We present Samten, a robotic desktop companion inspired by the Tibetan ritual of stacking Mani stones. In an era of pervasive digital distractions, Samten addresses the challenge of sustained focus by integrating cultural metaphors with tangible interaction. Composed of three modular stones with distinct textures and shapes, Samten operationalizes the abstract concept of "focus" into a tangible structure requiring balance. The system monitors user engagement and provides subtle multi-sensory feedback (vibration, light, and stone movement) when attention drifts. If distraction persists, the stack physically collapses, serving not as a penalty, but as a signal of disrupted workflow. The subsequent act of restacking the stones functions as a mindful ritual to reset cognitive load. A user study (N=12) suggests that this tangible intervention effectively transforms the negative experience of distraction into a moment of mindful restoration, fostering a "Zen-like" state for deep work.

Link prediction is widely used in various fields to predict the missing or potential future links between node pairs in the network. Knowledge distillation (KD) methods have been introduced for the link prediction task, demonstrating exceptional performance in inference acceleration. However, these methods primarily focus on supervised learning settings that rely on high-quality labels, and they fail to capture the rich structural features inherent in graph data within the teacher model effectively, resulting in suboptimal performance. To address the problem of reducing label dependency, we propose a link prediction method via contrastive learning with relational distillation (LCLRD) that aims to improve model performance under label scarcity. LCLRD incorporates contrastive learning into the teacher model for pretraining, enabling the teacher model to obtain high-quality node representations more effectively. Then, graph structural information is extracted from the teacher model via relational distillation and transferred to the student model. In addition, we leverage the Pearson correlation coefficient (PCC) as a new matching strategy to replace the Kullback–Leibler (KL) divergence, aiming to alleviate the prediction discrepancy between the student and the stronger teacher model. The experimental results show that LCLRD significantly outperforms other baseline methods on 12 datasets, demonstrating the superiority of this approach.

One of the key challenges faced by autistic children is understanding social affordances in complex environments, which further impacts their ability to respond appropriately to social signals. In traffic scenarios, this impairment can even lead to safety concerns. In this paper, we introduce an LLM-simulated immersive projection environment designed to improve this ability in autistic children while ensuring their safety. We first propose 17 design considerations across four major categories, derived from a comprehensive review of previous research. Next, we developed a system called AIroad, which leverages LLMs to simulate drivers with varying social intents, expressed through explicit multimodal social signals. AIroad helps autistic children bridge the gap in recognizing the intentions behind behaviors and learning appropriate responses through various stimuli. A user study involving 14 participants demonstrated that this technology effectively engages autistic children and leads to significant improvements in their comprehension of social affordances in traffic scenarios. Additionally, parents reported high perceived usability of the system. These findings highlight the potential of combining LLM technology with immersive environments for the functional rehabilitation of autistic children in the future.

As research in rehabilitation therapy delves deeper into the exploration of serious games in virtual environments, there is increasing attention on Virtual Reality (VR) serious games, particularly in multiplayer gaming (i.e., patient and therapist) under collaborative motion modes (CMMs). However, previous studies largely focus on traditional frameworks of collaborative mode, leaving a gap in exploring novel CMMs. Recently, a new concept called virtual co-embodiment (e.g., a shared virtual avatar controlled by multiple users) has emerged in research, showing potential to create new CMMs in the realm of Serious Games, contributing to the study of multiple users’ collaboration to assist rehabilitation training. Therefore, we developed a VR game called MindMeld Cyclers based on a virtual co-embodiment system to investigate the gaming experiences under different CMMs and assess their impact on user game engagement, embodiment, and perceived usability. This study elaborates on the design concept and technical implementation of the serious game, followed by an experiment for user study. The results ultimately demonstrate the potential of MindMeld Cyclers and its proposed new CCMs in the further development and application of serious games, highlighting their positive impact on CCM research in the context of VR serious games.

Research has demonstrated that users’ heuristic decision-making processes cause external factors like defaults and framing to influence the outcome of privacy decisions. Proponents of “privacy nudging” have proposed leveraging these effects to guide users’ decisions. Our research shows that defaults and framing not only influence the outcome of privacy decisions, but also the process of evaluating the contextual factors associated with the decision, effectively making the decision-making process more heuristic. In our analysis of an existing dataset of scenario-based smart home privacy decisions, we demonstrate that defaults and framing not only have a direct effect on participants’ decisions; they also moderate the effect of their cognitive appraisals of the presented scenarios on the decision. These results suggest that nudges like defaults and framing exacerbate the well-researched problem that people often employ heuristics rather than making deliberate privacy decisions, and that privacy-setting interfaces should avoid the effects of heuristic decision-making.

