Reviewerly/exHarmony huggingface dataset:
TypeAnywhere: A QWERTY-Based Text Entry Solution for Ubiquitous Computing

We present a QWERTY-based text entry system, TypeAnywhere, for use in off-desktop computing environments. Using a wearable device that can detect finger taps, users can leverage their touch-typing skills from physical keyboards to perform text entry on any surface. TypeAnywhere decodes typing sequences based only on finger-tap sequences without relying on tap locations. To achieve optimal decoding performance, we trained a neural language model and achieved a 1.6% character error rate (CER) in an offline evaluation, compared to a 5.3% CER from a traditional n-gram language model. Our user study showed that participants achieved an average performance of 70.6 WPM, or 80.4% of their physical keyboard speed, and 1.50% CER after 2.5 hours of practice over five days on a table surface. They also achieved 43.9 WPM and 1.37% CER when typing on their laps. Our results demonstrate the strong potential of QWERTY typing as a ubiquitous text entry solution.
HybridTrak: Adding Full-Body Tracking to VR Using an Off-the-Shelf Webcam

Full-body tracking in virtual reality improves presence, allows interaction via body postures, and facilitates better social expression among users. However, full-body tracking systems today require a complex setup fixed to the environment (e.g., multiple lighthouses/cameras) and a laborious calibration process, which goes against the desire to make VR systems more portable and integrated. We present HybridTrak, which provides accurate, real-time full-body tracking by augmenting inside-out1 upper-body VR tracking systems with a single external off-the-shelf RGB web camera. HybridTrak uses a full-neural solution to convert and transform users’ 2D full-body poses from the webcam to 3D poses leveraging the inside-out upper-body tracking data. We showed HybridTrak is more accurate than RGB or depth-based tracking methods on the MPI-INF-3DHP dataset. We also tested HybridTrak in the popular VRChat app and showed that body postures presented by HybridTrak are more distinguishable and more natural than a solution using an RGBD camera.
Anti-Racist HCI: notes on an emerging critical technical practice

Our everyday technologies evidence clear examples of racial bias. Rather than attempt to eliminate bias through seeking fairness in algorithms, regulatory intervention or a race-blind stance, this paper seeks to correct the balance by adopting an explictly anti-racist approach to the design of sociotechnical systems. As a research-through-design initiative, we bring techniques from critical technical practice to bear on revealing and inverting assumptions in HCI, attempting to produce alternative sociotechnical systems that aim not merely to reveal or correct but to destablize or dismantle systems of oppression. We articulate core principles to guide such work and articulate four system prototypes to interrogate anti-racist HCI as a potential form of critical technical practice. We conclude with discussion of the challenges that face anti-racist HCI in terms of timing, reflection, and failure, addressing what an anti-racist critical technical practice reveals about the enduring structural sources of inequity in the products and practices of HCI.
Virtual Transcendent Dream: Empowering People through Embodied Flying in Virtual Reality

Flying dreams have the potential to evoke a feeling of empowerment (or self-efficacy, confidence in our ability to succeed) and self-transcendent experience (STE), which have been shown to contribute to an individual’s overall well-being. However, these exceptional dreaming experiences remain difficult to induce at will. Inspired by the potential of Virtual Reality (VR) to support profound emotional experiences, we explored if a VR flying interface with more embodied self-motion cues could contribute to the benefits associated with flying dreams (i.e., STE and empowerment). Our results indicated that a flying interface with more self-motion cues indeed better supported STE and empowerment. We derived several design considerations: obscurity, extraordinary light and supportive setting. Our results contribute to the discourse around design guidelines for self-transcendence and empowerment in VR, which may further be applied to the improvement of mental well-being.
Consent in the Age of AR: Investigating The Comfort With Displaying Personal Information in Augmented Reality

Social Media (SM) has shown that we adapt our communication and disclosure behaviors to available technological opportunities. Head-mounted Augmented Reality (AR) will soon allow to effortlessly display the information we disclosed not isolated from our physical presence (e.g., on a smartphone) but visually attached to the human body. In this work, we explore how the medium (AR vs. Smartphone), our role (being augmented vs. augmenting), and characteristics of information types (e.g., level of intimacy, self-disclosed vs. non-self-disclosed) impact the users' comfort when displaying personal information. Conducting an online survey (N=148), we found that AR technology and being augmented negatively impacted this comfort. Additionally, we report that AR mitigated the effects of information characteristics compared to those they had on smartphones. In light of our results, we discuss that information augmentation should be built on consent and openness, focusing more on the comfort of the augmented rather than the technological possibilities.
UniTE: Unified Translation Evaluation

