Reviewerly/exHarmony huggingface dataset:
Towards Enabling Synchronous Digital Creative Collaboration: Codifying Conflicts in Co-Coloring

Humans are unique in working collaboratively by sharing and understanding intentions. However, digital collaboration is daunting, especially in creative design life cycles, due to non-linear workflows and lack of micro-alignments coupled with the need for robust network connectivity. We present a formative study with creatives to identify key themes in conflicts that arise in this space. We introduce CollabColor, a user interface that aids in resolving conflicts for two users synchronously collaborating on a low-touch creative task. More specifically, given an uncolored line-art on a canvas and a set of reference images from the users as input, we arrive at design goals for an intelligent system that can enhance our interface. We find that such a system must provide non-obtrusive interventions during real-time collaboration to ensure that the final colorization of the art is coherent, and all the users’ aligned preferences are incorporated.
Drawing Out the Everyday Hyper-[In]Securities of Digital Identity

In a study of everyday digital identity, a set of primary drawings were made by researchers in online focus group settings as a way to capture our participants’ spoken narratives of hyper-[in]security in the usages of digital identity. In a second stage of work, key extracts from the drawings were collaged using the method described in the paper, allowing an exploratory qualitative cartography of hyper-[in]security to be constructed. These secondary collages group the [in]securities thematically without losing essential contextual information. Samples of our data are given, to illustrate the contribution of the method to experience-centred design, with special reference to security from the perspective of marginalised and underserved communities. We discuss our method as a step towards multidimensional cognitive mapping of the salient features of our participants’ narratives about hyper-[in]security, potentially paving the way for further world building explorations of digital identity futures.
Relative generalized Hamming weights of evaluation codes

This work aims to algebraically describe the relative generalized Hamming weights of evaluation codes. We give a lower bound for these weights in terms of a footprint bound, and prove that this bound can be sharp. We compute the next-to-minimal weight of toric codes over hypersimplices of degree 1.
Virfie: Virtual Group Selfie Station for Remote Togetherness

Selfies have become a prominent means of online communication. Group selfies, in particular, encourage people to represent their identity as part of the group and foster a sense of belonging. During the COVID-19 pandemic, video conferencing systems are used as a tool for group selfies. However, conventional systems are not ideal for group selfies due to the rigidness of grid-based layout, information overload, and lack of eye contact. To explore design opportunities and needs for a novel virtual group selfie platform, we conducted a participatory design, and identified three characteristics of virtual group selfie scenarios, "context with narratives", "interactive group tasks", and "capturing subtle moments." We implemented Virfie, a web-based platform that enables users to take group selfies with embodied social interaction, and to create and customize selfie scenarios using a novel JSON specification. In order to validate our design concept and identify usability issues we conducted a user study. Feedbacks from the study participants suggest that Virfie is effective at strengthening social interaction and remote togetherness.
Understanding User Mental Models Through App Sketches from Memory

User-generated sketches serve as a powerful lens for understanding users’ mental models of apps and services. Inspired by research on hand-drawn maps of cities, we conducted an exploratory study in which 123 participants drew the interface of a popular mobile app from memory. An analysis of the resulting sketches highlights that these types of artifacts can be used to understand which elements are most central to users’ understanding of the app's core features and architecture—including non-existent features from other apps and services. We discuss how this work can be applied to the design of mobile apps and describe areas of future research.
Long-form text matching with word vector clustering and graph convolution

In many Natural Language Processing tasks such as information retrieval, recommendation system and network public opinion, text matching is indispensable. BERT and traditional text matching methods have many disadvantages in dealing with large text tasks. In order to better realize long-form text matching tasks, aiming at the problem of low text decomposition accuracy of Concept Interaction Graph (CIG) model in long-form text matching tasks, this paper proposes an improved CIG long-form text matching model. In this model, the word vector clustering results are introduced as the filler words of keywords, and the text decomposition accuracy is improved by using multiple words as the decomposition basis. At the same time, a feature extraction method (GRU-key) integrating keyword information into local features is proposed, which enhances the extraction ability of local features, so as to realize the matching of long-form texts. The experimental results show that compared with the current popular BERT pre-training model, SBERT model and CIG model, the method of filling keywords with word vector clustering results and adding GRU-key to extract local features has achieved better results in accuracy and FI evaluation index, and the FI values on CNSE and CNSS data sets have reached 84.51% and 91.20% respectively.
Research on knowledge base question and answer methods based on the joint subgraph structure of interrogative features

