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High-Efficiency Split Computing for Cooperative Edge Systems: A Novel Compressed Sensing Bottleneck arxiv.org/abs/2504.15295 .DC

High-Efficiency Split Computing for Cooperative Edge Systems: A Novel Compressed Sensing Bottleneck

The advent of big data and AI has precipitated a demand for computational frameworks that ensure real-time performance, accuracy, and privacy. While edge computing mitigates latency and privacy concerns, its scalability is constrained by the resources of edge devices, thus prompting the adoption of split computing (SC) addresses these limitations. However, SC faces challenges in (1) efficient data transmission under bandwidth constraints and (2) balancing accuracy with real-time performance. To tackle these challenges, we propose a novel split computing architecture inspired by compressed sensing (CS) theory. At its core is the High-Efficiency Compressed Sensing Bottleneck (HECS-B), which incorporates an efficient compressed sensing autoencoder into the shallow layer of a deep neural network (DNN) to create a bottleneck layer using the knowledge distillation method. This bottleneck splits the DNN into a distributed model while efficiently compressing intermediate feature data, preserving critical information for seamless reconstruction in the cloud. Through rigorous theoretical analysis and extensive experimental validation in both simulated and real-world settings, we demonstrate the effectiveness of the proposed approach. Compared to state-of-the-art methods, our architecture reduces bandwidth utilization by 50%, maintains high accuracy, and achieves a 60% speed-up in computational efficiency. The results highlight significant improvements in bandwidth efficiency, processing speed, and model accuracy, underscoring the potential of HECS-B to bridge the gap between resource-constrained edge devices and computationally intensive cloud services.

arXiv.org

D$^{2}$MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving arxiv.org/abs/2504.15299 .DC .AI

D$^{2}$MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving

The mixture of experts (MoE) model is a sparse variant of large language models (LLMs), designed to hold a better balance between intelligent capability and computational overhead. Despite its benefits, MoE is still too expensive to deploy on resource-constrained edge devices, especially with the demands of on-device inference services. Recent research efforts often apply model compression techniques, such as quantization, pruning and merging, to restrict MoE complexity. Unfortunately, due to their predefined static model optimization strategies, they cannot always achieve the desired quality-overhead trade-off when handling multiple requests, finally degrading the on-device quality of service. These limitations motivate us to propose the D$^2$MoE, an algorithm-system co-design framework that matches diverse task requirements by dynamically allocating the most proper bit-width to each expert. Specifically, inspired by the nested structure of matryoshka dolls, we propose the matryoshka weight quantization (MWQ) to progressively compress expert weights in a bit-nested manner and reduce the required runtime memory. On top of it, we further optimize the I/O-computation pipeline and design a heuristic scheduling algorithm following our hottest-expert-bit-first (HEBF) principle, which maximizes the expert parallelism between I/O and computation queue under constrained memory budgets, thus significantly reducing the idle temporal bubbles waiting for the experts to load. Evaluations on real edge devices show that D$^2$MoE improves the overall inference throughput by up to 1.39$\times$ and reduces the peak memory footprint by up to 53% over the latest on-device inference frameworks, while still preserving comparable serving accuracy as its INT8 counterparts.

arXiv.org

The Model Counting Competitions 2021-2023 arxiv.org/abs/2504.13842 .AI .DS .LO

The Model Counting Competitions 2021-2023

Modern society is full of computational challenges that rely on probabilistic reasoning, statistics, and combinatorics. Interestingly, many of these questions can be formulated by encoding them into propositional formulas and then asking for its number of models. With a growing interest in practical problem-solving for tasks that involve model counting, the community established the Model Counting (MC) Competition in fall of 2019 with its first iteration in 2020. The competition aims at advancing applications, identifying challenging benchmarks, fostering new solver development, and enhancing existing solvers for model counting problems and their variants. The first iteration, brought together various researchers, identified challenges, and inspired numerous new applications. In this paper, we present a comprehensive overview of the 2021-2023 iterations of the Model Counting Competition. We detail its execution and outcomes. The competition comprised four tracks, each focusing on a different variant of the model counting problem. The first track centered on the model counting problem (MC), which seeks the count of models for a given propositional formula. The second track challenged developers to submit programs capable of solving the weighted model counting problem (WMC). The third track was dedicated to projected model counting (PMC). Finally, we initiated a track that combined projected and weighted model counting (PWMC). The competition continued with a high level of participation, with seven to nine solvers submitted in various different version and based on quite diverging techniques.

arXiv.org

Interactions par franchissement gr\^ace a un syst\`eme de suivi du regard arxiv.org/abs/2504.13844 .HC

Interactions par franchissement grâce a un système de suivi du regard

Human-computer interactions based on gaze-tracking have spread during the last few years. Video games, applications in health, trading, market research, and many other fields have started to use this new technology that seems invisible to the user. However, the dominant form of interaction using gaze tracking uses dwell-time for command activation, which introduces strong constraints in the interaction: dwell-time activation requires users to look steadily at an element for a predefined amount of time in to select it. While dwell-time alleviates a part of the Midas touch problem (referring to the fact that an element fixed by the user will be activated even if it was not intended to do so), it doesn't completely remove it: users should not gaze too long on an item, or they may trigger an unintended activation. In addition, dwell-time slows down users' interaction by requiring a pause each time an activation is needed. In this project, we study an alternative selection method based on crossing interactions, a well-studied method used in conventional HCI. This interaction allows users' gaze to rest in areas that don't have crossing triggers, and it removes the need to pause in the interaction. We found that crossing interaction had similar performances than dwell-time interaction with novice users. The performance was even better for users having previous experience with gaze interaction.

