Winning the battle against excessive screen time is challenging, considering the emergence of new, exciting, and engaging screen content. From the use of applications such as YouTube, Instagram, Facebook, TikTok, and Roblox, users have different reasons for interacting with screen devices. Several public health issues caused by excessive screen time have become a critical concern with many adverse effects, especially for children. We conducted an interview study with both parents and children to understand the current practices and challenges in screen time management. Several approaches have been identified to manage and reduce screen time, including parental control, self-monitoring, setting daily limits, and verbal agreement. Most of these approaches involve a large commitment from both parents and children and a lack of motivation from the children to comply. Effective solutions are needed to help parents and children work together by empowering children to make healthy and productive decisions regarding screen use.

Large Language Models (LLMs) have advanced tremendously in recent years. Large-scale models such as GPT4, GEMINI, Llama, and Claude have opened new frontiers for what is possible for generative models. They’ve been utilized in a plethora of applications, including those that require tedious domain knowledge and subject matter expertise. One such application for the utilization of LLMs proposed in this study is the attribute generation process for both labeled and unlabeled datasets. Data labels, along with the image features/attributes, are crucial for the application of image datasets in causal reasoning tasks. Causal reasoning for image datasets cannot be accomplished without the proper data labels, which serve as the ground truth. However, manual data labeling and attribute/feature generation for image datasets is a tedious and sometimes unfeasible task. Therefore, we propose to use LLMs for automating the attribute generation and image labeling task for images. To address this, we investigate LLMs to determine whether they are capable of generating attributes and how correct these attributes are. Using two different datasets and a combination of prompt-tuning, we highlight mixed results for the ability of ChatGPT 4o to accurately classify the image label and generate attributes for the images based on the prompt, types of images, input methods, and whether fine-tuning is performed. For the AWA dataset, we highlight $100 \%$ accuracy for image classification and attribute generation when prompted with the options for the possible attributes. When the attribute options are not provided the results vary for both datasets. For the MRI scans, the accuracy of the image classification and attributes varied from $40 \%$ to $100 \%$ depending on whether the attribute options were provided and the level of granularity needed.

In this paper, we describe a preliminary case study that examines the challenges faced by very young children with Type I Spinal Muscular Atrophy (SMA) and how technology may help these children live a more independent life. Several input solutions were examined to support interaction between a young patient and computer-based systems. We started working with the patient when he was two and a half years old. The challenges observed and lessons learned regarding both working with very young children with severe disabilities and the use of specific technical solutions are discussed.

With the increasing development of social media, we now face large amounts of up-to-date data, new information resources, and fast and transparent information propagating methods. They have changed the way we understand the world, which means we can have a different perspective when computing situation awareness. In this paper, we give a detailed explanation on how social media data affect situation awareness and our focus is set on the military environment. We discuss why subject matter expert based human computation is necessary and essential for this procedure and how to involve it in the situation awareness architecture. The goal of our paper is to give readers some suggestions on how to use social media and human processing power within the military situational awareness domain.

In this paper, we present a novel smartphone-based rehabilitation approach called ARMStrokes that provides real-time support for stroke survivors to complete rehabilitation exercises for upper extremity recovery. ARMStrokes allows stroke survivors to exercise through interactive games anytime and anywhere and receive instant feedback about the quality of their performance. Stroke survivors can also communicate with their therapists or physicians through the supporting web-based platform. Focus groups involving stroke survivors, caregivers, and therapists have been conducted to evaluate the system and the feedback is highly positive.

In today's military environment vast amounts of disparate information are available. To aid situational awareness it is vital to have some way to judge information importance. Recent research has developed a fuzzy-based system to assign a Value of Information (VoI) determination for individual pieces of information. This paper presents an investigation of the effect of integrating subsequent complementary and/or contradictory information into the VoI process. Specifically, the idea of using complementary and/or contradictory new information to impact the previously used fuzzy membership values for the information content characteristic applied in the VoI calculations is shown to be a particularly suitable approach.

