Reviewerly/exHarmony huggingface dataset:
Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Public Health

The promise of visualization recommendation systems is that analysts will be automatically provided with relevant and high-quality visualizations that will reduce the work of manual exploration or chart creation. However, little research to date has focused on what analysts value in the design of visualization recommendations. We interviewed 18 analysts in the public health sector and explored how they made sense of a popular in-domain dataset1 in service of generating visualizations to recommend to others. We also explored how they interacted with a corpus of both automatically- and manually-generated visualization recommendations, with the goal of uncovering how the design values of these analysts are reflected in current visualization recommendation systems. We find that analysts champion simple charts with clear takeaways that are nonetheless connected with existing semantic information or domain hypotheses. We conclude by recommending that visualization recommendation designers explore ways of integrating context and expectation into their systems.
Dancing with the Avatars: Feedforward Learning from Self-Avatars

Feedforward is a training technique where people observe themselves perform a new skill to promote rapid learning, commonly implemented via video self-modelling. Avatars provide a unique opportunity to self-model skills an individual's physical self cannot yet perform. We investigated the use of avatars in video-based learning and explore the potential of feedforward learning from self-avatars. Using modern dancing as a skill to learn, we compared the user experience when learning from a human training video and an avatar training video, considering both self-avatars (n=8) and gender-matched generic avatars (n=8). Our results indicate that learning from avatars can improve the user experience over learning from a human in a video, providing attentional and motivational benefits. Furthermore, self-avatars make the training more relatable and immersive than generic avatars. We discuss the implications from this preliminary work, highlighting methodological considerations for feedforward learning from avatars and promising future work.
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning

Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when explaining predictions of images that are subject to systematic error (bias) by perturbations and corruptions. Furthermore, the distortions persist despite model fine-tuning on images biased by different factors (blur, color temperature, day/night). We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for explanation and bias level predictions. In simulation studies, the approach not only enhanced prediction accuracy, but also generated highly faithful explanations about these predictions as if the images were unbiased. In user studies, debiased explanations improved user task performance, perceived truthfulness and perceived helpfulness. Debiased training can provide a versatile platform for robust performance and explanation faithfulness for a wide range of applications with data biases.
Everywhere Cursor: Extending Desktop Mouse Interaction into Spatial Augmented Reality

Spatial augmented reality (SAR) allows us to extend desktop environments to our surroundings. However, previous works on enabling mouse interaction in SAR lacks consistent pointing behaviour with desktop environments. We present Everywhere Cursor, an indirect mouse interaction technique that moves along the environment's surface geometry, which preserves mouse behaviour in desktop environments. We discuss fundamental design problems in extending desktop mouse interaction to SAR, implementation details of our system, and guidelines on future improvements. Our work provides an important step in the transition of desktop environments to a pervasive computing system.
Exploring Discrete Drawing Guides to Assist Users in Accurate Mid-air Sketching in VR

Even though VR design applications that support sketching are popular, sketching accurately in mid-air is challenging for users. In this paper, we explore discrete visual guides that assist users’ stroke accuracy and drawing experience inside the virtual environment. We also present an eye-tracking study that compares continuous, discrete, and no guide in a basic drawing task. Our experiment asks participants to draw a circle and a line using three different guide types, three different sizes and two different orientations. Results indicate that discrete guides are more user-friendly than continuous guides, as the majority of participants preferred their use, while we found no difference in speed/accuracy compared to continuous guides. Potentially, this can be attributed to distinct eye-gaze strategies, as discrete guides led users to shift their eyes more frequently between guide points and the drawing cursor. Our insights are useful for practitioners and researchers in 3D sketching, as they are a first step to inform future design applications of how visual guides inside the virtual environment affect visual behaviour and how eye-gaze can become a tool to assist sketching.
Semantic sentiment analysis based on a combination of CNN and LSTM model

With the development of social media, there are more and more platforms for people to express their opinions and ideas openly, so there are more and more comments on the same thing and event from different angles on the Internet. Therefore, in order to clean up the internet environment, it is important to root out the source of toxic comments in each other’s anonymous web surfing. In this paper, we proposed a model which combines convolutional neural network (CNN) and long and short-term memory network (LSTM). We used a dataset from Kaggle Toxic comment challenge for model training and testing. Finally, the experimental results that ROC AUC is 0.98 which outperforms CNN and LSTM individually used as a classifier for the task.
Knowledge Extraction: A Few-shot Relation Learning Approach

