Industrial micro-CT demands reconstruction methods that reliably preserve fine internal details while keeping scanning cost manageable. Analytical and iterative algorithms remain widely deployed. However, fidelity degrades as projection counts decrease, while higher projection counts inevitably increase scanning cost. To address this trade-off, implicit neural representations (INRs) have been explored as promising approaches to improve memory and computational efficiency while enhancing reconstruction quality. Nevertheless, current INRs converge slowly, suffer from spectral bias, and lack integrated rendering capabilities. We therefore introduce HR-NAF, a high-resolution neural attenuation field that integrates ray-based sampling (conforming to CT geometry and densely sampling regions of interest), space-variant Fourier encoding (SVFE) to mitigate spectral bias, and a bin scheduler that accelerates convergence by progressively emphasizing high-frequency details. Furthermore, HR-NAF supports direct 3D visualization without external engines. Our method achieves a substantial gain in reconstruction quality with varying numbers of projections, and this performance gap became more pronounced with limited-view settings. This superior fidelity directly enhances the reliable identification of critical high-frequency defects (e.g., pores and cracks) in industrial components, underscoring HR-NAF’s potential to significantly improve the accuracy and efficiency of non-destructive testing (NDT) procedures.

Human fall (HF) detection is critical for ensuring the health of elderly people in preventing accidents and enhancing healthcare outcomes. To address enhanced fall detection, we introduce acceleration and body part clustering for human fall detection (ABC-HF), a novel deep-learning methodology constructed for accurate HF detection using millimeter wave (mmWave) radar sensors. However, challenges arise due to data ambiguities from mmWave measurements, resulting in an incomplete representation of complex HF motions. The proposed ABC-HF framework is constructed as an anomaly detection framework based on a variational autoencoder (VAE) with a reverse frame estimation model. This model is equipped with a human fall descriptor (HF-descriptor) designed to encapsulate the latent features of HF, thereby facilitating an understanding of HF. This descriptor incorporates physical characteristics of HF. It includes a temporal differential layer specifically designed to extract acceleration features, which are critically correlated with falls. Furthermore, to reflect the composite structure of the human body, an attention-based soft clustering module has been integrated. This dual approach ensures a comprehensive representation and analysis of the dynamics involved in HF. Through rigorous quantitative and qualitative evaluations, we have validated the performance of our model. In quantitative analyses, ABC-HF stands out by achieving superior F-scores with over 94% across a range of HF scenarios, encompassing diverse fall and non-fall movements in settings such as indoor office spaces and industrial environments with complex terrain features including varied floor surfaces, elevation changes, and obstacles. During qualitative assessments, we examined the operations of the HF-descriptor in detail.

This paper aims to foster social interaction between parents and young adult children living apart via music. Our approach transforms their music-listening moment into an opportunity to listen to others' favorite songs and enrich interaction in their daily lives. To this end, we designed and implemented DJ-Fam, a mobile application that enables parents and children to listen to their favorite songs and use them as conversation starters to foster parent-child interaction. From our deployment study with seven families over four weeks in South Korea, we show the potential of DJ-Fam to positively influence parent-child interaction and their mutual understanding and relationship. Specifically, DJ-Fam considerably increases the frequency of communication and diversifies the communication channels and topics, all of which are satisfactory to the participants.

The blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically consist of many sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or for reliable drone delivery in smart cities. The distributed ledger and data block validation across blockchain networks guarantee the ultimate data security. However, the current blockchain technology is restricted in terms of its overall deployment across IoT networks. A general permissionless blockchain technology typically targets high-performance network nodes with sufficient computing power and memory space. A blockchain node with less computing power and memory, such as an IoT sensor or actuator, cannot employ blockchain technology as a fully functional node. A lightweight blockchain provides practical blockchain availability to IoT networks. We propose an operational advance to develop a lightweight blockchain for IoT networks. The opportunistic block validation optimizes the block validation process. It measures the network vulnerability based on the node reputation, block validation degree, number of fraudulent messages, and network stability. The reinforcement learning mechanism employed in the opportunistic block validation determines whether block validation is to be performed. Two separately developed reinforcement learning methods can increase the processing performance while maintaining the transaction data integrity. In addition, the proposed blockchain technology is easily implementable because it adopts a Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed block validation mechanism.