Several variants of spiking neural P systems (SNPS) have been presented in the literature to perform arithmetic operations. However, each of these variants was designed only for one specific arithmetic operation. In this paper, a complete arithmetic calculator implemented by SNPS is proposed. An application of the proposed calculator to information fusion is also proposed. The information fusion is implemented by integrating the following three elements: (1) an addition and subtraction SNPS already reported in the literature; (2) a modified multiplication and division SNPS; (3) a novel storage SNPS, i.e. a method based on SNPS is introduced to calculate basic probability assignment of an event. This is the first attempt to apply arithmetic operation SNPS to fuse multiple information. The effectiveness of the presented general arithmetic SNPS calculator is verified by means of several examples.

In this article, we extend and improve upon a previously developed data-driven approach to design privacy-setting interfaces for users of household IoT devices. The essence of this approach is to gather users’ feedback on household IoT scenarios before developing the interface, which allows us to create a navigational structure that preemptively maximizes users’ efficiency in expressing their privacy preferences, and develop a series of ‘privacy profiles’ that allow users to express a complex set of privacy preferences with the single click of a button. We expand upon the existing approach by proposing a more sophisticated translation of statistical results into interface design, and by extensively discussing and analyzing the tradeoff between user-model parsimony and accuracy in developing privacy profiles and default settings.

User testing is often used to inform the development of user interfaces (UIs). But what if an interface needs to be developed for a system that does not yet exist? In that case, existing datasets can provide valuable input for UI development. We apply a data-driven approach to the development of a privacy-setting interface for Internet-of-Things (IoT) devices. Applying machine learning techniques to an existing dataset of users' sharing preferences in IoT scenarios, we develop a set of "smart" default profiles. Our resulting interface asks users to choose among these profiles, which capture their preferences with an accuracy of 82%---a 14% improvement over a naive default setting and a 12% improvement over a single smart default setting for all users.

As a popular routing protocol in wireless sensor networks (WSNs), greedy routing has received great attention. The previous works characterize its data deliverability in WSNs by the probability of all nodes successfully sending their data to the base station. Their analysis, however, neither provides the information of the quantitative relation between successful data delivery ratio and transmission power of sensor nodes nor considers the impact of the network congestion or link collision on the data deliverability. To address these problems, in this paper, we characterize the data deliverability of greedy routing by the ratio of successful data transmissions from sensors to the base station. We introduce n-guaranteed delivery which means that the ratio of successful data deliveries is not less than n, and study the relationship between the transmission power of sensors and the probability of achieving n-guaranteed delivery. Furthermore, with considering the effect of network congestion, link collision, and holes (e.g., those caused by physical obstacles such as a lake), we provide a more precise and full characterization for the deliverability of greedy routing. Extensive simulation and real-world experimental results show the correctness and tightness of the upper bound of the smallest transmission power for achieving n-guaranteed delivery.

Modern computer systems are increasingly bounded by the available or permissible power at multiple layers, ranging from a single chip to an entire data center. To cope with this reality, it is necessary to understand how power bounds impact the design and performance of emergent computer systems. In this paper, we study the problem of coordinated power allocation between processors and memory modules on power-bounded systems. We experimentally and analytically investigate the dynamics between cross-component power allocation and application performance, identify the patterns of power allocation scenarios, and develop optimal power allocation methods. In our study, we discover that (1) different applications share categorical patterns with regard to how power allocations among individual components impact application performance and actual power, (2) the per-node power budget must exceed a certain threshold in order to achieve desirable performance and efficiency, (3) there exist workload-specific optimal power allocations under a given power budget and such optimal power coordination can be pinpointed using the heuristics derived from the categorical patterns and a light-weight power-performance profiling. Results from this study demonstrate the importance and feasibility of cross-component coordination to the implementation of power-bound high performance computing technology.

As a popular routing protocol in wireless sensor networks (WSNs), greedy routing has received great attention. The previous works characterize its data deliverability in WSNs by the probability of all nodes successfully sending their data to the base station. Their analysis, however, neither provides the information of the quantitative relation between successful data delivery ratio and transmission power of sensor nodes nor considers the impact of the network congestion or link collision on the data deliverability. To address these problems, in this paper, we characterize the data deliverability of greedy routing by the ratio of successful data transmissions from sensors to the base station. We introduce η-guaranteed delivery which means that the ratio of successful data deliveries is not less than η, and study the relationship between the transmission power of sensors and the probability of achieving η-guaranteed delivery. Furthermore, with considering the effect of network congestion and link collision, we provide a more precise and full characterization for the deliverability of greedy routing. Extensive simulation and real-world experimental results show the correctness and tightness of the upper bound of the smallest transmission power for achieving η-guaranteed delivery.

In some seismic numerical applications we have to simulate wave propagation with sharp medium discontinuities. The rotated staggered grid (RSG) finite difference (FD) scheme and the arbitrary high-order derivatives discontinuous Galerkin (ADER-DG) finite element (FE) scheme can both be used for the problem of strong material heterogeneities. In this paper we study their behavior in a two-layer model with a varying ratio of the material parameters in the first layer to the second. We compared the results of the numerical schemes with the exact solution. The FD method and the FE method can both get small envelop misfits. The FE scheme has advantage over the FD method on phase misfits, but it needs a high CPU effort.