Reviewerly/exHarmony huggingface dataset:
Evaluating Large Language Models for Sentence Augmentation in Low-Resource Languages: A Case Study on Kazakh

Large language models (LLMs) have revolutionized natural language processing (NLP) and demonstrated exceptional performance in various NLP tasks for widely spoken languages. However, their efficacy in handling low-resource languages remains an area of concern. This study investigates the performance of LLMs, particularly GPT-3, in sentence augmentation tasks for a low-resource language, Kazakh. We employ a blind peer review methodology, where five native Kazakh annotators assess the quality of LLM-generated augmentations. The results reveal that LLMs excel in popular languages like English, Chinese, and German, but face challenges with low-resource languages due to limited training data. This work sheds light on the importance of improving LLMs' adaptability and relevance to address the unique needs of low-resource languages. Further research could enhance the augmentation capabilities of LLMs in scenarios with limited data sources, ensuring their effectiveness in promoting linguistic diversity and inclusivity. Furthermore, this study underscores the significance of cross-language transfer learning and data collection efforts to empower LLMs in supporting linguistic diversity and fostering inclusivity across the global language landscape.
The Comparative Analysis of Technologies and Software for Single Sign-On

The article provides the review and analysis of software products that implement support for single sign-on technology. Using single sign-on technology, users (in our case, employees of a research institute) move from one information system to another, unrelated to the first system, without re-authentication. The overview and brief description of the single sign-on systems types (enterprise and Web-based) is provided. The technological basis of such information systems is described (SAML language or OpenID standard). A number of currently existing software solutions are listed, grouped in terms of their licenses (proprietary international, proprietary local and free and open source international). The conclusion is drawn about feasibility of using single sign-on system with recommendations for the specific choice. Potential problems and disadvantages of such systems using are also mentioned. The review and analytical parts of the article are supported by links to actual publications on this topic.
Platform (In)Justice: Exploring Research Priorities and Practical Solutions

This SIG calls for a global conversation around platform (in)justice. By focusing on the experiences of people residing in the Majority World (also known as Global South), we aim at creating a space for international and interdisciplinary exchange on socio-economically, politically, and culturally sensitive platform design and operation. The following topics motivate the SIG: concerns about equal access, structural discrimination, global inequities, and the desire to find solutions to those challenges. We invite the CSCW community to explore how attention to power relations, colonial residual, geopolitical tensions, and historical specificities can lead us to more sustainable and just platform designs. Through our SIG, we aspire to shape a research agenda for platform (in)justice that centers best practices and solutions to mediate some of the harms previously identified in the CSCW community. Going beyond this individual event, we will identify strategies of action that center the needs and assets of people residing in the Majority World when it comes to designing, upholding or challenging the frameworks enabling contemporary platforms.
Conceptualizing and Improving Creator Moderation Design with Platform Stakeholders

My doctoral research aims to conceptualize and improve creator moderation design based on (1) content creators’ voice and (2) multiple platform stakeholders’ collaboration. Four of our previous publications in the CSCW conferences have uncovered the socioeconomic impacts of moderation on creators, as well as the fairness and bureaucracy challenges they undertake, implicating the transparency design requirements for addressing these challenges. Two of proposed future studies will pivot into evaluating and refining creator moderation design with different platform stakeholders. First, I will explore how audiences and creators recognize creator moderation's compatibility with platform ecosystem (e.g., monetization, recommendation algorithms). Second, I will organize participatory design workshops with stakeholders such as creators, commercial moderators, and audiences to redesign creator moderation to balance different stakeholders’ stakes and platform's top-down moderation efforts. My dissertation aims to contribute to HCI and CSCW fields through the notion of creator moderation and comprehensive design and policy solutions for a safe and sustaining platform ecosystem.
Invisible Coordination Work: Field Stations as Scientific Caregivers

Field stations are an important part of the global scientific research infrastructure providing access and logistical support that makes scientific contributions across domains from ecology to archeology possible. There are over 1000 field stations worldwide. However, little attention has been given to the cooperative work at field stations or how computer-supported cooperative work could benefit this cooperation. We conducted six ethnographic interviews from August 2022 through October 2022 with staff representing six field stations. The interviews made visible the articulation and coordination work needed for scientific caregiving that field stations do before, during, and after research teams do research at the site. This poster represents a portion of preliminary work in a larger study on cooperative work at field stations.
Leveraging Large Language Model as Support for Human Problem Solving: An Exploration of Its Appropriation and Impact