arXiv.org

Towards Enhanced Learning through Presence: A Systematic Review of Presence in Virtual Reality Across Tasks and Disciplines arxiv.org/abs/2504.13845 .HC

Towards Enhanced Learning through Presence: A Systematic Review of Presence in Virtual Reality Across Tasks and Disciplines

The rising interest in Virtual Reality (VR) technology has sparked a desire to create immersive learning platforms capable of handling various tasks across environments. Through immersive interfaces, users can engage deeply with virtual environments, enhancing both learning outcomes and task performance. In fields such as education, engineering, and collaboration, presence has emerged as a critical factor influencing user engagement, motivation, and skill mastery. This review provides a comprehensive examination of the role of presence across different tasks and disciplines, exploring how its design impacts learning outcomes. Using a systematic search strategy based on the PRISMA method, we screened 2,793 articles and included 78 studies that met our inclusion criteria. We conducted a detailed classification and analysis of different types of presence in VR environments, including spatial presence, social presence, co-presence, self-presence, and cognitive presence. This review emphasizes how these varied types of presence affect learning outcomes across tasks and fields, and examines how design elements and interaction techniques shape presence and subsequently impact learning outcomes. We also summarize trends and future directions, identifying research gaps and opportunities to improve learning outcomes by enhancing presence in VR environments, thus offering guidance and insight for future research on VR presence and learning effectiveness.

arXiv.org

Interview AI-ssistant: Designing for Real-Time Human-AI Collaboration in Interview Preparation and Execution arxiv.org/abs/2504.13847 .HC .CL

Interview AI-ssistant: Designing for Real-Time Human-AI Collaboration in Interview Preparation and Execution

Recent advances in large language models (LLMs) offer unprecedented opportunities to enhance human-AI collaboration in qualitative research methods, including interviews. While interviews are highly valued for gathering deep, contextualized insights, interviewers often face significant cognitive challenges, such as real-time information processing, question adaptation, and rapport maintenance. My doctoral research introduces Interview AI-ssistant, a system designed for real-time interviewer-AI collaboration during both the preparation and execution phases. Through four interconnected studies, this research investigates the design of effective human-AI collaboration in interviewing contexts, beginning with a formative study of interviewers' needs, followed by a prototype development study focused on AI-assisted interview preparation, an experimental evaluation of real-time AI assistance during interviews, and a field study deploying the system in a real-world research setting. Beyond informing practical implementations of intelligent interview support systems, this work contributes to the Intelligent User Interfaces (IUI) community by advancing the understanding of human-AI collaborative interfaces in complex social tasks and establishing design guidelines for AI-enhanced qualitative research tools.

arXiv.org

Assistive XR research for disability at ACM ASSETS: A Scoping Review arxiv.org/abs/2504.13849 .HC

Assistive XR research for disability at ACM ASSETS: A Scoping Review

Despite the rise in affordable eXtended Reality (XR) technologies, accessibility still remains a key concern, often excluding people with disabilities from accessing these immersive XR platforms. Consequently, there has been a notable surge in HCI research on creating accessible XR solutions (also known as, assistive XR). This increased focus in assistive XR research is also reflected in the number of research and innovative solutions submitted at the ACM Conference on Accessible Computing (ASSETS), with an aim to make XR experiences inclusive for disabled communities. However, till date, there is little to no work that provides a comprehensive overview of state-of-the-art research in assistive XR for disability at ACM ASSETS, a premier conference dedicated for research in HCI for people with disabilities. This study aims to fill this research gap by conducting a scoping review of literature delineating the key focus areas, research methods, statistical and temporal trends in XR research for disability at ACM ASSETS (2019-2023). From a pool of 1595 articles submitted to ASSETS, 26 articles are identified that specifically focus on XR research for disability. Through a detailed analysis, 6 key focus areas of XR research explored at ACM ASSETS are identified and a detailed examination of each is provided. Additionally, an overview of multiple research methods employed for XR research at ASSETS is also presented. Lastly, this work reports on the statistics and temporal trends regarding the number of publications, XR technologies used, disabilities addressed, and methodologies adopted for assistive XR research at ASSETS, highlighting emerging trends and possible future research directions.

arXiv.org

Factors That Influence the Adoption of AI-enabled Conversational Agents (AICAs) as an Augmenting Therapeutic Tool by Frontline Healthcare Workers: From Technology Acceptance Model 3 (TAM3) Lens -- A Systematic Mapping Review arxiv.org/abs/2504.13183 .HC .AI

Factors That Influence the Adoption of AI-enabled Conversational Agents (AICAs) as an Augmenting Therapeutic Tool by Frontline Healthcare Workers: From Technology Acceptance Model 3 (TAM3) Lens -- A Systematic Mapping Review

Artificial intelligent (AI) conversational agents hold a promising future in the field of mental health, especially in helping marginalized communities that lack access to mental health support services. It is tempting to have a 24/7 mental health companion that can be accessed anywhere using mobile phones to provide therapist-like advice. Yet, caution should be taken, and studies around their feasibility need to be surveyed. Before adopting such a rapidly changing technology, studies on its feasibility should be explored, summarized, and synthesized to gain a solid understanding of the status quo and to enable us to build a framework that can guide us throughout the development and deployment processes. Different perspectives must be considered when investigating the feasibility of AI conversational agents, including the mental healthcare professional perspective. The literature can provide insights into their perspectives in terms of opportunities, concerns, and implications. Mental health professionals, the subject-matter experts in this field, have their points of view that should be understood and considered. This systematic literature review will explore mental health practitioners' attitudes toward AI conversational agents and the factors that affect their adoption and recommendation of the technology to augment their services and treatments. The TAM3 Framework will be the lens through which this systematic literature review will be conducted.

arXiv.org
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