Reviewerly/exHarmony huggingface dataset:
On the Effect of User Tracking on Perceived Source Positions in Mobile Audio Augmented Reality

Mobile Audio Augmented Reality (AAR) allows users to live sonic experience where virtual sound objects are integrated seamlessly with the real-world acoustic space. This paper focuses on user perception of the virtual source position and the impact of different out-of-the-box user tracking and head orientation methods made available by iPad’s Augmented Reality (AR) camera tracking system and AirPods Pro. We propose an experimental procedure to rank tracking approaches according to perceived accuracy in positioning eye-level and ground-level virtual audio sources compared to real source references. To correctly provide a plausible AAR scenario, the proposed consumer electronic setup simulates a virtual sound source employing the scattering delay network (SDN) algorithm for calibrated dynamic auralisation and customized head-related transfer functions (HRTFs) for personalized user acoustics. The natural listening experience of real sound sources leverages AirPods’s active signal processing algorithms for headphone hear-through capabilities. The main result of this study lies in observing an accommodation effect by users interacting with different tracking approaches and sound source positions. In summary, participants tend to prefer more stable tracking solutions despite accuracy and wide head movement range.
Demo: an Interactive Visualization Combining Rule-Based and Feature Importance Explanations

The Human-Computer Interaction (HCI) community has long stressed the need for a more user-centered approach to Explainable Artificial Intelligence (XAI), a research area that aims at defining algorithms and tools to illustrate the predictions of the so-called black-box models. This approach can benefit from the fields of user-interface, user experience, and visual analytics. In this demo, we propose a visual-based tool, "F.I.P.E.R.", that shows interactive explanations combining rules and feature importance.
Poster: Human-in-the-Loop Anomaly Detection in Industrial Data Streams

The detection of anomalies in an industrial setting remains an important and open challenge for most manufacturing companies. The potential benefits from the utilization of an anomaly detection system are substantial, as deviations from normal operating conditions can cause downtimes, quailty issues or safety hazards. The main requirements for an anomaly detection system include the selection of the machine learning model applicable to streaming data, providing the explanations of the model’s decision and participation of human operator in the learning process of the model. We have proposed the anomaly detection system, which addresses the above challenges and is applicable in industrial environment.
Recruitment Chatbot Acceptance in Company Practices: An Elicitation Study

Recruitment bots are becoming increasingly popular, but there is limited research on how human-centered design principles guide their development. This study aims to address this gap by providing insights into the design of recruitment chatbots that prioritize human-centered principles and improve the overall experience for their final users. Semi-structured interviews with 4 recruiters and 6 job seekers were conducted to investigate their experiences with recruitment procedures and their perspectives on how chatbots could influence their experiences. The study revealed insights related to the impact of chatbots on the user experience and their acceptability in the context of human-centeredness, thus contributing to the development of next-generation, human-in-the-loop chatbot recruitment systems.
Localify.org: Locally-focus Music Artist and Event Recommendation

Cities with strong local music scenes enjoy many social and economic benefits. To this end, we are interested in developing a locally-focused artist and event recommendation system called Localify.org that supports and promotes local music scenes. In this demo paper, we describe both the overall system architecture as well as our core recommendation algorithm. This algorithm uses artist-artist similarity information, as opposed to user-artist preference information, to bootstrap recommendation while we grow the number of users. The overall design of Localify was chosen based on the fact that local artists tend to be relatively obscure and reside in the long tail of the artist popularity distribution. We discuss the role of popularity bias and how we attempt to ameliorate it in the context of local music recommendation.
Two-sided Calibration for Quality-aware Responsible Recommendation

Calibration in recommender systems ensures that the user’s interests distribution over groups of items is reflected with their corresponding proportions in the recommendation, which has gained increasing attention recently. For example, a user who watched 80 entertainment videos and 20 knowledge videos is expected to receive recommendations comprising about 80% entertainment and 20% knowledge videos as well. However, with the increasing calls for responsible recommendation, it has become inadequate to just match users’ historical behaviors especially when items are grouped by their qualities, which could result in undesired effects at the system level (e.g., overwhelming clickbaits). In this paper, we envision the two-sided calibration task that not only matches the users’ past interests distribution (user-level calibration) but also guarantees an overall target exposure distribution of different item groups (system-level calibration). The target group exposure distribution can be explicitly pursued by users, platform owners, and even the law (e.g., the platform owners expect about 50% knowledge video recommendation on the whole). To support this scenario, we propose a post-processing method named PCT. PCT first solves personalized calibration targets that minimize the changes in users’ historical interest distributions while ensuring the overall target group exposure distribution. Then, PCT reranks the original recommendation lists according to personalized calibration targets to generate both relevant and two-sided calibrated recommendations. Extensive experiments demonstrate the superior performance of the proposed method compared to calibrated and fairness-aware recommendation approaches.
Third Workshop on Recommender Systems for Human Resources (RecSys in HR 2023)