The emergence of pre-trained Large Language Model (LLM) has opened up new possibilities for people to access language resources at their fingertips. Previously, patterns of language could be difficult to derive from large-scale documents, which impeded people from processing and extracting information contained within. Observations from common users’ practices and experiences suggest that LLM may appear to possess certain capacities for processing, handling and working with not just human language, but also the associated knowledge. However, the original construction of LLM is essentially language-centric, which is not more than a probabilistic model representing and summarizing language patterns from large language corpora, without deliberately incorporating other types of data or information (e.g., user behaviors, domain concepts) into the model construction. Consequently, when using LLM in the real-world, it’s not uncommon to appropriate and re-purpose an LLM for handling tasks that don’t necessarily match what it’s built for. In this poster, we present an exploratory study aimed at understanding how people interact with an LLM, chatGPT, to obtain instructions to work on a problem-solving task, installing Python on a remote computer. The results reveal that users’ literacy and expectations concerning LLM can influence how they perceive and utilize it. Surprisingly, low-literacy participants with limited understanding of LLM appear to benefit more, producing implications for designing user-centric AI/ML tools.
Tools to Support Health and Well-being with Personal Data

Personal data can be instrumental for health and well-being. While it has become easier to obtain such data, realizing its benefits remains challenging. People overburden themselves by collecting too much data yet they may not collect the relevant data, especially as their needs evolve. They cannot usually handle the complexity of the data or they fail to connect the data to their needs. They additionally struggle to translate data insights to actions. My research addresses these challenges through 1) using technology probes around tools that help individuals understand their personal well-being, 2) building data-driven behavior planning systems that facilitate health behavior change, and 3) designing computational methods that characterize and quantify the impact of social adversities on mental and emotional state.
NBGuru: Generating Explorable Data Science Flowcharts to Facilitate Asynchronous Communication in Interdisciplinary Data Science Teams

Data scientists typically work with domain experts in a Data Science (DS) project, resulting in knowledge gaps between roles. Communication holds an immense and difficult workload due to the complicated content, limited meeting time, vast audience backgrounds, etc. Thus, it is almost impossible to build a common ground within the team. Taking a step back, flowcharts and program descriptions have shown to help programmers learn algorithms. However, drawing a flowchart or writing a description takes time and effort. The novel AI-powered search engines can generate elaborate grounded responses with citations. It is then possible to generate flowcharts with text descriptions from code. Therefore, we studied 92 DS flowcharts and 173 code descriptions from top-voted Kaggle notebooks. We propose NBGuru, a flowchart-based communication tool. Users can explore computation steps asynchronously with generated texts and citations. Furthermore, we also discuss the possibility of AI in other collaborative roles.
Personal Objects as Design Materials: A Case Study of Co-Designing Safe Spaces With Young Adults

Design materials are often imposed on users in co-design. This can be problematic as the materials may not be relevant or useful to users’ contexts or goals, or, at worst, could lead to alienation and exclusion. We propose an approach that shifts control of design materials to users, supporting them to select and utilize personal objects in the design process. We conducted a case study alongside a STEM education nonprofit based in Chicago to explore how 9 young adults brought in and engaged with personal objects in the co-design of a “Safe Spaces” event for peers. We found that the personal objects helped inform participants’ designs, structure the conversation, and foster inclusion and common ground. We provide suggestions for how to support users bringing in and using personal objects as design materials.
How People Initiate and Respond to Discussions Around Online Community Norms: A Preliminary Analysis on Meta Stack Overflow Discussions

Supporting norms-related discussions can aid people in understanding and abiding by ambiguous norms in large-scale online communities. Yet, how social and linguistic factors, such as the identities of interlocutors and the language framing of posts, can influence discussions around norms, is underexplored. In this work, we performed a preliminary analysis based on a dataset containing 123 question threads on Meta Stack Overflow, a site for discussions of the workings and policies of Stack Overflow, to understand how people initiate and respond to norms-related discussions. Results revealed that question posts with different levels of personal relatedness and question specificity have significantly different sentiments, and they also draw comments with diverged sentiments. We present implications and directions of future work based on our findings.
Environmental and Climate Justice in Computing

