remote consultations. We conducted a study to (re-)evaluate these tools in a furniture sales consultation context. We focused on the consultant side of these settings and explored how the use of different pointing methods and perspectives affect different situations during a consultation. For this, we developed and evaluated a prototype with ten furniture store workers. Initial results show that while most usability and task load scores were even, the participants reported clear favorites for certain settings. We use these results to derive design recommendations for similar future projects.
CartRight: Maintaining Good Posture in the Presence of Adaptive Haptics

Haptic VR systems exert dynamically varying forces on virtually embodied users. VR approaches have always presented ergonomic challenges, but the emergence of adaptive haptics makes it increasingly difficult to maintain good posture. We look at the example of pushing a virtual shopping cart to explore relationships that exist between posture and adaptive haptics. Two experimental studies demonstrate (1) a direct effect of adaptive haptics on poor posture; and (2) that real-time feedback improves posture, even under significant dynamic musculoskeletal loading. Our findings suggest it is important to incorporate posture feedback in haptic VR design, and we also show that the resulting good posture extends beyond feedback.
WiDetect

Motion detection acts as a key component for a range of applications such as home security, occupancy and activity monitoring, retail analytics, etc. Most existing solutions, however, require special installation and calibration and suffer from frequent false alarms with very limited coverage. In this paper, we propose WiDetect, a highly accurate, robust, and calibration-free wireless motion detector that achieves almost zero false alarm rate and large through-the-wall coverage. Different from previous approaches that either extract data-driven features or assume a few reflection multipaths, we model the problem from a perspective of statistical electromagnetic (EM) by accounting for all multipaths indoors. By exploiting the statistical theory of EM waves, we establish a connection between the autocorrelation function of the physical layer channel state information (CSI) and target motion in the environment. On this basis, we devise a novel motion statistic that is independent of environment, location, orientation, and subjects, and then perform a hypothesis testing for motion detection. By harnessing abundant multipaths indoors, WiDetect can detect arbitrary motion, be it in Line-Of-Sight vicinity or behind multiple walls, providing sufficient whole-home coverage for typical apartments and houses using a single link on commodity WiFi. We conduct extensive experiments in a typical office, an apartment, and a single house with different users for an overall period of more than 5 weeks. The results show that WiDetect achieves a remarkable detection accuracy of 99.68% with a zero false rate, significantly outperforming the state-of-the-art solutions and setting up the stage for ubiquitous motion sensing in practice.
Process-mining based dynamic software architecture reconstruction

Dynamic architecture reconstruction approaches aim to reconstruct the run-time architecture of a software system. Process mining is an emerging field combining process analytics and data science techniques. In this paper, we present an approach that creates interactive architecture visualizations without requiring any knowledge of the source code of the system under study. The approach is implemented in the tool AJPOLog. with two case studies, we show that this approach creates reliable results with relatively little effort. Though the studies also show that more research is needed to apply process mining techniques in the field of architecture reconstruction.
How to shape the future of smart clothing

Smart textiles are garments with integrated sensors and actuators which have a large potential of being beneficial in our future society. These textiles can monitor our health condition during daily life, remind us to do some extra fitness activities or warn us when our blood pressure is too high or when our heart beating becomes irregular. They can be used in hospitals for the continuous monitoring of patients but also to help soldiers navigating the dark and athletes to optimize their performance. During the last 10 years three major smart textile related developments can be noted. First of all the sensors for wearables have become smaller, more reliable and easier to use. Next to that in the field of textile engineering new, and better electrical conducting yarns were developed which can be handled in textile production processes such as knitting and weaving. Thirdly, in the field of information technologies much more efficient algorithms for data processing and interpretation have been developed and more can be expected from the developments in artificial intelligence. Based on this the market for smart textiles is expected to grow exponentially but although researchers have been working on smart textiles already for a long time, we do not see them in the shop next-door yet. In this paper we will discuss the developments and trends, list the challenges and propose a strategy to come to the next generation of Smart Textiles.
A study of over-the-air (OTA) update systems for CPS and IoT operating systems