extended-abstract Share on Third Workshop on Recommender Systems for Human Resources (RecSys in HR 2023) Authors: Toine Bogers Department of Computer Science, IT University of Copenhagen, Denmark Department of Computer Science, IT University of Copenhagen, Denmark 0000-0003-0716-676XView Profile , David Graus Randstad, Netherlands Randstad, Netherlands 0000-0002-6245-0870View Profile , Mesut Kaya Department of Communication and Psychology, Aalborg University Copenhagen, Denmark Department of Communication and Psychology, Aalborg University Copenhagen, Denmark 0000-0003-2305-6683View Profile , Chris Johnson Indeed, USA Indeed, USA 0009-0007-5448-2833View Profile , Jens-Joris Decorte TechWolf, Belgium TechWolf, Belgium 0009-0000-7892-1211View Profile Authors Info & Claims RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsSeptember 2023Pages 1244–1247https://doi.org/10.1145/3604915.3608755Published:14 September 2023Publication History 0citation70DownloadsMetricsTotal Citations0Total Downloads70Last 12 Months70Last 6 weeks70 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Interpretable User Retention Modeling in Recommendation

Recommendation usually focuses on immediate accuracy metrics like CTR as training objectives. User retention rate, which reflects the percentage of today’s users that will return to the recommender system in the next few days, should be paid more attention to in real-world systems. User retention is the most intuitive and accurate reflection of user long-term satisfaction. However, most existing recommender systems are not focused on user retention-related objectives, since their complexity and uncertainty make it extremely hard to discover why a user will or will not return to a system and which behaviors affect user retention. In this work, we conduct a series of preliminary explorations on discovering and making full use of the reasons for user retention in recommendation. Specifically, we make a first attempt to design a rationale contrastive multi-instance learning framework to explore the rationale and improve the interpretability of user retention. Extensive offline and online evaluations with detailed analyses of a real-world recommender system verify the effectiveness of our user retention modeling. We further reveal the real-world interpretable factors of user retention from both user surveys and explicit negative feedback quantitative analyses to facilitate future model designs. The source codes are released at https://github.com/dinry/IURO.
Knowledge-Aware Recommender Systems based on Multi-Modal Information Sources

The last few years showed a growing interest in the design and development of Knowledge-Aware Recommender Systems (KARSs). This is mainly due to their capability in encoding and exploiting several data sources, both structured (such as knowledge graphs) and unstructured (such as plain text). Nowadays, a lot of models at the state-of-the-art in KARSs use deep learning, enabling them to exploit large amounts of information, including knowledge graphs (KGs), user reviews, plain text, and multimedia content (pictures, audio, videos). In my Ph.D. I will follow this research trend and I will explore and study techniques for designing KARSs leveraging representations learnt from multi-modal information sources, in order to provide users with fair, accurate, and explainable recommendations.
Evaluation of a Multimodal Interaction System for Big Displays

Big displays (ultrawalls) are increasingly present in professional, urban and leisure environments. Traditionally used as visualization tools, large-size screen surfaces may expand their interaction capabilities when integrated with sensors and mobile devices. This article describes a system that enables both voice and gesture control and mobile-mediated interaction with multiple information streams in a big display. The system offers support to a visualization model based on tiles and predefined layouts, which serves to handle the information streams and services. A set of specific commands combining voice and gesture control are preliminarily proposed. The vocabulary is enough to configure a command and monitoring service for fleets of drones. On this service scenario, the paper describes a study with ten users that has been carried out to verify that multimodal interaction can be efficient and perceived as acceptable, in particular when compared to mobile-enabled interaction. Results show that both proposals may deliver similar efficiency for simple tasks. Users are not reluctant to use multimodality, although they prefer voice over gestures and suggest combining both mobile and multimodal approaches.
Bursts of polarised single photons from atom-cavity sources