More than 80% of human civilization information exists in the text. Knowledge extraction aims to obtain structured knowledge from text, a sub-task to build a knowledge graph. Current methods mainly target closed scenarios, but with the growth of knowledge, this will face challenges. Few-shot learning can extract new relations in open scenes. We proposed a bootstrapping method, Topic Model (TS), based on Neural Snowball to improve performance further. Specifically, we designed a new framework and embedded the instance selector tBERT that selects quality sentences. The relation classifier is trained from filtered sentences and a small amount of labeled data to predict new relations. Experiments show that our approach can screen out more diverse sentences for better few-shot relation learning. Moreover, Topic Snowball achieved significant improvement compared to Neural Snowball when the number of seed instances is small. Codes and datasets will be released soon.
Super Size Me: Interface Size, Identity and Embodiment in Digital Musical Instrument Design

Digital interfaces are shrinking, driven by pressures of mass production and consumer culture, and often accompanied by a discourse of control, precision or convenience. Meanwhile, human bodies remain the same size, and the changing size of interfaces has implications for the formation of user identities. Drawing on embodied cognition, effort and entanglement theories of HCI, we explored the impact of interface size on the co-constitution of humans and technology. We designed an oversized digital musical instrument and invited musicians to use the instrument to create original performances. We found that both the performances and the musicians’ self-perception were influenced by the large size of the instrument, shining new light on the ways in which designing technology is designing humans and in turn culture.
Resource-Efficient Continual Learning for Sensor-Based Human Activity Recognition

Recent advances in deep learning have granted unrivaled performance to sensor-based human activity recognition (HAR) . However, in a real-world scenario, the HAR solution is subject to diverse changes over time such as the need to learn new activity classes or variations in the data distribution of the already-included activities. To solve these issues, previous studies have tried to apply directly the continual learning methods borrowed from the computer vision domain, where it is vastly explored. Unfortunately, these methods either lead to surprisingly poor results or demand copious amounts of computational resources, which is infeasible for the low-cost resource-constrained devices utilized in HAR. In this paper, we provide a resource-efficient and high-performance continual learning solution for HAR. It consists of an expandable neural network trained with a replay-based method that utilizes a highly-compressed replay memory whose samples are selected to maximize data variability. Experiments with four open datasets, which were conducted on two distinct microcontrollers, show that our method is capable of achieving substantial accuracy improvements over baselines in continual learning such as Gradient Episodic Memory, while utilizing only one-third of the memory and being up to 3× faster.
Understanding User Experiences Across VR Walking-in-place Locomotion Methods

Navigating large-scale virtual spaces is a major challenge in Virtual Reality (VR) applications due to real-world spatial limitations. Walking-in-place (WIP) locomotion solutions may provide a natural approach for VR use cases that require locomotion to share similar qualities with walking in real-life. However, there is limited knowledge on the range of experiences across common WIP methods to inform the design of usable WIP solutions using consumer-accessible components. This paper contributes to this knowledge via a user study with 40 participants that experienced several easy-to-setup WIP methods in a VR commuting simulation. A nuanced understanding of cybersickness and exertion relationships and walking affordances based on different tracker setups were among the findings derived from a corroborated analysis of think-aloud, interview, and observational data, supplemented with self-reports of VR sickness, presence and flow. Practical design insights were then constructed along the dimensions of cybersickness, affordances, space and user interfaces.
A Game of Dark Patterns: Designing Healthy, Highly-Engaging Mobile Games

Gaming is a more accessible, engaging and popular past-time than ever before. Recent research highlights games as strikingly effective means of capturing and holding our attention — so effective, some argue, to the point of deleterious effect. An impassioned CHI2021 panel discussion directed these efforts towards the ethics and adoption of dark patterns. And yet, we know little as to how dark patterns are perceived and arise in the design, development and use of games. This paper seeks to address this knowledge gap by recounting findings from a design-led inquiry comprising interviews and workshops conducted with mobile game players, designers, developers, and business developers. We contribute an understanding of how dark patterns arise in the development, use and commercialisation of mobile games, their effects on players and industry professionals, and means for the consideration, negotiation and navigation of these strategies for gamer-engagement by design — in support of healthier, highly-engaging game experiences.
Research on hybrid collision detection algorithm based on separation distance