Rural children in South Korea exhibit higher foreign language anxiety and lower English competency. For such marginalized contexts where local communities cannot support children's learning, we propose and explore a new pedagogical approach, CRAYON (Community-based RelAY Online educatioN). In CRAYON, a pool of non-professional tutors take turns to meet and teach rural children in short relay sessions through mobile technologies. It uncovers and promotes volunteers' internal willingness to participate in community-based teaching, which could otherwise be fragmented and dormant in their tightly-woven daily lives. It greatly lessens the barriers to participation from multiple dimensions, i.e., time, space, and expertise, and encourages interested volunteers to easily join without taking much burden. As such, the approach can create new learning opportunities and help rural children overcome their motivational and environmental hurdles. Tutors could approach each child and share short but precious time with her; helping her experience repetitive and sufficient exposure to the language, each time with a newly met tutor. We conducted a short relay session-based English learning program for 5 rural children for 4 weeks in South Korea with 15 tutors. From the field deployment, we find that the rural children and the undergraduate tutors engaged in effective interactions and scaffolding, despite the constraints of partitioned short sessions. A particular pattern of interaction, i.e., continuous learner engagement support, emerged as they drew out the interactions over a short period of time. It was highly encouraging to observe that all children, including those who were disengaged in their classroom environments, actively participated in the CRAYON sessions. The findings elicited from the study have important implications in multiple dimensions. They suggest the possibility of extending the scope of learning environments to include first-met tutors and learners beyond re-established relationship. In a larger perspective, the findings imply a new direction to overcome the challenges of low childhood literacy in under-resourced areas. With adequate and sufficient support from educational institutions and CRAYON, this study argues that volunteer tutors with less experience can deliver effective instruction by sharing just a short period of time, and help a child who has been lagging behind the pace of the school catch up and re-engage.

Accurate estimation of indoor space geometries is vital for constructing precise digital twins, whose broad industrial applications include navigation in unfamiliar environments and efficient evacuation planning, particularly in low-light conditions. This study introduces EchoScan, a deep neural network model that utilizes acoustic echoes to perform room geometry inference. Conventional sound-based techniques rely on estimating geometry-related room parameters such as wall position and room size, thereby limiting the diversity of inferable room geometries. Contrarily, EchoScan overcomes this limitation by directly inferring room floorplan maps and height maps, thereby enabling it to handle rooms with complex shapes, including curved walls. The segmentation task for predicting floorplan and height maps enables the model to leverage both low- and high-order reflections. The use of high-order reflections further allows EchoScan to infer complex room shapes when some walls of the room are unobservable from the position of an audio device. Herein, EchoScan was trained and evaluated using RIRs synthesized from complex environments, including the Manhattan and Atlanta layouts, employing a practical audio device configuration compatible with commercial, off-the-shelf devices.

Deep learning-based fault diagnosis methods require a large number of labeled datasets. However, considering the changing operating conditions, it is impractical to obtain labeled datasets for all cases. Therefore, this study proposes a new unsupervised clustering and domain adaptation framework to circumvent data deficiency and domain issues. The proposed framework comprises two steps: unsupervised clustering and domain adaptation. In the unsupervised clustering, an expectation-maximization adversarial autoencoder, which combines an expectation-maximization algorithm with an adversarial autoencoder, is used for feature extraction and subspace mapping. Subsequently, the mapped features are clustered using a Gaussian mixture model. In the domain adaptation, a domain synchronization that is based on the symmetric Kullback-Leibler divergence metric is used to infer the relationship between the source and target domain clusters. The experiments on two rolling-element-bearing datasets validate the effectiveness of our method. Specifically, our method performs domain adaptation without retraining, which is promising for real industrial applications.