Translation quality evaluation plays a crucial role in machine translation.According to the input format, it is mainly separated into three tasks, i.e., reference-only, source-only and source-reference-combined. Recent methods, despite their promising results, are specifically designed and optimized on one of them.This limits the convenience of these methods, and overlooks the commonalities among tasks.In this paper, we propose UniTE, which is the first unified framework engaged with abilities to handle all three evaluation tasks.Concretely, we propose monotonic regional attention to control the interaction among input segments, and unified pretraining to better adapt multi-task learning.We testify our framework on WMT 2019 Metrics and WMT 2020 Quality Estimation benchmarks.Extensive analyses show that our single model can universally surpass various state-of-the-art or winner methods across tasks.Both source code and associated models are available at https://github.com/NLP2CT/UniTE.
Effects of Explanations in AI-Assisted Decision Making: Principles and Comparisons

Recent years have witnessed the growing literature in empirical evaluation of explainable AI (XAI) methods. This study contributes to this ongoing conversation by presenting a comparison on the effects of a set of established XAI methods in AI-assisted decision making. Based on our review of previous literature, we highlight three desirable properties that ideal AI explanations should satisfy — improve people’s understanding of the AI model, help people recognize the model uncertainty, and support people’s calibrated trust in the model. Through three randomized controlled experiments, we evaluate whether four types of common model-agnostic explainable AI methods satisfy these properties on two types of AI models of varying levels of complexity, and in two kinds of decision making contexts where people perceive themselves as having different levels of domain expertise. Our results demonstrate that many AI explanations do not satisfy any of the desirable properties when used on decision making tasks that people have little domain expertise in. On decision making tasks that people are more knowledgeable, the feature contribution explanation is shown to satisfy more desiderata of AI explanations, even when the AI model is inherently complex. We conclude by discussing the implications of our study for improving the design of XAI methods to better support human decision making, and for advancing more rigorous empirical evaluation of XAI methods.
LDGC-SR: Integrating long-range dependencies and global context information for session-based recommendation

Graph neural networks (GNN) have been applied in the session-based recommendation, which aims to predict the potential items the user will interact with next time based on the given anonymous interaction sequences. Most advanced GNN-based recommendation methods (e.g., LESSR, SGNN-HN and GCE-GNN) focus on how to either capture long-range dependencies or use global context information to enhance the recommendation performance. However, these approaches do not consider whether they can enhance each other and cooperatively improve performance. In addition, these methods mainly take the representation of the last interacted item as current interest, ignoring the potential information in other recent interactions and neighbor sessions. To solve these issues, we propose a model called LDGC-SR to integrate Long-range Dependencies and Global Context information for Session-based Recommendation. LDGC-SR employs normalization and adaptive weight fusion mechanism (NAWFM) to integrate long-range node information and global context information to alleviate the over-smooth problem of item representation and the over-fitting problem of the model. NAWFM can reduce the numerical bias between long-range dependencies and global context information, and dynamically adjust their importance to the user’s preference. Moreover, LDGC-SR employs a global context enhanced short-term memory module (GCE-SMM) to capture the user’s current interest more accurately and incorporate it as an auxiliary task. The experimental results demonstrate that our proposed method performs better than the state-of-the-art methods on all four public datasets, and provide suggestions for future session-based recommendation systems.
Quantitative strongest post: a calculus for reasoning about the flow of quantitative information

We present a novel strongest-postcondition-style calculus for quantitative reasoning about non-deterministic programs with loops. Whereas existing quantitative weakest pre allows reasoning about the value of a quantity after a program terminates on a given initial state, quantitative strongest post allows reasoning about the value that a quantity had before the program was executed and reached a given final state. We show how strongest post enables reasoning about the flow of quantitative information through programs. Similarly to weakest liberal preconditions, we also develop a quantitative strongest liberal post. As a byproduct, we obtain the entirely unexplored notion of strongest liberal postconditions and show how these foreshadow a potential new program logic - partial incorrectness logic - which would be a more liberal version of O'Hearn's recent incorrectness logic.
Randomness-oriented Multi-dimensional Cloud-based belief rule Base approach for complex system modeling

