Over-The-Air (OTA) software updates are widely used in automotive embedded systems to remotely address software defects and vulnerabilities. However, the distribution of software packages is vulnerable to malicious attacks, posing severe security threats. Various cryptographic algorithms are used to secure automotive OTA software updates. However, existing secure OTA software updates rely on digital certificates for identity authentication. These digital certificates are often provided by third-party Certificate Authorities (CAs) and issued based on physical identifiers (e.g., Vehicle Identification Number (VIN), engine number, or Electronic Control Unit-ID (ECU-ID)), which are susceptible to illegal modification. Additionally, these secure OTA software updates fail to provide integrated security that encompasses authentication, confidentiality, integrity, access control, and data freshness. To tackle these existing drawbacks, we propose LigSecOTA, a lightweight OTA software update with integrated security based on a one-machine-one-certificate digital identity management system. The one-machine-one-certificate digital identity management system issues a unique and trusted digital certificate for each ECU based on bit time information instead of physical identifiers; these certificates are then used for ECU authentication. LigSecOTA ensures integrated security, including authentication, confidentiality, integrity, access control, and data freshness, through three processes: authentication, authorization, and package distribution. The authorization dy namically provides keys for the package distribution, significantly enhancing security. The security attributes of LigSecOTA are formally verified using the ProVerif tool. Finally, we evaluate LigSecOTA on the NXP LS1028A platform with an ARM Cortex A72 core. Experimental results demonstrate that LigSecOTA outperforms state-of-the-art secure OTA software updates in terms of computation and communication overhead, highlighting its lightweight nature.

Cross-speed bearing fault diagnosis based on unsupervised domain adaptation can handle data distribution differences across various operating speeds, supporting intelligent maintenance of equipment like wind turbines with variable operating speeds. Existing methods focus on aligning sample distributions between source and target domains through global or subdomain correlations. However, these methods overlook essential relationships, such as possible high sample similarity between target subdomains and discrepancies in decision boundaries between source and target domains, leading to sub-stantial class confusion issues. To address class confusion, this paper proposes a subdomain uncertainty optimization method by using these relationships. Class uncertainty is proposed to quantify the degree of classification ambiguity among target domain samples, facilitating the differentiation of high-similarity samples. Boundary optimization is introduced to refine decision boundaries learned from the source domain, alleviating the adverse effects of boundary discrepancies between domains. Additionally, the CL-CNN network is adopted and adjusted to collaborate with the class uncertainty term and boundary optimization term, thus achieving optimal cross-speed fault diagnosis. Extensive experiments conducted across 18 cross-speed tasks demonstrate the superiority of the proposed method, which achieves a stable average accuracy of 99.86%. All code will be released on https://github.com/IWantBe/SUO.

Some people with a motor disability that limits hand movements use technology to draw via their eyes. Free-eye drawing has been re-investigated recently and yielded state-of-the-art results via unimodal gaze control. However, limitations remain, including limited functions, conflicts between observation and drawing, and the brush tailing issue. We introduce a professional unimodal gaze control free-eye drawing application and improve upon free-eye drawing by extended gaze-based user interface functions, improved brush dynamics, and a double-blink gaze gesture. An experiment and a field study were conducted to assess the system’s usability compared to the mainstream gaze-control drawing method and hand drawing and the accessibility among users with motor disabilities. The results showed that the application provides efficient interaction and the ability to create hand-sketch-level graphics for people with motor disabilities. Herein, we contribute a robust and professional free-eye drawing application, detailing valuable design considerations for future developments in gaze interaction.

Multiple processors system on chip (MPSoC) has been the trend in cyber-physical systems (CPSs), and reasonable partitioning for MPSoC resources is a critical step in CPSs design. The uncertainties of environment and time are both important factors that need to be considered in the design, but none of the previous work pays attention to two uncertainties at the same time. The state-of-the-art work presented a detailed process of applying uncertain programming to solve the partitioning problem, which provides a solution for designing in an uncertain environment. However, this work only considers the bipartition scenario which cannot be directly applied to MPSoC, and it does not focus specifically on time uncertainty. In this paper, we propose a method for modeling the MPSoC partitioning problem in an uncertain environment, with the time uncertainty concern. We present the uncertain model that can be applied to the multiple optional resources scenario. We build the optimization model with the objective of minimizing time, analyze two different cases of minimizing the uncertain time, and finally prove a unified deterministic model to solve. We come up with three algorithms, including the heuristic algorithm, the genetic algorithm, and the exact algorithm, and experiments show that the heuristic algorithm and the genetic algorithm can obtain good approximate solutions compared with the exact algorithm.

EyeCompass is a novel free-eye drawing system enabling high-fidelity and efficient free-eye drawing through unimodal gaze control, addressing the bottlenecks of gaze-control drawing. EyeCompass helps people to draw using only their eyes, which is of value to people with motor disabilities. Currently, there is no effective gaze-control drawing application due to multiple challenges including involuntary eye movements, conflicts between visuomotor transformation and ocular observation, gaze trajectory control, and inherent eye-tracking errors. EyeCompass addresses this using two initial gaze-control drawing mechanisms: brush damping dynamics and the gaze-oriented method. The user experiments compare the existing gaze-control drawing method and EyeCompass, showing significant improvements in the drawing performance of the mechanisms concerned. The field study conducted with motor-disabled people produced various creative graphics and indicates good usability of the system. Our studies indicate that EyeCompass is a high-fidelity, accurate, feasible free-eye drawing method for creating artistic works via unimodal gaze control.

Synthesizing realistic clouds is a complex and demanding task, as clouds are characterized by random shapes, complex scattering and turbulent appearances. Existing approaches either employ two-dimensional image matting or three-dimensional physical simulations. This paper proposes a novel sketch-to-image deep learning system using fast sketches to paint and edit volumetric clouds. We composed a dataset of 2000 real cloud images and translated simple strokes into authentic clouds based on a conditional generative adversarial network (cGAN). Compared to previous cloud simulation methods, our system demonstrates more efficient and straightforward processes to generate authentic clouds for computer graphics, providing a widely accessible sky scene design approach for use by novices, amateurs, and expert artists.

Understanding how collective actions evolve into violence is critical for guiding public safety decisions. While real- world observations of collective violence are difficult and rare, simulation models can help us to explore its evolution process. We propose an Agent-Based Emotion Contagion (ABEC) model that simulates the spread of group violence when the mechanism of contagious grievance is at work. The model is motivated by related social psychological theories of group behavior and is implemented by incorporating the epidemiological emotion contagion mechanism with crowd's game-theoretic behaviors with an agent-based approach. Our simulation model generates some crowd patterns, including local outbursts of collective violence with grievance contagion, dynamic spatial clustering of violent civilians and the nonlinear evolution of collective violence. We also explore the variations of violence evolution by varying some parameters of our model. Results show that the high-density crowds and the widespread of grievance promote violence outburst. These results suggest opportunities to curb collective violence through dispersing crowds and allaying the grievance.