Improving the communication performance of parallel programs is an important but difficult problem in a large-scale distributed memory-based cluster. Efforts to improve parallel scalability often face severe huddles in managing communication overheads. This paper proposes a framework of a space-filing curve(SFC)-based task-remapping for communication intensive parallel applications. An SFC-based mapping, when applied for task-mapping of parallel applications preserves locality in terms of communications and produce a less fragmented task-mapping, reducing communication overheads. The framework also provides tools for performance analysis to see if the proposed task-mapping is appropriate for a given application running on a target system. It further develops a binary classifier as a predictor to decide whether or not to apply the proposed mapping before run-time. We evaluate the framework with three communication intensive applications in Cartesian coordinates: P3DFFT solver and Channel code using 2D domain decomposition model, and Poisson solver using 3D domain decomposition. The evaluation is conducted on a large-scale cluster system of fat-tree topology with up to 1,024 compute nodes. The proposed task-mapping achieves the overall performance improvement ranging from ~30% to ~66% over the baseline approach depending on the workloads. Also, when used in combination with the binary classifier-based predictor, it achieves the expected performance gains from 4% to 8%.

Public spaces1 are a basis of urban lives. The mode and culture of sharing could be an indicator of the quality of life in the cities. For example, the buses and restaurants should comfort and bring satisfaction to individual visitors, including the vulnerable with special needs. However, their operation mostly occurs in rather a closed and exclusive manner [2]. An inherent limitation to such an inclusive sharing lies in the exclusive modes of traditional device interfaces; a variety of devices, called public devices hereafter, are installed in the public space and determine operational details of the space. As such, the space itself is shared, however, the public devices are controlled in exclusive ways. Could the sharing of the public space be operated in a democratic way?

Public spaces are equipped with 'public actuators', e.g., HVAC, lighting fixtures, speakers, or streaming TV channels to ensure their visitors' comfort. However, many public actuators rarely allow the visitors to adjust their operation, limiting their utility and fairness across the visitors. Also, the social bar is often too high to speak up one's preference and attempt to change an actuator's operation. Social control and use of IoT devices is an underexplored new direction of research even with its huge potential and implication, but comes with high complexity and scale. This paper proposes a novel architecture, namely, Social Control-and-Use Architecture for IoT Devices, which provides a systematic view and an effective tool to handle the complication and intricacy in system design. It also proposes Hivemind, a first-of-a-kind system developed, upon the architecture, for sharing IoT-enabled actuators in a public space. It transforms an exclusively-controlled actuator in a public space into a true public actuator, supporting visitors to instantly participate in the democratic collective control. Also, a myriad of off-the-shelf actuators are easily incorporated without modification to their implementation. The field deployment of Hivemind shows its comprehensive service coverage as well as the users' approval on the democratic collective control of public actuators.

Public spaces, where we gather, commune, and take a rest, are the essential parts of a modern urban landscape, enriching citizen's everyday life [3]. How we share these spaces are considered an indicator of the quality of life. Public spaces thus have a responsibility to provide comfort and satisfaction to any visitors. However, in most times, the operations of the spaces are managed in rather an exclusive manner.

The automotive navigation often embarrasses drivers by providing guidance that is awkward, incomprehensible, or almost impossible to follow. We point out the lack of on-the-spot awareness as the key reason behind this situation. The current navigation does not consider a driver's characteristics such as driving ability, as well as the detailed conditions of the current driving environment. In this paper, we explore the cases of embarrassment related to navigation usage. We collected a total of 56 cases of embarrassing moments from three drivers' experiences and derived 8 categories of embarrassing moments.

Real-time remote interaction has become easier and richer powered by recent advances in mobile computing and communication. A number of research have been explored on enriching family interaction by augmenting an interaction channel with asynchronous communication [6] or additional sensory stimuli [5]. However, it is still far from achieving a sense of living together for family members involuntarily living apart, especially in context-aware impromptu interaction. For families living together, it is trivial to naturally perceive behavioral and situational contexts of the other and initiate a relevant interaction intuitively. For example, a wife starts a casual chat with asking her husband what he is going to cook when she sees him going to the kitchen or hears a simmering sound.