Many complex system modeling problems have to face two major challenges, randomness and interpretability, which are the motives and major contributions of this study. For the randomness challenge, the cloud model is adopted which reveals randomness by assuming the points within an interval following a certain distribution. For the interpretability challenge, the Belief Rule Base (BRB) is adopted which is essentially a white-box with direct access to experts and decision-makers. Therefore, a new approach is proposed to meet the two challenges by combining the cloud model and BRB, namely Cloud-BRB. In the new Cloud-BRB approach, new rule activation and matching degree calculation procedures have been proposed to better accommodate the cloud model into BRB. Moreover, new weight calculation procedures have also been proposed based on the multi-dimensional cloud model which connects the multiple attributes in practical problems and multiple attributes in BRB. A practical case of risk assessment on tunnel-induced damages to pipes is studied. Case study results show the consistency with previous approaches, which validate the efficiency of the proposed Cloud-BRB approach. As the belief distribution and randomness have been included in the Cloud-BRB approach, the results are more informative for decision-makers than previous approaches. Furthermore, Pipe No. 10 has been used to demonstrate the key influential factors identification process by the Cloud-BRB approach.
AirRes Mask: A Precise and Robust Virtual Reality Breathing Interface Utilizing Breathing Resistance as Output Modality

Increased levels of interactivity and multi-sensory stimulation have been shown to enhance the immersion of Virtual Reality experiences. We present the AirRes mask that enables users to utilize their breathing for precise natural interactions with the virtual environment without suffering from limitations of the sensing equipment such as motion artifacts. Furthermore, the AirRes mask provides breathing resistance as novel output modality that can be adjusted in real-time by the application. In a user study, we demonstrate the mask's precision measurements for interaction as well as its ability to use breathing resistance to communicate contextual information such as adverse environmental conditions that affect the user's virtual avatar. Our results show that the AirRes mask enhances virtual experiences and has the potential to create more immersive scenarios for applications by enforcing the perception of danger or improving situational awareness in training simulations, or for psychotherapy by providing additional physical stimuli.
Family Learning Talk in AI Literacy Learning Activities

The unique role that AI plays in making decisions that affect human lives creates a need to foster improved public understanding of AI systems. Informal learning spaces are particularly important contexts for fostering AI literacy, as they have the potential to reach a broader audience and provide spaces for children and parents to learn together. This paper explores 1) what types of dialogue family groups engage in when learning about AI in an at-home learning environment in order to inform our understanding of 2) how to design AI literacy activities for informal learning contexts. We present an analysis of family group dialogue surrounding three different AI education activities and use our findings to reflect on, update, and add to existing principles for designing AI literacy educational interventions.
Rank-in-Rank Loss for Person Re-identification

Person re-identification (re-ID) is commonly investigated as a ranking problem. However, the performance of existing re-ID models drops dramatically, when they encounter extreme positive-negative class imbalance (e.g., very small ratio of positive and negative samples) during training. To alleviate this problem, this article designs a rank-in-rank loss to optimize the distribution of feature embeddings. Specifically, we propose a Differentiable Retrieval-Sort Loss (DRSL) to optimize the re-ID model by ranking each positive sample ahead of the negative samples according to the distance and sorting the positive samples according to the angle (e.g., similarity score). The key idea of the proposed DRSL lies in minimizing the distance between samples of the same category along with the angle between them. Considering that the ranking and sorting operations are non-differentiable and non-convex, the DRSL also performs the optimization of automatic derivation and backpropagation. In addition, the analysis of the proposed DRSL is provided to illustrate that the DRSL not only maintains the inter-class distance distribution but also preserves the intra-class similarity structure in terms of angle constraints. Extensive experimental results indicate that the proposed DRSL can improve the performance of the state-of-the-art re-ID models, thus demonstrating its effectiveness and superiority in the re-ID task.
Are Ambient Intelligence and Augmented Reality Two Sides of the Same Coin? Implications for Human-Computer Interaction

We revisit the foundational principles of Ambient Intelligence (AmI) and Augmented Reality (AR) environments to discuss the perspective that AmI and AR feature the same vision of computing, as intuited at their origins, despite their recent development into what may appear as two distinct areas of scientific investigation. We focus on three concepts core to both AmI and AR, on which we capitalize to argue that a significant philosophical overlap exists between their visions: (1) the concept of an environment that undergoes a form of augmentation, (2) the indispensable process of an integration involving the environment, and (3) the emergence of a specific form of media congruent with the characteristics of the environment in which they are created, transmitted, and consumed. We draw implications for the science and practice of Human-Computer Interaction regarding new interactive environments enabled by the technologies of AmI and AR used conjointly.
Speeding up Inference with User Simulators through Policy Modulation

The simulation of user behavior with deep reinforcement learning agents has shown some recent success. However, the inverse problem, that is, inferring the free parameters of the simulator from observed user behaviors, remains challenging to solve. This is because the optimization of the new action policy of the simulated agent, which is required whenever the model parameters change, is computationally impractical. In this study, we introduce a network modulation technique that can obtain a generalized policy that immediately adapts to the given model parameters. Further, we demonstrate that the proposed technique improves the efficiency of user simulator-based inference by eliminating the need to obtain an action policy for novel model parameters. We validated our approach using the latest user simulator for point-and-click behavior. Consequently, we succeeded in inferring the user's cognitive parameters and intrinsic reward settings with less than 1/1000 computational power to those of existing methods.
AutoLossGen: Automatic Loss Function Generation for Recommender Systems

