Social touch is a rich channel of human communication, conveying emotion, intent, and meaning embedded in context. Yet most HCI studies treat touch in isolation, overlooking the layered subtleties that shape interpretation. We present a contextual analysis of 5,016 social touch events, grounded in a large collection of annotated scenes from films, dramas, and documentaries. Using a computer vision pipeline, we segmented touch events from video and annotated them across dimensions, including who is involved, how the gesture is performed, where on the body it occurs, and the cultural backdrop. Our analysis shows that identical gestures can convey distinct meanings depending on body location, relationship type, and context. Similar intentions—like comfort, encouragement, or dominance—may be expressed through different gestures or locations, shaped by relational dynamics, cultural norms, and public or private settings. These insights inform the design of socially aware touch technologies, including avatars, social agents, and mediated communication systems.

Figure 1: UltraEdit with blob interaction.A user is pulling a blob out of a 3D object, editing it, then releasing it.

Low-latency multimodal feedback is essential for maintaining a high-quality user experience in VR; however, unpredictable network conditions can introduce latency that negatively impacts user experience. This work investigates how users perceive multimodal haptic feedback—specifically thermal (hot/cold) and tactile stimuli—and how latency in such feedback affects user experience. We first measured users’ response times for thermal, tactile, and combined thermal-tactile stimuli. Subsequently, we conducted a psychophysical study to identify delay thresholds for each modality by examining temporal congruency between visual and haptic cues. We designed a haptic delay network simulator to emulate a realistic network environment. Results highlighted that combined thermal-tactile feedback has higher latency tolerance than thermal-only feedback, indicating that multimodal integration can buffer the negative effects of latency. Using these thresholds, we designed controlled latency conditions and assessed user experience. Based on our findings, we propose design recommendations for haptic data transmission in networked VR systems.

Modeling what makes an advertisement persuasive, i.e., eliciting the desired response from consumer, is critical to the study of propaganda, social psychology, and marketing. Despite its importance, computational modeling of persuasion in computer vision is still in its infancy, primarily due to the lack of benchmark datasets that can provide persuasion-strategy labels associated with ads. Motivated by persuasion literature in social psychology and marketing, we introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. We then formulate the task of persuasion strategy prediction with multi-modal learning, where we design a multi-task attention fusion model that can leverage other ad-understanding tasks to predict persuasion strategies. The dataset also provides image segmentation masks, which labels persuasion strategies in the corresponding ad images on the test split. We publicly release our code and dataset at https://midas-research.github.io/persuasion-advertisements/.

Microservices are terraforming the computing landscape with web-scale infrastructures (e.g., Facebook, Google, Amazon) and telecom infrastructures (e.g., ATT, Ericsson) adopting them. At it’s core, the microservices paradigm promotes a decoupling of applications into multiple services – a decoupling that promotes better scalability, fault-tolerance, and deployability. Unfortunately, this decoupling significantly increases the space of configuration options and performance problems, rendering traditional approaches to management ineffective. Recent efforts to address this problem embrace Artificial Intelligence for IT Operations (AIOps). However, training effective AI models requires significant amounts of data and, in some instances, a framework for quickly exploring or analyzing model performance. Digital twins, or simulators, have effectively enabled AI-based management frameworks within other domains (e.g., manufacturing, industrial and automotive).

We examine mid-air typing data collected from touch typists to evaluate the features and classification models for recognizing finger stroke. A large number of finger movement traces have been collected using finger motion capture systems, labeled into individual finger strokes, and classified into several key features. We test finger kinematic features, including 3D position, velocity, acceleration, and temporal features, including previous fingers and keys. Based on this analysis, we assess the performance of various classifiers, including Naive Bayes, Random Forest, Support Vector Machines, and Deep Neural Networks, in terms of the accuracy for correctly classifying the keystroke. We finally incorporate a linguistic heuristic to explore the effectiveness of the character prediction model and improve the total accuracy.

This paper presents a new way of data abstraction for visual and haptic representations in immersive analytics using a mid-air haptic display. Visual and haptic abstraction is proposed to transform raw data (wind tunnel data) into another form of data for effective visual and haptic data mapping. Three main features are extracted: (i) Magnitude of Velocity, (ii) Recirculation Region, and (iii) Vorticity. For each feature, visual and haptic abstractions are defined based on data characterization and data reduction. A preliminary study shows a promising direction toward multimodal data interaction in immersive analytics.

This paper presents MetaTwin, a collaborative Metaverse platform that supports one-to-one spatiotemporal synchrony between physical and virtual spaces. The users can interact with other users and surrounding IoT devices without being tied to physical spaces. Resource sharing is implemented to allow users to share media, including presentation slides and music. We deploy MetaTwin in two different network environments (i.e., within the US, Korea-US international) and summarize users’ feedback about the experience.

Software-defined networking (SDN) enables innovative and impressive solutions in the networking domain by decoupling the control plane from the data plane. In an SDN environment, the network control logic for load balancing, routing, and access control is written in software running on a decoupled control plane. As with any software development cycle, the SDN control plane is prone to bugs that impact the network's performance and availability. Yet, as a community, we lack holistic, in-depth studies of bugs within the SDN ecosystem. A bug taxonomy is one of the most promising ways to lay the foundations required for (1) evaluating and directing emerging research directions on fault detection and recovery, and (2) informing operational practices of network administrators. This paper takes the first step towards laying this foundation by providing a comprehensive study and analysis of over 500 `critical' bugs (including ~ 150 with manual analysis) in three of the most widely-used SDN controllers, i.e., FAUCET, ONOS, and CORD. We create a taxonomy of these SDN bugs, analyze their operational impact, and implications for the developers. We use our taxonomy to analyze the effectiveness and coverage of several prominent SDN fault tolerance and diagnosis techniques. This study is the first of its kind in scale and coverage to the best of our knowledge.

In this paper, we investigate the effects of mid-air haptics in interactive 3D data visualization. We build an interactive 3D data visualization tool that adapts hand gestures and mid-air haptics to provide tangible interaction in VR using ultrasound haptic feedback on 3D data visualization. We consider two types of 3D visualization datasets and provide different data encoding methods for haptic representations. Two user experiments are conducted to evaluate the effectiveness of our approach. The first experimental results show that adding a mid-air haptic modality can be beneficial regardless of noise conditions and useful for handling occlusion or discerning density and volume information. The second experiment results further show the strengths and weaknesses of direct touch and indirect touch modes. Our findings can shed light on designing and implementing a tangible interaction on 3D data visualization with mid-air haptic feedback.

Several domains in AI need to represent the relational structure as well as model uncertainty. Markov Logic is a powerful formalism which achieves this by attaching weights to formulas in finite first-order logic. Though Markov Logic Networks (MLNs) have been used for a wide variety of applications, a significant challenge remains that weights do not generalize well when training domain sizes are different from those seen during testing. In particular, it has been observed that marginal probabilities tend to extremes in the limit of increasing domain sizes. As the first contribution of our work, we further characterize the distribution and show that marginal probabilities tend to a constant independent of weights and not always to extremes as was previously observed. As our second contribution, we present a principled solution to this problem by defining Domain-size Aware Markov Logic Networks (DA-MLNs) which can be seen as re-parameterizing the MLNs after taking domain size into consideration. For some simple but representative MLN formulas, we formally prove that probabilities defined by DA-MLNs are well behaved. On a practical side, DA-MLNs allow us to generalize the weights learned over small-sized training data to much larger domains. Experiments on three different benchmark MLNs show that our approach results in significant performance gains compared to existing methods.