General movement assessment (GMA) is a non-invasive method used to evaluate neuromotor behavior in infants under six months of age and is considered a reliable tool for the early detection of cerebral palsy (CP). However, traditional GMA relies on the subjective judgment of multiple internationally certified physicians, making it time-consuming and limiting its accessibility for widespread use. Furthermore, artificial intelligence (AI) approaches may overcome these limitations but are usually based on motion skeletons and lack the ability to capture detailed body information. Here, we propose CoGMA (Collaborative General Movements Assessment), a novel multi-modality co-learning framework for GMA. By integrating multimodal large language model as auxiliary network during training, CoGMA incorporates four types of input data-skeleton data, clinical information, RGB video, and text descriptions-to enhance representation learning. During inference, however, CoGMA achieves efficient and accurate prediction using only skeleton data and clinical information. Experimental evaluations indicate that CoGMA demonstrates robust performance across both the writhing and fidgety movement stages, while also excelling in zero-shot evaluation of fidget movement, thereby mitigating the issue of limited training samples in this stage. This framework significantly enhances the GMA methodology and lays the groundwork for future advancements in early detection and research on infant neuromotor behavior. Additionally, to facilitate anonymized data sharing, we introduce InfantAnimator, a tool that generates non-identifiable videos while preserving essential motion features, thereby supporting broader research and collaboration. The code is available at GitHub: https://github.com/wwYinYin/CoGMA.

Path planning is a critical technology for autonomous navigation in mobile robots and a primary focus in robotics research. The Ant Colony Optimization (ACO) algorithm is widely used for path planning. However, traditional ACO algorithms often suffer from slow convergence, low efficiency, and a tendency to get trapped in local optima. Thus, a multi-strategy adaptive improved ant colony algorithm (MSAACA) is proposed in this study. First, the initial pheromone is non-uniformly initialized based on the number of obstacles in adjacent nodes, and a new adaptive regulation of pheromone volatility is introduced. Secondly, by introducing the artificial potential field method and combining potential field attraction with distance information, the heuristic information function is enhanced. At the same time, the state transition rule is improved, and dynamic selection factors and dynamic adjustment factors are defined to update the selection ratio adaptively and dynamically adjust the influence of heuristic information, thus improving the global nature of the algorithm. Finally, the optimal path obtained by the algorithm is further optimized to reduce the number of inflection points effectively, leading to reduced running time and energy consumption of the mobile robot. The experimental results demonstrate that the algorithm exhibits high global search capability, significantly accelerated convergence speed, and improved working efficiency of mobile robots. This validates the effectiveness and superiority of the proposed algorithm.

To broaden the input range and stabilize the output of the full-bridge LLC resonant converter, a topology combining the switched inductor buck converter (SLD) with the full-bridge LLC converter is proposed. This topology not only provides the wide voltage regulation range of the source-level converter (SLD buck unit) but also keeps the advantages of the load-level converter (LLC resonant converter) such as zero-voltage switching (ZVS) and zero-current switching (ZCS). Firstly, the characteristics and advantages of the two stages of the cascade converter are analyzed based on the working principle of the cascade converter. Subsequently, a fixed-frequency control strategy based on the overall output voltage feedback is adopted. Specifically, when the LLC resonant converter consistently operates at its dual-resonant frequency, the converter achieves maximum efficiency, effectively compensating for the potential efficiency drop in the first stage SLD unit, thereby improving the overall system efficiency. Finally, theoretical analysis is validated on the Matlab/Simulink platform.

There is an increasing interest in processing data described by graph structures resulting in graph signal processing (GSP) and graph neural networks (GNN). One of the fundamental problems in GSP is graph learning, which uncovers the network topology from the signals measured at vertices. However, most existing approaches to graph learning merely look at the functional mapping from the smooth signals to the graph without considering signals' regressors. This paper proposes a novel algorithm (GLReg) for graph learning from smooth signals on the network and other regressors, considering the graph signals' smoothness and their relationship with other regressors. The theoretical derivation explains the proposed algorithm GLReg, and experimental tests on synthetic and real-world graphs show the effectiveness of our algorithm. The results of our study provide new insight into the graph learning model, and can be widely applied in the analysis of geographical, biomedical, and social networks.

A novel explicit model predictive control strategy is proposed for DC-DC converters in this study. Firstly, the state-space models of boost converter are established, both on-state and off-state respectively. By characteristic analysis of state-space functions, the control target is reconfigured as a linear parametric-varying (LPV) model with time-variant state matrices. Towards such target, then an explicit model predictive controller (MPC) is proposed in order to enhance transition dynamics. A novel prediction model is designed by utilizing of Tylor series. Moreover, estimated average states are given as one of the objective variables in cost function by measurement of state-space averaging (SSA) method. Consequently, the computational load of boost converter control system is alleviated adequately. At the end, two numerical simulations of voltage tracking are performed, one in waveform of slope and the other is sinusoidal. The results show remarkable performances of rapid response without any steady-state errors.