Abstract In order to improve the safety of digital subtraction angiography (DSA) equipment in the treatment of cardiovascular diseases, a hybrid collision detection algorithm based on separation distance is proposed by combining oriented bounding box (OBB) algorithm with GJK algorithm. Firstly, according to the structure of DSA equipment, the real-time position of DSA equipment is simulated in robot operating system (ROS); Then, the OBB is used to envelope the moving parts, and then the generated OBB model is applied to GJK algorithm to calculate the separation distance between the moving parts and quickly eliminate the detection pairs outside the threshold; Finally, for the detection pairs within the threshold, the real model is applied with GJK algorithm to calculate the separation distance between the real models and judge whether the models collide. The experimental results show that the average detection time of the hybrid collision detection algorithm is 15ms. The application of the algorithm in practical equipment has achieved good anti-collision effect.
Network Blockchain Security Sharing Model Based on Fuzzy Logic

In light of the continuous development of Internet technology, many problems involving network security based on the blockchain have also been observed gradually. In this paper, the existence forms of network security hazards based on the blockchain are explored to establish a network blockchain security sharing model based on fuzzy logic. In security sharing, network blockchains are often maintained by many parties. This poses new potential threats and challenges to privacy protection in these multiparty network blockchains. This paper proposes a research scheme for a network blockchain security sharing model based on fuzzy logic. The tampering of the protected network blockchain is prevented by blockchain, and the fuzzy logic algorithm is used to ensure its confidentiality. This solution allows the exchange in the protected network blockchain and the security of transaction information based on the fuzzy logic algorithm. According to the results of the experiments, this method can ensure security sharing of the network blockchain with high practicality and achieve the expected design effect.
Anomaly detection in wearable location trackers for child safety

Child Location trackers are widely available devices but the ability of these trackers to detect outlying incidents remain untapped. The main goal of this research is to incorporate anomaly detection in wearable child location trackers using machine learning, with a primary focus on unsupervised outlier detection techniques. Therefore, a low powered gyroscope-enabled electronic tracker using Low Drop Out (LDO) converters was fabricated as a building block for the software/hardware integration. Common unsupervised techniques were adopted for experimentation including the Local Outlier Factor (LOF), the k-Nearest Neighbor (kNN) Global Anomaly Score and the Histogram-based Outlier Score (HBOS). The results obtained show that LOF performs well in detecting local outliers as well as identifying new patterns to prevent false alarms, however, the paths to the newly identified locations bring about false alarms. The kNN Global Outlier Score is only capable of detecting global outliers where k is greater than 15. HBOS, on the other hand, performs better than the kNN Global Outlier Score but is unable to learn new locations that become part of the normal pattern. The proposed model is a potential candidate for an outlier detection system for combating child loss and kidnapping cases, thereby ensuring child safety globally.
DSRD: deep sparse representation with learnable dictionary for remotely sensed image denoising

Patch-based modelling methods (i.e. sparse coding) had proven their great ability in solving the degradation problem caused by additive white Gaussian noise (AWGN) in remotely sensing images. However, these methods usually pursue surprising performance with sacrificing computational efficiency, and the learned dictionary is universal, resulting in a weak model representation flexibility. To address these issues, this paper proposes a deep sparse representation with learnable dictionary (DSRD) scheme, where the major difference from the previous sparse coding methods is that sparse representation coefficients and dictionaries are both deeply learned, acted as two modular parts to be plugged into the unfolded sparse coding model, to speed up for a stable solution as well as yielding competitive performance. The ablation studies illustrate that our DSRD strategy is efficient for a fixed solution while a flexible and powerful capacity in producing pleasing denoising performance. Meanwhile, the comparisons of quantitatively and qualitatively experimental results further demonstrate that it is effective and can generate enjoyable denoising performance that even outperforms that produced by other state-of-the-arts.
VRySmart: a Framework for Embedding Smart Devices in Virtual Reality

As immersive virtual experiences find their way into our living room entertainment, they are becoming part of our daily technological consumption. However, state-of-the-art virtual reality (VR) remains disconnected from other digital devices in our environment, such as smartphones or tablets. As context switches between acting in the virtual environment and resolving external notifications negatively influence immersion, we look towards integrating smart devices into virtual experiences. To this aim, we present the VRySmart framework. Through either optical marker tracking or simultaneous localization and mapping (SLAM), embedded smart devices can be used as VR controllers with different levels of integration while their content is incorporated into the virtual context to support the plausibility of the illusion. To investigate user impressions, we conducted a study (N = 10) where participants used a smartphone in four different virtual scenarios. Participants positively assessed smart device usage in VR. We conclude by framing implications for future work.
Design of Enterprise Financial Information Management System Based on Blockchain Technology