Repetitive rehabilitation exercises on a regular basis are crucial for enhancing and restoring functional ability and quality of life to those with cognitive and physical disabilities such as stroke. However, a good proportion of patients are often non-compliant to the repetitive exercise regimen prescribed by their therapists. This may be due to a variety of factors including lack of motivation. Although interactive gaming systems such as the Wii by Nintendo and haptic devices have been introduced to make repetitive actions more fun and engaging for typically functioning individuals, challenges still remain for those who have some impairment. Expensive and complicated gaming systems are still not widely used by those with disabilities. In addition, therapists and caregivers, who play an important role in rehabilitation, have not been sufficiently involved. To address these problems as they pertain to people with disabilities, we propose a collaborative rehabilitation support system (CRSS) where mobile-based interactive games are included. Through our approach, we expand in-home rehabilitation to self-rehabilitation which allows users to perform rehab anywhere and anytime. A pilot study involving stroke survivors, caregivers, therapists and physicians was conducted to evaluate our system, and users' feedback is highly positive.

Mobile-based augmented reality is an emerging technology that provides immersive experiences over wireless networks. Its applications, such as 3D streaming, have become more and more popular on mobile devices. However, most of these applications do not support real-time 3D interactions. Consequently, mobile users can only watch or browse 3D contents passively instead of actively interacting with 3D objects. In this paper, we propose a novel method that allows mobile users to change a model's shape and motions through interactions via mobile touch screen and obtain feedbacks in real-time. To accelerate computational speed and reduce communication load, we compute 3D deformations using a spectral representation. Moreover, a progressive deformation streaming technique is proposed to reduce the effect of interaction delay between the server and mobile clients. Our experimental results indicate that our method provides real-time interaction feedback, offering satisfactory user experiences.

In this video presentation, we introduce an immersive multimodal virtual environment which supports real-time interactions with 3D deformable model through a haptic device. We include a system called "FakeSpace" to imitate 3D environment, and a PHAMTOM device to simulate touching forces. Movements of 3D deformable models are simulated based on a spectral method, and forces are simulated as spring forces. We are able to real-time update both visual and haptic feedbacks, so that to provide a more realistic user interaction. In addition, with the help of stereoscopic display, we can present an immersive 3D experience. This video illustrates the settings of our environment and demonstrates how users real-time manipulate 3D models in this immersive system using some interactive examples. Our system is reconfigurable and is useful for different applications in the fields of education, entertainment, medical simulation and so on.

Collaborative virtual environment has been limited on static or rigid 3D models, due to the difficulties of real-time streaming of large amounts of data that is required to describe motions of 3D deformable models. Streaming shape deformations of complex 3D models arising from a remote user's manipulations is a challenging task. In this paper, we present a framework based on spectral transformation that encodes surface deformations in a frequency format to successfully meet the challenge, and demonstrate its use in a distributed virtual environment. Our research contributions through this framework include: i) we reduce the data size to be streamed for surface deformations since we stream only the transformed spectral coefficients and not the deformed model; ii) we propose a mapping method to allow models with multi-resolutions to have the same deformations simultaneously; iii) our streaming strategy can tolerate loss without the need for special handling of packet loss. Our system guarantees real-time transmission of shape deformations and ensures the smooth motions of 3D models. Moreover, we achieve very effective performance over real Internet conditions as well as a local LAN. Experimental results show that we get low distortion and small delays even when surface deformations of large and complicated 3D models are streamed over lossy networks.

Fast and efficient streaming of detailed 3D model over lossy network has long been a challenge, although progressive compression techniques were proposed long time ago. One reason is that packet loss occurring in unreliable networks is highly unpredictable, and leads to connectivity inconsistency and distortions. In this paper, we address this problem by proposing a receiver-based loss tolerance scheme based on a prediction technique. Our method works without introducing protection bits and retransmission. We stream mesh refinement data on reliable and unreliable networks separately so as to reduce the transmission delay as well as to obtain a satisfactory decompression result. The tests indicate that the decompression is completed quickly, suggesting that it is a practical solution. Moreover, the proposed prediction technique achieves a good approximation of the original mesh with low distortion.

Nowadays, the Internet provides a convenient medium for sharing complex 3D models online. However, transmitting 3D progressive meshes over networks may encounter the problem of packets loss that can lead to connectivity inconsistency and distortion of the reconstructed meshes. In this paper, we combine reliable and unreliable channels to reduce both time delay and mesh distortion, and we propose an error-concealment scheme for tolerating packet loss when the meshes are transmitted over unreliable network channels. When the loss of connectivity data occurs, the decoder can predict the geometry data and mesh connectivity information, and construct an approximation of the original mesh. Therefore, the proposed error-concealment scheme can significantly reduce the data size required to be transmitted over reliable channels. The results show that both the computational cost of our error-concealment scheme and the distortion introduced by our scheme are small.