Existing knowledge base question and answer methods rarely consider the influence of question sentences and candidate subgraph structure information when performing entity representation. This paper proposes a knowledge base question and answer method based on subgraph structure fusion question perception to address these issues. Firstly, the question is transformed into a semantic quantity by the pre-training model RoBERTa; secondly, the subgraphs of the candidate answer sets are extracted from the knowledge base based on the subject entities in the question, and the structural representation of the candidate subgraphs is enhanced by the attention calculation based on the query information to achieve question perception; secondly, the entity representation is pre-trained based on the background knowledge base and fused with the corresponding structural representation. Finally, the candidate answers are scored based on the fused vectors, and the entity with the highest score is taken as the answer. Comparative tests were conducted on the WebQuestionsSP dataset, and the experimental results showed that the proposed model outperformed other benchmark models.
Fast AUC Maximization Learning Machine With Simultaneous Outlier Detection

While AUC maximizing support vector machine (AUCSVM) has been developed to solve imbalanced classification tasks, its huge computational burden will make AUCSVM become impracticable and even computationally forbidden for medium or large-scale imbalanced data. In addition, minority class sometimes means extremely important information for users or is corrupted by noises and/or outliers in practical application scenarios such as medical diagnosis, which actually inspires us to generalize the AUC concept to reflect such importance or upper bound of noises or outliers. In order to address these issues, by means of both the generalized AUC metric and the core vector machine (CVM) technique, a fast AUC maximizing learning machine, called ρ -AUCCVM, with simultaneous outlier detection is proposed in this study. ρ -AUCCVM has its notorious merits: 1) it indeed shares the CVM's advantage, that is, asymptotically linear time complexity with respect to the total number of sample pairs, together with space complexity independent on the total number of sample pairs and 2) it can automatically determine the importance of the minority class (assuming no noise) or the upper bound of noises or outliers. Extensive experimental results about benchmarking imbalanced datasets verify the above advantages of ρ -AUCCVM.
Text similarity calculation method of Siamese network based on ALBERT

Text similarity calculation based on deep learning has always been an important research in the field of natural language processing. However, the traditional deep learning model has some disadvantages in the application of text similarity calculation, Text similarity calculation based on deep learning has always been an important research in the field of natural language processing. However, traditional deep learning models have drawbacks in the application of text similarity calculation, such as insufficient extraction of text semantics, inability to combine context, and inability to Understand polysemy and so on. In order to solve the problems of existing text similarity calculation methods based on deep learning, this paper proposes a Siamese network based on ALBERT, uses ALBERT model for word embedding, and proposed the ABSBGRU model by combining Bi-GRU of Siamese structure with attention mechanism. On the premise of minimizing the increased computational cost, we can extract the deep semantic information better. The experimental results show that ABSBGRU model has stronger deep semantic extraction ability. Compared with other traditional models, F1 Score is higher and training cost less than some other models.
A construction method of a large-scale physical rendering 3D semantic segmentation dataset

With the further development of 3D technology, it provides more convenience for us to use the 3D information of stereo images to perform semantic segmentation and disparity estimation, but there is still a lack of large-scale indoor datasets for semantic segmentation and disparity estimation from stereo images. In this paper, we propose a large-scale synthetic dataset with ground truth, which can be used for the training of deep models. The generation of the dataset fully considers the characteristics of the actual robot navigation, and is rendered from 331 trajectories formed by 222778 poses estimated by a navigation algorithm. The temporal nature of the dataset is suitable for continuous tasks such as robotic state estimation and object tracking. We use the advanced semantic segmentation and disparity estimation methods to evaluate the data set, and the results show that it has good effect and applicability.
SignFind: A Synchronized Sign Language and Chinese Character Teaching Game for Chinese Deaf Children Using Gesture Recognition

This paper presents a targeted system to help Chinese deaf children learn both sign language and Chinese characters in early language learning. The system combines sign recognition and in-air writing techniques with games so that children can practice sign language and Chinese character writing at the same time. Sign recognition is used to assess the accuracy of sign language and in-air writing records the process of writing Chinese characters. In addition, the game adds to the learning fun and makes children more willing to complete learning tasks. We have developed a prototype to evaluate the effectiveness of a simultaneous Chinese sign language and Chinese character teaching system based on gesture recognition and in-air writing. We expect that this system will increase children's willingness and efficiency to learn sign language and Chinese characters, and eventually be used to assist in early language education for deaf children in the Chinese region.
ESsense - An Electrostatic Sensor System for Simple, Contactless Motion Control in Games