The fast-growing networked computing devices create many distributed systems and generate new signals on a large scale. Typical applications include peer-to-peer streaming of multimedia data, crowd- sourcing, and measurement by sensor networks. Therefore, the massive amount of networked data is a form of big data, calling for new data structures and algorithms different from classical ones suitable for small data sizes. We consider a vital data format for recording information from networked distributed systems: signals on graphs. A significant concern is to protect the privacy of large scales of signals when processed at third parties, such as cloud data centers. A de-facto solution is to outsource encrypted data before they arrive at the third-parties. We propose a novel and efficient privacy-protected outsourced denoising algorithm based on the information-theoretic secure multi-party computation (secure MPC). Among the operations of signals on graphs, denoising is useful before further meaningful processing can occur. We experiment with our algorithms in a popular platform of secure MPC and compare it with Paillier's homomorphic encryption approach. The results demonstrate a better efficiency of our approach.

This Full Paper for the Innovative Practice Category presents our method of successfully using micro videos as teaching assistance in the course of real-time operating system (RTOS). With the wide deployment of embedded systems, RTOS has become a very important topic in undergraduate computer engineering curriculum at many universities. Inspired by previous work of using videos in instructions, we implemented a series of micro videos for RTOS to facilitate students' understanding and practice. Different from the flipped-classroom method, our video assistants (VA) serve as a learning support rather than as a repetition or substitute to the course lectures. Students need to have normal lecture meetings to make full use of VAs. Each video is short, typically a few minutes. Each covers a topic such as summary of lectures, concept explanations, and software operation instructions. To assess the VAs, we analyze a number of factors and student attainment of the course learning outcomes. Student attainment of the course learning outcomes, as measured by the students' academic performance on exams, has demonstrated a positive effect of the VAs and that the impact is statistically significant. Additionally, students gave quite positive comments on the VAs in their evaluation of teaching.

Secure multi-party computation (MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. Among many secure MPC primitives, Shamir's secret sharing (SSS) has the advantages of having low complexity and information-theoretic security. However, SSS requires multiple honest participants and is susceptible to collusion attacks. In this paper, we provide a detailed analysis of different types of collusion attacks and propose novel mechanisms to deter such attacks in a fully distributed manner. Focusing on outsourced computing environments where secret data owners can collaborate on a public computing platform, we study collusion attacks using game theory. For those attacks where the thefts are detectable, we show that they can be effectively deterred by an explicit retaliation mechanism between data owners. The result is based on a comprehensive analysis that takes into account the cost of collusion, the privacy preference, and the associated uncertainty. For those attacks where the thefts cannot be detected, we expand the analysis to include the computing platform and provide deterrence through deceptive collusion requests as well as a novel cryptographic censorship protocol. The correctness and the privacy of the protocols are proved under the rational adversarial model. Our SSS-based protocols are shown to outperform the state-of-the-art garbled circuit systems, while our simulation results validate the proposed mechanism designs in deterring collusion.

Secure multi-party computation (MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. Information-theoretic secure MPC protocols, though more efficient than their computationally secure counterparts, require at least three computational parties and are prone to collusion attacks. Previous work has used mechanism designs to deter collusion. An important element missing is the consideration of how different players value privacy. In this paper, we provide a detailed analysis of possible outcomes under different privacy preferences based on the relative cost of collusion attacks over loss of privacy. We explicitly calculate the conditions under which honesty is the solution. Simulation results provide further evidence to demonstrate the validity of our mechanism design.

Many secure multiparty computation (SMC) protocols use Shamir's Secret Sharing (SSS) scheme as a building block. Unlike other cryptographic SMC techniques such as garbled circuits (GC), SSS requires no data expansion and achieves information theoretic security. A weakness of SSS is the possibility of collusion attacks from participants. In this paper, we propose an evolutionary game-theoretic (EGT) approach to deter collusion in SSS-based protocols. First, we consider the possibility of detecting the leak of secret data caused by collusion, devise an explicit retaliation mechanism, and show that the evolutionary stable strategy of this game is not to collude if the technology to detect the leakage of secret is readily available. Then, we consider the situation in which data-owners are unaware of the leakage and thereby unable to retaliate. Such behaviors are deterred by injecting occasional fake collusion requests, and detected by a censorship scheme that destroys subliminal communication. Comparison results show that our collusion-deterred SSS system significantly outperforms GC, while game simulations confirm the validity of our EGT framework on modeling collusion behaviors.