Soft floating robots (SFRs) represent a shift from rigid machines, offering gravity-defying, compliant, and tactile embodiments for indoor cohabitation. However, their development remains fragmented across isolated prototypes, lacking a coherent design vocabulary. Without a systematic understanding of their interactional capabilities, designers struggle to leverage SFRs’ unique affordances, and these systems often remain limited to novelty applications that are difficult to integrate into everyday life. To address this, we propose a design space for interaction with SFRs. Informed by an exploratory study with 12 experts from HCI, Design, and Robotics, we identify ten design dimensions spanning physical, interactive, and behavioral properties, along with a range of application scenarios. We further present proof-of-concept design examples to demonstrate how this design space can support diverse interaction possibilities. This work contributes a structured framework for understanding and designing interactions with SFRs, supporting their integration into everyday indoor environments.

The rapid advancement of 5G and 6G technologies has introduced various innovative applications, such as autonomous driving and augmented reality, which significantly increase network traffic and computational demands, particularly in densely populated areas. This study focuses on the utilization of unmanned aerial vehicles (UAVs) for Mobile Edge Computing (MEC) to address these challenges. Specifically, we explore the joint optimization of base station (BS) selection, computing resource allocation and UAV trajectory to minimize system delays and energy consumption in scenarios where computational requirements are related to user movement. A deep reinforcement learning-based approach, UM-DDPG, is proposed to optimize task offloading strategies and UAV trajectories. With our simulation platform, the results show that our method has been effective in reducing the system cost, including both delay and energy consumption, compared to traditional methods.

Modern software systems require various capabilities to meet architectural and operational demands, such as the ability to scale automatically and recover from sudden failures. Self-adaptive software systems have emerged as a critical focus in software design and operation due to their capacity to autonomously adapt to changing environments. However, educating students on this topic is scarce in academia, and a survey among practitioners identified that the lack of knowledgeable individuals has hindered its adoption in the industry. In this paper, we present our experience teaching a course on selfadaptive software systems that integrates theoretical knowledge and hands-on learning with industry-relevant technologies. To close the gap between academic education and industry practices, we incorporated guest lectures from experts and showcases featuring industry professionals as judges, improving technical and communication skills for our students. Feedback based on surveys from 21 students indicates significant improvements in their understanding of self-adaptive systems. The empirical analysis of the developed course demonstrates the effectiveness of the proposed course syllabus and teaching methodology. In addition, we provide a summary of the educational challenges of running this unique course, including balancing theory and practice, addressing the diverse backgrounds and motivations of students, and integrating the industry-relevant technologies. We believe these insights can provide valuable guidance for educating students in other emerging topics within software engineering.

Unmanned aerial vehicles (UAVs) play a crucial role in mobile edge computing (MEC). On one hand, UAVs can serve as relay nodes, being flexibly deployed in various complex areas to provide network coverage services for users in remote regions. On the other hand, UAVs can carry edge servers, enabling them to approach terminal devices more conveniently and enhance the efficiency of task computation. However, in the face of a large number of computing tasks, the load balancing, network performance and computational resource management of the network are facing serious challenges due to the differences in task density caused by the irregular movement of ground users (GUs), as well as the limitations of the UAV’s computational capability and coverage. In order to address the above challenges, in this paper, We propose a multi-UAV collaborative MEC system. Our goal is to offload tasks as efficiently as possible with full and reasonable utilisation of computational resources, and to evenly distribute computational resources to meet the needs of varying levels of task intensity in a region through rational deployment of UAV locations and collaboration among UAVs. Specifically, in our proposed system framework, GUs can offload tasks to their associated UAVs and further decide whether or not to offload tasks to collaborative UAV or remote Base station (BS) based on the load of computational resources. We study the problem of location deployment of multiple UAVs for better offloading and resource allocation to tasks. Then, we model the task offloading and resource allocation process as a Markov decision process (MDP) focusing on minimising the average user cost of the system and propose a deep reinforcement learning (DRL)-based multi-uav collaborative computation offloading and resource allocation (DMCOA) algorithm. It has been shown through extensive simulation experiments that the algorithm can effectively reduce the average user cost of the MEC system.

To mitigate the domain shift and enhance the alignment of the spatial-spectral features, this letter proposes a novel dual-intervention-constrained mask-adversary (DICMA) framework for unsupervised domain adaptation (UDA) of hyperspectral image classification (HSIC). Innovatively, DICMA integrates a generator, masker, and bi-classifier within an adversarial framework constrained by a dual intervention mechanism. Specifically, the correlation intervention module (CIM) ensures the preservation and independence of causal spatial-spectral variables, while the knowledge distillation intervention module completes the spatial-spectral generalization with constrained distillation information. Besides, with the collaborative adversarial training strategy, the proposed approach transfers effective knowledge for spatial-spectral feature alignment. Experimental results and analyses demonstrate the effectiveness of the proposed DICMA model, which yields an accuracy of 91.15% in the Pavia University (PaviaU)$\to $Pavia Center (PaviaC). Our code will be released athttps://github.com/Chirsycy/DICMA.

The training of Deep Neural Networks (DNNs) brings enormous memory requirements and computational complexity, which makes it a challenge to train DNN models on resource-constrained devices. Training DNNs with reduced-precision data representation is crucial to mitigate this problem. In this article, we conduct a thorough investigation on training DNNs with low-bit posit numbers, a Type-III universal number (Unum). Through a comprehensive analysis of quantization with various data formats, it is demonstrated that the posit format shows great potential to be employed in the training of DNNs. Moreover, a DNN training framework using 8-bit posit is proposed with a novel tensor-wise scaling scheme. The experiments show the same performance as the state-of-the-art (SOTA) across multiple datasets (MNIST, CIFAR-10, ImageNet, and Penn Treebank) and model architectures (LeNet-5, AlexNet, ResNet, MobileNet-V2, and LSTM). We further design an energy-efficient hardware prototype for our framework. Compared to the standard floating-point counterpart, our design achieves a reduction of 68, 51, and 75 percent in terms of area, power, and memory capacity, respectively.

Traditional methods of access control such as password token or identification card are being replaced by biometrics recognition technology in many fields because of their limitation in reliability and usability. Vein pattern identification is outstanding compared to other biometrics methods such as fingerprint or iris due to its dependability and ease of use. At the same time, current biometrics recognition systems and applications have some disadvantages in usability, cost, and supported user age range. In this paper, we propose MyPalmVein, which is a low-cost identification system based on vein pattern recognition. We also introduce a mobile version of MyPalmVein system. The evaluation of MyPalmVeins performance on a public database of 250 subjects as well as field tests enrolled around 300 subjects from 2 months to 65+ years old shows that MyPalmVein is very accurate on wide age range user.