People in work-separated families have been heavily relying on cutting-edge face-to-face communication services. Despite their ease of use and ubiquitous availability, experiences in living together are still far incomparable to those through remote face-to-face communication. We envision that enabling a remote person to be spatially superposed in one's living space would be a breakthrough to catalyze pseudo living-together interactivity. We propose HomeMeld, a zero-hassle self-mobile robotic system serving as a co-present avatar to create a persistent illusion of living together for those who are involuntarily living apart. The key challenges are 1) continuous spatial mapping between two heterogeneous floor plans and 2) navigating the robotic avatar to reflect the other's presence in real time under the limited maneuverability of the robot. We devise a notion of functionally equivalent location and orientation to translate a person's presence into another in a heterogeneous floor plan. We also develop predictive path warping to seamlessly synchronize the presence of the other. We conducted extensive experiments and deployment studies with real participants.

No abstract available.

We introduce Zaturi, a system enabling parents to create an audio book for their babies by utilizing micro spare time at work. We define micro spare time at work as tiny fragments of time with low cognitive loads that frequently occur at work, such as waiting for an elevator. We show that putting together micro spare time at work helps a working parent (1) build a tangible symbol conveying his/her thoughts to the beloved baby and (2) develop his/her own feelings of parental achievement without compromising regular working hours. Zaturi lets the parent immediately be aware of micro spare time and provides a crafted interface to seamlessly record the book piece by piece, so that the baby can enjoy listening to the book recorded in the parent's own voice. Through an extensive design process, we characterize the notion of micro spare time and build a working prototype of Zaturi. We also report parents' perceptions and family reactions after a two-week deployment.

We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.

Emerging continuous sensing apps introduce new major factors governing phones' overall battery consumption behaviors: (1) added nontrivial persistent battery drain, and more importantly (2) different battery drain rate depending on the user's different mobility condition. In this paper, we address the new battery impacting factors significant enough to outdate users' existing battery model in real life. We explore an initial approach to help users understand the cause and effect between their physical activity and phones' battery life. To this end, we present Sandra, a novel mobility-aware smartphone battery information advisor, and study its potential to help users redevelop their battery model. We perform an extensive explorative study and deployment for 30 days with 24 users. Our findings reveal what they essentially learned, and in which situations they found Sandra very helpful. We share the lessons learned to help in the design of future mobility-aware battery advisors.

Recently, a number of continuous sensing applications have been actively proposed in research communities and commercially released in the market. However, due to their unique power characteristics, user behavior-dependent battery drain, they bring new challenges for users' power management on these applications. In this demonstration, we present a comprehensive approach to support users' power management for continuous sensing applications. First, at pre-installation time, we provide an instant, personalized power estimation of a continuous sensing application. Without exhaustive trial and error, users can decide judiciously to install a certain application or not. Second, at runtime, we provide mobility-aware battery information. With this information, users can better estimate the phone's remaining battery life based on their imminent mobility conditions and take necessary actions in advance such as carrying an additional battery or minimizing the use of applications.

Interpersonal touch is our most primitive social language strongly governing our emotional well-being. Despite the positive implications of touch in many facets of our daily social interactions, we find wide-spread caution and taboo limiting touch-based interactions in workplace relationships that constitute a significant part of our daily social life. In this paper, we explore new opportunities for ubicomp technology to promote a new meme of casual and cheerful interpersonal touch such as high-fives towards facilitating vibrant workplace culture. Specifically, we propose High5, a mobile service with a smartwatch-style system to promote high-fives in everyday workplace interactions. We first present initial user motivation from semi-structured interviews regarding the potentially controversial idea of High5. We then present our smartwatch-style prototype to detect high-fives based on sensing electric skin potential levels. We demonstrate its key technical observation and performance evaluation.