Abstract Photonic qubits play an instrumental role in the development of advanced quantum technologies, including quantum networking, boson sampling and measurement based quantum computing. A promising framework for the deterministic production of indistinguishable single photons is an atomic emitter coupled to a single mode of a high finesse optical cavity. Polarisation control is an important cornerstone, particularly when the polarisation defines the state of a quantum bit. Here, we propose a scheme for producing bursts of polarised single photons by coupling a generalised atomic emitter to an optical cavity, exploiting a particular choice of quantisation axis. In connection with two re-preparation methods, simulations predict ten-photon bursts coincidence count rates on the order of 1 kHz with single <?CDATA $^{87}\mathrm{Rb}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mi /> <mml:mrow> <mml:mn>87</mml:mn> </mml:mrow> </mml:msup> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> <mml:mi mathvariant="normal">b</mml:mi> </mml:mrow> </mml:math> atoms trapped in a state of the art optical cavity. This paves the way for novel n -photon experiments with atom-cavity sources.
Stories from the Empty School Desk: Places, Objects and Memories in Augmented Reality

Stories from the Empty School Desk is an educational project and experience based on the use of augmented reality and designed to raise the awareness of young generations about the dramatic events related to the persecution of Jews and the Italian resistance movement during Italian Fascism and the Nazi occupation of Italy. The project is characterized by the design of a temporary place of memory, the reconstruction of an Italian classroom of the 30s-40s, to be explored with AR technology. The AR experience was created with the collaboration of the high school’s teachers and students, starting from information available in the historical archive of the school itself and other trusted sources. In the AR experience, visitors are engaged with objects, historical documents, and human silhouettes that populate the classroom. An evaluation study displays good results for several analyzed dimensions, including engagement and ethics.
MenstruWear: In-The-Wild Study For Characterizing The Effect Of User Activities On Sanitary Napkin-Based Bodily Fluid Monitoring

Wearable sanitary napkin sensors can capture the effect of physiological variations and activities of users in a real-life setting, while lab studies tend to focus on input parameter changes. We introduce a wearable sanitary napkin sensor system to explore discharge bodily fluid volume measurement opportunities in the wild. We use this system to understand the impact of body parameters and activities on the sensing system, such as standing, sitting, walking, and lying down. We identified several engineering, methodological, and ethical challenges associated with wearable intimate device design. Our discussions contribute to an ongoing dialogue around women’s health in HCI and the barriers to personal care technology design. Through four-hour-long user studies with 10 participants on different activities, we showed that the sensing system behaves uniformly for all activities and across users. Using feedback interviews with participants, we find that our system is comfortable for performing activities and valuable technology for females.
VisionARy: Exploratory research on Contextual Language Learning using AR glasses with ChatGPT

Language learning is a challenging and time-consuming process, requiring an immersive environment and active usage of the language. However, English as a Foreign Language (EFL) learners often face difficulties in obtaining immersive learning experiences due to one-sided teaching and a lack of real-life English language contexts for practice. To overcome this challenge, various approaches have been proposed that leverage technologies such as mobile-based augmented reality and chatbots. However, these approaches have limitations that hinder full immersion into the language learning environment and fail to adequately contextualize the learning experience. With the recent breakthroughs in large language models and the development of lighter and more powerful AR glasses, we propose VisionARy, the first system that integrates ChatGPT into AR glasses to improve oral language skills and provide a contextualized learning experience. The evaluation results suggest that VisionARy has the potential to be effective and is highly accepted in improving oral language skills, compared to traditional learning methods. Furthermore, our findings provide important insights for the future design of language learning systems.
Bridging Objective and Subjective Evaluations in Data Visualization: a Crossover Experiment

One of the problems affecting evaluation in the design and adoption of HCI technology is that neither objective nor subjective measures are sufficient when taken alone or individually. This paper proposes a crossover approach, making sense of objective and subjective evaluation methods by hypothesizing them as constitutive of each other’s explanation. Objective image features borrowed from image processing may explain or being explained in terms of validated qualitative items for infographics value-in-use and qualitative labelling from users’ interaction. These methods are all applied to the evaluation of a small set of Data Vizualizations (Data Viz from now on). Image features are computed first, in order to provide a varied-features Data Viz selection from researchers; the subjective part of the evaluation is accomplished by the 98 participants of an experiment, who interacted with pairs of Data Viz by executing a task, then using the validated items of the Infographics-Value (IGV) short scale, and adding free qualitative comments. Crossing over these dimensions shows that: a high feature congestion in a Data Viz can hinder its perceived intuitiveness and clarity; a poorly distributed saliency may impact intuitiveness and clarity too; a high colorfulness may influence the perceived beauty; both saliency and colorfulness may impact on the perceived usefulness, informativity, and beauty. Furthermore, colorfulness can improve or worsen the perceived overall quality of design and quality of interaction when used and combined with feature congestion; and saliency may improve or worsen the perceived beauty when interacting with colorfulness. These results show how objective and subjective evaluations may be exploited as each other’s explanations for improving the evaluation process during both design and user experience with Data Viz. Based on this experiment, the importance of crossing-over quantitative and qualitative Data Viz evaluation is argued, and motivations to the exploitation of a combination of approaches instead of the application of one approach alone are supported. This contribution intends to lead towards a holistic Data Viz quality assessment method, able to provide a virtuous cycle enforcing both quantitative and qualitative approaches during all the phases of a Data Viz evaluation life.
Virtual Reality for Object Location Spatial Memory: A Comparison of Handheld Controllers and Force Feedback Gloves