In recommendation systems, the choice of loss function is critical since a good loss may significantly improve the model performance. However, manually designing a good loss is a big challenge due to the complexity of the problem. A large fraction of previous work focuses on handcrafted loss functions, which needs significant expertise and human effort. In this paper, inspired by the recent development of automated machine learning, we propose an automatic loss function generation framework, AutoLossGen, which is able to generate loss functions directly constructed from basic mathematical operators without prior knowledge on loss structure. More specifically, we develop a controller model driven by reinforcement learning to generate loss functions, and develop iterative and alternating optimization schedule to update the parameters of both the controller model and the recommender model. One challenge for automatic loss generation in recommender systems is the extreme sparsity of recommendation datasets, which leads to the sparse reward problem for loss generation and search. To solve the problem, we further develop a reward filtering mechanism for efficient and effective loss generation. Experimental results show that our framework manages to create tailored loss functions for different recommendation models and datasets, and the generated loss gives better recommendation performance than commonly used baseline losses. Besides, most of the generated losses are transferable, i.e., the loss generated based on one model and dataset also works well for another model or dataset. Source code of the work is available at https://github.com/rutgerswiselab/AutoLossGen.
A Novel Deep Auto-Encoder Based Linguistics Clustering Model for Social Text

The wide adoption of media and social media has increased the amount of digital content to an enormous level. Natural language processing (NLP) techniques provide an opportunity to extract and explore meaningful information from a large amount of text. Among natural languages, Urdu is one of the widely used languages worldwide for spoken and written communications. Due to its wide adopt-ability, digital content in the Urdu language is increasing briskly, especially with social media and online NEWS feeds. Government agencies and advertisers must filter and understand the content to analyze the trends and cohorts in their interest and national prerogative. Clustering is considered a baseline and one of the first steps in natural language understanding. There are many state-of-the-art clustering techniques specifically for English, French, and Arabic, but no significant research has been conducted in Urdu language processing. Doing it for short text segments is challenging because of limited features and the absence of meaningful language discourse and nuance. Many rule-based NLP techniques are adopted to overcome these issues, relying on human-designed features and rules. Therefore, these methods do not promise remarkable results. Alongside NLP, deep learning techniques are pretty efficient in capturing contextual information with minimal noise compared to other traditional methods. By taking on this challenging job, we develop a deep learning-based technique for Urdu short text clustering for the very first time without a human-designed feature. In this paper, we propose a method of short text clustering using a deep neural network that automatically learns feature representations and clustering assignments simultaneously. This method learns clustering objectives by converting the high dimensional feature space to a low dimensional feature space. Our experiments on the Urdu NEWS headlines dataset show remarkable results compared to state-of-the-art methods.
Public Views on Digital COVID-19 Certificates: a Mixed Methods User Study

The COVID-19 pandemic has led governments worldwide to introduce various measures restricting human activity and mobility. Along with the administration of COVID-19 vaccinations and rapid testing, socio-technological solutions such as digital COVID-19 certificates have been considered as a strategy to lessen these restrictions and allow the resumption of routine activities. Using a mixed-methods approach – a survey (n=1008) and 27 semi-structured interviews – this study explores the attitudes of residents in the Republic of Ireland towards the idea of introducing digital COVID-19 certificates. We examine the topics of acceptability, fairness, security and privacy of COVID-related personal data, and practical considerations for implementation. Our study reveals the conditional and contextual nature of the acceptability of digital certificates, identifying specific factors that affect it, associated data practices, and related public concerns and expectations of such technologies.
Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles

Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers. However, most of the previous work utilized ensemble models in the context of software defect prediction with the default hyperparameter values, which are considered suboptimal. In this paper, we investigate the applicability of a stacking ensemble built with fine-tuned tree-based ensembles for defect prediction. We used grid search to optimize the hyperparameters of seven tree-based ensembles: random forest, extra trees, AdaBoost, gradient boosting, histogram-based gradient boosting, XGBoost and CatBoost. Then, a stacking ensemble was built utilizing the fine-tuned tree-based ensembles. The ensembles were evaluated using 21 publicly available defect datasets. Empirical results showed large impacts of hyperparameter optimization on extra trees and random forest ensembles. Moreover, our results demonstrated the superiority of the stacking ensemble over all fine-tuned tree-based ensembles.