While climate change has been a longstanding concern of HCI and CSCW communities, this scholarship has rarely drawn attention to the well-documented pattern of minoritized and marginalized communities unfairly carrying the brunt of environmental burdens. Through this one-day remote workshop, we plan to critically extend how CSCW can support climate action by focusing on two social movements, environmental and climate justice, both of which aim to reduce environmental degradation and pursue sustainable communities without doing so at the expense of others. In this workshop, we aim to identify how CSCW and datafication have helped to uphold environmental or climate justice commitments or has been complicit in producing or maintaining environmental harms. We also plan to discuss and identify a CSCW research agenda addressing how to support climate justice principles and processes in designing technologies and systems. We hope that this workshop will help to initiate and foster a longer-term relationship with researchers, activists and practitioners who are engaging with or interested in climate justice in computing.
Information Theory-based Wordle Game Word Difficulty Classification and Dynamic Planning Optimization Research

Wordle has gained popularity as a word-guessing game. This study aims to describe the difficulty attributes of target words in Wordle and categorize them based on their difficulty. After two rounds of word guessing, this paper calculated the average mutual information for the third-round correct answers. This data was utilized to build a K-means classification model, categorizing words into three distinct groups: easy, normal, and hard. For instance, the word "EERIE" has an average mutual information of 8.997 bits, categorizing it as a word of 'hard' difficulty. Given the vast number of words to process, the computation time was extensive. To address this, this paper employed dynamic programming, leading to a significant reduction in operation time. Additionally, a Monte Carlo simulation model was established to simulate potential player guessing patterns, validating the classification model's robustness. The model developed in this research offers fresh perspectives on strategy selection in Wordle games and the difficulty assessment of target words. It serves as a dependable guide for game developers when classifying word difficulty.
Data Analysis of the Wordle Game: Insights and Predictive Models Based on Twitter Data

In 2022, the game "Wordle" gained immense popularity worldwide as players faced the challenge of guessing a five-letter word within six attempts, accompanied by feedback. This paper presents an extensive analysis of "Wordle" based on data mined from Twitter during the period from January 7 to December 31, 2022. The primary objective is to explore the game's dynamics and player engagement comprehensively.To achieve this, a sophisticated time-series model was developed to effectively track the fluctuation in the number of players. The model highlights an initial upward trend, reaching its peak on February 3, followed by a gradual decline and eventual stabilization, reflecting the sustained allure of the game. Additionally, this paper leveraged an XGBoost model to predict the distribution of player attempts, exhibiting remarkable accuracy, particularly for attempts ranging from three to six. This research demonstrates the powerful impact of data science in decoding intricate game dynamics and player behavior. Moreover, it emphasizes the fusion of gaming, data analytics, and social media as an exciting frontier for future research. The study's findings provide valuable insights into the gaming community's preferences and the underlying mechanisms that drive user engagement in digital gaming platforms.
Occlusion relationship reasoning with a feature separation and interaction network

Abstract Occlusion relationship reasoning aims to locate where an object occludes others and estimate the depth order of these objects in three-dimensional (3D) space from a two-dimensional (2D) image. The former sub-task demands both the accurate location and the semantic indication of the objects, while the latter sub-task needs the depth order among the objects. Although several insightful studies have been proposed, a key characteristic of occlusion relationship reasoning, i.e., the specialty and complementarity between occlusion boundary detection and occlusion orientation estimation, is rarely discussed. To verify this claim, in this paper, we integrate these properties into a unified end-to-end trainable network, namely the feature separation and interaction network (FSINet). It contains a shared encoder-decoder structure to learn the complementary property between the two sub-tasks, and two separated paths to learn specialized properties of the two sub-tasks. Concretely, the occlusion boundary path contains an image-level cue extractor to capture rich location information of the boundary, a detail-perceived semantic feature extractor, and a contextual correlation extractor to acquire refined semantic features of objects. In addition, a dual-flow cross detector has been customized to alleviate false-positive and false-negative boundaries. For the occlusion orientation estimation path, a scene context learner has been designed to capture the depth order cue around the boundary. In addition, two stripe convolutions are built to judge the depth order between objects. The shared decoder supplies the feature interaction, which plays a key role in exploiting the complementarity of the two paths. Extensive experimental results on the PIOD and BSDS ownership datasets reveal the superior performance of FSINet over state-of-the-art alternatives. Additionally, abundant ablation studies are offered to demonstrate the effectiveness of our design.
An Emotional-Aware Mobile Terminal Accessibility-Assisted Recommendation System for the Elderly Based on Haptic Recognition