There is growing use of Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) in industry, homes, cars, and other environments, and several operating systems have been proposed to manage these environments. The growing use of long-lived IoT and CPS has made them susceptible to obsolescence and change, just like "normal" software, demanding systematic support for periodic updates of their embedded software. However, there is little empirical data about the structure, architecture, specifications, and dependencies of these subsystems. This article presents an analysis of over-the-air (OTA) update support in 26 existing open-source IoT/CPS operating systems and embedded software projects, performed primarily by examining their documentation and supplementing with occasional source code examination. We found that seven projects give details of an OTA update mechanism; four projects do not report details of OTA update mechanisms, but third-party developers implemented specific solutions to support OTA updates using these projects; and the remaining 15 projects do not report a particular update capability at all in their documentation. This study will allow extending, organize, and compare OTA update capabilities of future IoT/CPS operating systems.
MindTrain: How to Train Your Mind with Interactive Technologies

Technological products for training the mind that support subjective well-being are gaining popularity in our daily lives. Using Electroencephalographic (EEG) signals for neurofeedback is helpful for learning and a promising approach to train the mind. We introduce MindTrain, a novel, gamified neurofeedback training environment that allows users to learn the skill to voluntarily self-regulate their brain activity in Virtual Reality (VR). MindTrain combines the concept of implicit control with a mobile consumer EEG-wearable in an interactive and immersive VR-environment for visualising the feedback. We tested the feasibility of MindTrain for training to control states of relaxation and concentration. Our results prove that MindTrain is a promising novel method that warrants further investigation within a larger study. Furthermore, the use of the mobile EEG-wearable demonstrates the potential for bringing MindTrain out of the laboratory into a real-world context.
BoardWatch: a tree-enhanced regression model for billboard popularity prediction with multi-source urban data

Predicting the popularity of outdoor billboards is crucial for many applications such as guidance of billboard placement and estimation of advertising cost. Recently, some researchers have worked on leveraging single traffic data to access the performance of billboards, which often leads to coarse-grained performance estimation and undesirable ad placement plans. To solve the problem, we propose a data-driven system, named BoradWatch, for fine-grained billboard popularity prediction. In particular, we extract three types of critical features based on multi-source urban data, including billboard profile, geographic feature and commercial feature. Furthermore, we propose a hybrid model named Tree-Enhanced Regression Model (TERM) based on extracted features for prediction, which takes full advantage of the feature transformation of decision trees model to enhance the prediction performance of the linear model. Experiment results on real-world outdoor billboard data and multi-source urban data demonstrate the effectiveness of our work.
LungTrack

Respiration rate sensing plays a critical role in elderly care and patient monitoring. The latest research has explored the possibility of employing Wi-Fi signals for respiration sensing without attaching a device to the target. A critical issue with these solutions includes that good monitoring performance could only be achieved at certain locations within the sensing range, while the performance could be quite poor at other "dead zones. " In addition, due to the contactless nature, it is challenging to monitor multiple targets simultaneously as the reflected signals are often mixed together. In this work, we present our system, named LungTrack, hosted on commodity RFID devices for respiration monitoring. Our system retrieves subtle signal fluctuations at the receiver caused by chest displacement during respiration without need for attaching any devices to the target. It addresses the dead-zone issue and enables simultaneous monitoring of two human targets by employing one RFID reader and carefully positioned multiple RFID tags, using an optimization technique. Comprehensive experiments demonstrate that LungTrack can achieve a respiration monitoring accuracy of greater than 98% for a single target at all sensing locations (within 1st -- 5th Fresnel zones) using just one RFID reader and five tags, when the target's orientation is known a priori. For the challenging scenario involve two human targets, LungTrack is able to achieve greater than 93% accuracy when the targets are separated by at least 10 cm.