We showcase ESsense, a system of electrostatic (ES) sensors for alternative controllers, and its potential as a low-cost, easily-assembled motion detection system, to enable new interactions otherwise inaccessible to DIY controller makers. We characterize their most suitable interactions and demonstrate their ability to remap game inputs and recontextualise existing games with motion detection. The custom motion controllers provide button-based games with greater immersion than their original inputs, requiring motion that corresponds intuitively to the game’s displayed actions and reactions. We also developed a game demo that showcases the potential of this technology to create new experiences. It gives the player the role of a music conductor, allowing them to control the tempo of a music track by swinging a baton in front of a conductor’s stand. Given its ability to detect motion of any kind, we also discuss other possibilities for the system, such as in contexts beyond games.
Light-In-Light-Out (Li-Lo) Displays: Harvesting and Manipulating Light to Provide Novel Forms of Communication

Many of us daily encounter shadow and reflected light patterns alongside macro-level changes in ambient light levels. These are caused by elements—opaque objects, glass, mirrors, even clouds—in our environment interfacing with sunlight or artificial indoor lighting. Inspired by these phenomena, we explored ways of creating digitally-supported displays that use light, shade and reflection for output and harness the energy they need to operate from the sun or indoor ambient light. Through a set of design workshops we developed exemplar devices: SolarPix, ShadMo and GlowBoard. We detail their function and implementation, as well as evidencing their technical viability. The designs were informed by material understandings from the Global North and Global South and demonstrated in a cross-cultural workshop run in parallel in India and South Africa where community co-designers reflected on their uses and value given lived experience of their communication practices and unreliable energy networks.
xai-primer.com — A Visual Ideation Space of Interactive Explainers

demonstration Share on xai-primer.com — A Visual Ideation Space of Interactive Explainers Authors: Beatrice Gobbo School of Design, Politecnico di Milano, Italy School of Design, Politecnico di Milano, ItalyView Profile , Tommaso Elli Dipartimento di Design, Politecnico di Milano, Italy Dipartimento di Design, Politecnico di Milano, ItalyView Profile , Uta Hinrichs School of Informatics, University of Edinburgh, United Kingdom School of Informatics, University of Edinburgh, United KingdomView Profile , Mennatallah El-Assady AI Center, ETH Zürich, Switzerland AI Center, ETH Zürich, SwitzerlandView Profile Authors Info & Claims CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing SystemsApril 2022 Article No.: 193Pages 1–4https://doi.org/10.1145/3491101.3519880Online:28 April 2022Publication History 0citation70DownloadsMetricsTotal Citations0Total Downloads70Last 12 Months70Last 6 weeks14 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
Distinguishable keypoint detection and matching for optical satellite images with deep convolutional neural networks

As a required step in optical remote sensing applications, image matching identifies correspondences to estimate the relationship between two images. To address this task, feature-based algorithms, such as Scale-Invariant Feature Transform (SIFT), use detectors to identify keypoints and then apply descriptors to represent these keypoints as feature vectors. Thereby, these vectors from different images are matched by Euclidean distance to produce matching points as correspondences. Deep learning networks are widely used in the design of detectors and descriptors due to their powerful representation capabilities. However, previous methods oriented to repeatability tend to be of low localization accuracy. To solve this problem, we define optimal keypoints as “detected, repeatable, reasonable, and distinguishable” (DRRD). Based on this idea, we introduce a DRRD framework for remote sensing optical image matching. Specifically, we designed a deep-learning-based descriptor network which is trained in a self-supervised manner with a novel adaptive mix content triplet (AMCT) loss function. We established a training procedure for remote sensing images using a multi-spectral image dataset. The proposed method obtained 1694 correct correspondences from 8192 detected keypoints in multi-temporal image matching tasks and a sub-pixel accuracy in the image rectification experiments. Comparison experiments show that the proposed DRRD framework outperforms other state-of-the-art methods.
Janus Screen: Screen with Switchable Projection Surfaces Using Wire Grid Polarizer

In this paper, we present a novel screen system employing polarizers that allow switching of the projection surface to the front, rear, or both sides using only two projectors on one side. In this system, we propose a method that employs two projectors equipped with polarizers and a multi-layered screen comprising an anti-reflective plate, transparent screen, and wire grid polarizer. The multi-layered screen changes whether the projected image is shown on the front or rear side of the screen depending on the polarization direction of the incident light. Hence, the proposed method can project images on the front, rear, or both sides of the screen by projecting images from either or both projectors using polarizers. In addition, the proposed method can be easily deployed by simply attaching multiple optical films. We implement a prototype and confirm that the proposed method can selectively switch the projection surface.
Feasibility Studies in Indoor Localization through Intelligent Conversation