For the purpose of improving the user experience of mobile smart terminals for the elderly, accessibility tools have become an indispensable component of intelligent system frameworks to meet the varying needs of senior citizens for assistance. Nonetheless, the complexity of accessibility assistive tools complicates the lives of some elderly with a lack of cognitive experience to use these tools independently. Our purpose is to study the relationship between the accessibility design elements of smartphones, the emotions, and touch behaviors of elderly users, and to optimize the accessibility design methods of mobile smart terminals. First, we obtained the four interface elements with the most influence on users through a Semantic Differential evaluation involving 50 participants. Second, in a touch event collection experiment, 20 participants’ touch data was collected, and an SVM-based correlation model between interface elements, user emotions, and touch behaviors was established. Third, the prototype of the assistive recommender was iterated employing the combination of the correlation model and Genetic Algorithms. Ultimately, the effectiveness of the six touch media recommendations was compared through usability testing. The experimental results indicates that: (1) Emotion-aware accessibility assistive recommendation systems based on haptic recognition can enhance the elderly’s ability to access information through mobile terminals. (2) Middle-aged and elderly users have a more robust negative emotion reflection for dragging and swiping on touch screens. (3) Negative emotions of elderly users can assist identify design defects of accessibility tools. Our research work distilled a set of design suggestions for digital accessibility improvement, thereby enhancing the usability and inclusiveness of assistive tools in diverse contexts, and reducing the psychological pressure caused by unfamiliar interfaces. This study supplies a reference for improving the emotional experience of elderly people employing mobile terminals and extending the approach to accessible design.
MultiVT: Multiple-Task Framework for Dentistry

Current image understanding methods on dental data are often trained end-to-end on inputs and labels, with focus on using state-of-the-art neural architectures. Such approaches, however, typically ignore domain specific peculiarities and lack the ability to generalize outside their training dataset. We observe that, in RGB images, teeth display a weak or unremarkable texture while exhibiting strong boundaries; similarly, in panoramic radiographs root tips and crowns are generally visible, while other parts of the teeth appear blurry. In their respective image type, these features are robust to the domain shift given by different sources or acquisition tools. Therefore, we formulate a method which we call MultiVT, able to leverage these low level image features to achieve results with good domain generalization properties. We demonstrate with experiments that, by focusing on such domain-robust features, we can achieve better segmentation and detection results. Additionally, MultiVT improves generalization capabilities without applying domain adaptive techniques - a characteristic which renders our method suitable for use in real-world applications.
Design and Analysis of an Expert System for the Detection and Recognition of Criminal Faces

The process of identifying a person using their facial traits is referred to as face recognition, and it is a form of biometric identification. The use of facial recognition might range from that of an entertainment tool to one of a security tool. Even while other forms of biometric identification, such as fingerprints and iris scans, are reliable, they require the active participation of an individual. As a result, criminals cannot rely on them as the most reliable means of verification. When a criminal database, which stores the individual details of a criminal, and facial recognition technology are brought together, it can identify a criminal who is depicted in an image or seen in a video feed. Not only does a criminal recognition system needs to have a high level of accuracy, but it also needs to be able to adapt to significant changes in lighting, occlusion, aging, expressions, and other factors. In this study, they were analyzed and compared with the many methods of face detection and face recognition, such as HAAR cascades, local binary patterns histogram, support vector machines, convolutional neural networks, and ResNet-34. These methods include a variety of different approaches to recognizing faces. An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. In addition to that, a variety of uses of this criminal recognition in the real world are also discussed.
Characterizing information access needs in gaze-adaptive augmented reality interfaces: implications for fast-paced and dynamic usage contexts

Gaze-adaptive interfaces can enable intuitive hands-free augmented reality (AR) interaction but unintentional selection (i.e. “Midas Touch”) can have serious consequences during high-stakes real-world AR use. In the present study, we assessed how simulated gaze-adaptive AR interfaces, implementing single and dual gaze inputs, influence Soldiers’ human performance and user experience (UX) in a fast-paced virtual reality marksmanship task. In Experiment 1, we investigated 1- and 2-stage dwell-based interfaces, finding confirmatory dual gaze dwell input effectively reduced Midas Touch but also reduced task performance and UX compared to an always-on (AO) interface. In Experiment 2, we investigated gaze depth-based interfaces, finding similar negative impacts of confirmatory dwell on Midas Touch, task performance, and UX. Overall, compared to the AO interface, single gaze input interfaces (e.g. single dwell or gaze depth threshold) reduced viewing of task-irrelevant information and yielded similar task performance and UX despite being prone to Midas Touch. Broadly, our findings demonstrate that AR users performing fast-paced dynamic tasks can tolerate some unintentional activation of AR displays if reliable and rapid information access is maintained and point to the need to develop and refine gaze depth estimation algorithms and novel gaze depth-based interfaces that provide rapid access to AR display content.