According to the development of information technology, a large amount of data information has been increasing, especially financial information. There are many acts of fraud, which makes it impossible to trace the data sources of enterprise financial information and relevant personnel. According to the fact that most staff are unable to complete accurate queries on the required financial statements, there are serious problems of nonstandard behavior and lack of key information in the financial statements of these enterprises. Because many comparable challenges have not been handled adequately, it has always made financial information management more complex for businesses, posed a threat to the whole industry's ecosystem, and harmed the interest of many stakeholders, among other things. Due to the qualities of blockchain, such as centralization, anonymity, trustworthiness, and willfulness, which make information difficult to forecast and tamper with, blockchain has gotten a lot of attention in recent years, and bitcoin and other digital currencies have grown popular. Because these characteristics are related to the information storage security and information transmission speed required by enterprise financial information management, blockchain has become a new idea to solve problems related to enterprise financial information management, according to current application and exploration. The overall management system incorporating blockchain technology is created in detail with the financial management system architecture and data processing scheme provided by blockchain technology in order to build an open and secure transmission mechanism for blockchain data. Moreover, it also integrates the encryption technology of the combination of symmetric key and asymmetric key and fast and secure data access control.
A Mind in Intelligent Personal Assistants: An Empirical Study of Mind-Based Anthropomorphism, Fulfilled Motivations, and Exploratory Usage of Intelligent Personal Assistants

Intelligent personal assistants (IPAs) own anthropomorphic features which enable users' perception of anthropomorphism. Adopting the perspective of mind-based anthropomorphism, the purpose of this paper is to investigate how mind-based anthropomorphism influences users' exploratory usage of IPAs. Based on the notion that anthropomorphism can satisfy people's sociality and effectance motivation, we hypothesize that mind-based anthropomorphism can enhance people's social connection with IPAs and IPA self-efficacy, which can in turn influence their exploratory usage of IPAs. Questionnaires were developed and distributed to users who had experience in smart speaker-based IPAs on Wenjuanxing and 551 valid questionnaires were collected to test the research model. The results revealed that cognitive and affective anthropomorphism exerted common and differential impacts on IPA self-efficacy and social connection. Cognitive anthropomorphism versus affective anthropomorphism had stronger influences on IPA self-efficacy, while affective anthropomorphism had stronger impacts on social connection. Both IPA self-efficacy and social connection enhanced users' intentions to explore IPAs. This study enriches previous studies on IPA adoption or post-adoption by investigating exploratory usage which captures how users are deeply engaged with IPAs.
Towards Balancing Real-World Awareness and VR Immersion in Mobile VR

Virtual Reality (VR) can be used to create immersive infotainment experiences for car passengers. However, not much is known about how to best incorporate the essentials of their surroundings for balancing real-world awareness and immersion. To address this gap, we explored 2D and 3D visual cues of the rear-seat space to notify passengers about different real-world tasks (lower armrest, take cup, close window, and hold handle) during a first-person game in VR. Results from our pilot study (n = 19) show that users perceive a lower workload in the task hold handle than all other tasks. They also feel more immersed in VR after completing this task, compared to take cup and close window. Based on our findings, we propose real-world task types, synchronous visual cues, and various input and transition approaches as promising future research directions.
Lattice Menu: A Low-Error Gaze-Based Marking Menu Utilizing Target-Assisted Gaze Gestures on a Lattice of Visual Anchors

We present Lattice Menu, a gaze-based marking menu utilizing a lattice of visual anchors that helps perform accurate gaze pointing for menu item selection. Users who know the location of the desired item can leverage target-assisted gaze gestures for multilevel item selection by looking at visual anchors over the gaze trajectories. Our evaluation showed that Lattice Menu exhibits a considerably low error rate (~1%) and a quick menu selection time (1.3-1.6 s) for expert usage across various menu structures (4 × 4 × 4 and 6 × 6 × 6) and sizes (8, 10 and 12°). In comparison with a traditional gaze-based marking menu that does not utilize visual targets, Lattice Menu showed remarkably (~5 times) fewer menu selection errors for expert usage. In a post-interview, all 12 subjects preferred Lattice Menu, and most subjects (8 out of 12) commented that the provisioning of visual targets facilitated more stable menu selections with reduced eye fatigue.