Object Location Spatial Memory (OLSM) is used in everyday tasks for which it is important to remember where objects are in space. The spatial image of the environment to perform such OLSM tasks is created based on inputs from three spatial senses (visual, auditory, and haptic). Various causes, ranging from traumatic brain injury to Alzheimer’s disease and dementia, can compromise OLSM, requiring OLSM rehabilitation techniques. Virtual reality (VR) can provide a safe environment for engaging rehabilitation experiences. In the last years, new technologies for multi-modal interaction such as force feedback gloves (FGs) have appeared on the market, but their possible advantages over handheld controllers (HCs) that come together with head-mounted displays have not been evaluated in the context of OLSM tasks. This paper investigates whether adding haptic input can lead to more effective OLSM training by comparing a pair of HCs with a pair of FGs in performing the same OLSM task, i.e. placing different objects in memorized locations. We conducted a within-subjects user study with 24 participants who performed the OLSM task in two conditions: with HCs and with FGs. Presence was measured with the IPQ questionnaire, and results showed statistically significant differences in favor of interacting with FGs on the general item about the sense of being there. Participants judged the system usability high in both conditions. Perceived fatigue was higher when using FGs. We expected better performance with FGs thanks to the addition of haptic input, but no statistically significant differences were found in the total number of correctly placed objects. Results showed that the time to complete the task was lower with the HCs than FGs. Future comparisons with other types of FGs may confirm that this type of OLSM task does not benefit from specialized haptic hardware, supporting the possibility of performing OLSM rehabilitation exercises at patients’ home with affordable commercial VR kits.
Lagrangian motion magnification with double sparse optical flow decomposition

Microexpressions are fast and spatially small facial expressions that are difficult to detect. Therefore, motion magnification techniques, which aim at amplifying and hence revealing subtle motion in videos, appear useful for handling such expressions. There are basically two main approaches, namely, via Eulerian or Lagrangian techniques. While the first one magnifies motion implicitly by operating directly on image pixels, the Lagrangian approach uses optical flow (OF) techniques to extract and magnify pixel trajectories. In this study, we propose a novel approach for local Lagrangian motion magnification of facial micro-motions. Our contribution is 3-fold: first, we fine tune the recurrent all-pairs field transforms (RAFT) for OFs deep learning approach for faces by adding ground truth obtained from the variational dense inverse search (DIS) for the OF algorithm applied to the CASME II video set of facial micro expressions. This enables us to produce OFs of facial videos in an efficient and sufficiently accurate way. Second, since facial micro-motions are both local in space and time, we propose to approximate the OF field by sparse components both in space and time leading to a double sparse decomposition. Third, we use this decomposition to magnify micro-motions in specific areas of the face, where we introduce a new forward warping strategy using a triangular splitting of the image grid and barycentric interpolation of the RGB vectors at the corners of the transformed triangles. We demonstrate the feasibility of our approach by various examples.
Using ANN-MLP for Supervised Classification of the Hercynian Granitoids from Their Geochemical Chara Teristics at the Aouli Pluton. (High Moulouya, Morrocco)

The study of rare earths, trace elements and major elements makes it possible to distinguish the different entities of a plutonic complex in general. The aim of this work is to apply the supervised classification model based on artificial neural networks Multi-layer Perceptron (ANN-MLP) in a database of 168 granitoid samples of the Aouli pluton (High Moulouya, Morocco). The 168 input variables of the model correspond to the major elements, trace elements and rare earths. They were obtained by analyzing samples of the Aouli granitoids by X-ray spectroscopy for the major elements, X-ray fluorescence for trace elements, specific ion electrode for fluorine and neutron activation for soil analysis (Oukemeni 1993). Initially, 60% of the database, taken randomly, was used for the formation and choice of the architecture of the neural network MLP. Secondly, unknown test samples were identified by using ANN-MLP model determined in the learning phase. The relative performance of this model (ANN-MLP) was evaluated by calculating the coefficient of determination R2 and the NSE coefficient (Nash-Sutcliffe Efficiency). The results are reflected in the success rate of 86% and the coefficient of determination R2 of 66.4%, which is to choose the model ANN-MLP [20-2-4].