We propose a model to achieve human localization in indoor environments through intelligent conversation between users and an agent. We investigated the feasibility of conversational localization by conducting two studies. First, we conducted a Wizard-of-Oz study with N = 7 participants and studied the feasibility of localizing users through conversation. We identified challenges posed by users’ language and behavior. Second, we collected N = 800 user descriptions of virtual indoor locations from N = 80 Amazon Mechanical Turk participants to analyze user language. We explored the effects of conversational agent behavior and observed that people describe indoor locations differently based on how the agent presents itself. We devise “Entity Suitability Scale,” a concrete and scalable approach to obtain information to support localization from the myriad of indoor entities users mention in their descriptions. Through this study, we lay foundation to our proposed paradigm of conversational localization.
Effects of Speaking Rate on Speech and Silent Speech Recognition

Speaking rate or the speed at which a person speaks is a fundamental user characteristic. This work investigates the rate in which users speak when interacting with speech and silent speech-based methods. Results revealed that native users speak about 8% faster than non-native users, but both groups slow down at comparable rates (34–40%) when interacting with these methods, mostly to increase their accuracy rates. A follow-up experiment confirms that slowing down does improve the accuracy of these methods. Both methods yield the best accuracy rates when speaking at 0.75x of the actual speaking rate. A post-hoc error analysis revealed that speech and silent speech methods and native and non-native speakers are susceptible to different types of errors.
“Tête-à-tête 22”: Mediating Intimate Relationship through Expressive Digital Interaction

The interactive installation “Tête-à-tête 22” is a conversational sitting object to explore the intimate dependency in relationships that are difficult to achieve remotely. Using a pair of kinetic chairs that gradually become unstable, the installation requires its seatmates work as a team to maintain their balance. The creative technology used in “Tête-à-tête 22” facilitates human-human connections by evoking a sensual experience in intriguing, reflective and expressive ways. Ultimately, the strenuous physical interaction aims to produce a sense of togetherness in everyday life, by constructing a digital entity with a symbolic meaning, an experiential possibility, and a functional role.
SEACHI 2022 Symposium

Southeast Asia that consists of eleven countries, has been proud of its way of life and rich culture and is generally happy to maintain its long comforting tradition. However, the region cannot deny that its diverse population and strategic location have become a center of attention for global players to invest in the region. With the emergence of Industry 4.0, digital transformation has become mandatory for any organizations or nations in Southeast Asia to consider. Through SEACHI (Southeast Asian CHI) Symposium, we aim to grow awareness in HCI and UX to improve the design and development of technology for a living and bring together the Southeast Asian academic researchers and industry practitioners. As HCI is maturing in Asia, we identified the remarkable growth and needs of HCI in the Southeast Asian community. In this symposium, we have several questions that we would like to answer: To what extent the HCI and UX that has been taught and practiced in Southeast Asia met the needs to support the digital transformation initiatives in the region; whether there has been any significant and proper contextualization of the HCI and UX fields; whether HCI and UX are still perceived as a Western mindset instead of a localized approach to make a difference in any projects; whether HCI and UX have become a standard norm in the digital product and design process and how HCI and UX players in Southeast Asia have worked together to create a unique ecosystem. Under the big conference theme "Equity, Justice and Access Commitments," the symposium aims to bring about equal and fair access for anyone to exchange information and transfer knowledge in this multidisciplinary environment and multi-socioeconomic aspects of research and practice HCI and UX in Southeast Asia.
On Appropriation and Nostalgic Reminiscence of Technology

Technological objects present themselves as necessary, only to become obsolete faster than ever before. This phenomenon has led to a population that experiences a plethora of technological objects and interfaces as they age, which become associated with certain stages of life and disappear thereafter. Noting the expanding body of literature within HCI about appropriation, our work pinpoints an area that needs more attention, “outdated technologies.” In other words, we assert that design practices can profit as much from imaginaries of the future as they can from reassessing artefacts from the past in a critical way. In a two-week fieldwork with 37 HCI students, we gathered an international collection of nostalgic devices from 14 different countries to investigate what memories people still have of older technologies and the ways in which these memories reveal normative and accidental use of technological objects. We found that participants primarily remembered older technologies with positive connotations and shared memories of how they had adapted and appropriated these technologies, rather than normative uses. We refer to this phenomenon as nostalgic reminiscence. In the future, we would like to develop this concept further by discussing how nostalgic reminiscence can be operationalized to stimulate speculative design in the present.