We propose a novel system, GeoProblem Factory, designed to effectively generate high-quality geometry problems for intelligent education. The system enables to efficiently produce batches of geometry problems for teachers and students, either to save time and manual effort or to support personalized learning. Generating geometry problems is particularly challenging, as it requires ensuring both solvability and controllability from a pedagogical perspective. To address these issues, we adopt a state-of-the-art pipeline method based on a symbolic deduction engine and develop a visual interaction demo. This demo allows users to easily refine the generated problems through visual operations. It provides two modes for inputting controllable information: specifying knowledge points or supplying a reference problem. Moreover, the system can automatically generate a preliminary geometric diagram corresponding to each problem for further refinement. Through human–machine interaction, the system can more efficiently produce high-quality geometry problems than ever.

The increasing demands for system complexity and robustness have prompted the integration of temporal logic into Multi-Agent Reinforcement Learning (MARL) to address tasks with non-Markovian properties. However, incorporating non-Markovian properties introduces additional computational complexities, as agents are required to integrate historical data into their decision-making process. Also, optimizing strategies within a multi-agent environment presents significant challenges due to the exponential growth of the state space with the number of agents. In this study, we introduce an innovative hierarchical MARL framework that synthesizes temporal equilibrium strategies through parity games and subsequently encodes them as individual reward machines for MARL coordination. More specifically, we reduce the strategy synthesis problem into an emptiness problem concerning parity games with optimized states and transitions. Following this synthesis step, the temporal equilibrium strategy is decomposed into individual reward machines for decentralized MARL. Theoretical proofs are provided to verify the consistency of the Nash equilibrium between the parallel composition of decomposed strategies and the original strategy. Empirical evidence confirms the efficacy of the proposed synthesis technique, showcasing its ability to reduce state space compared to the state-of-the-art tool. Furthermore, our study highlights the superior performance of the distributed MARL paradigm over centralized approaches when deploying decomposed strategies.

Batch steganography regarding to image-selection and payload-allocation has gained increasing attention due to the secure demanding of data hiding of real scenario. However, due to the predefined selection mechanism, the chosen images are always complex which means that the diversity of the selected cover set is finite. In this paper, we develop a diverse and secure batch steganography scheme including the model-based generation and double-layered payload assignment. To construct the diverse image set, we use the Kullback-Leibler (KL) divergence to quantify the diversity increment and, relying on steganographic distortion, we select multiple image subsets (class) to create the diverse cover set in which each subset is modelled as the normal distribution with proper model parameters. Depending on the distortion of image subset, we assign the payload into all subsets with between-class allocation. Moreover, for the assigned payload of each subset, we introduce the linear model to achieve the within-class allocation. Finally, we obtain a diverse cover set along with suitable payload. Extensive experiments demonstrate the practicality of the proposed method in diversity and, compared with other selection methods, exhibit higher security on multiple steganalytic tools.

In the field of motion monitoring in smart home, the 5G technology can be applied to Internet of Things systems for facilitating our daily life. In this paper, a comprehensive study on the fall detection system based on 5G network is presented. Starting with analyzing the moving stability, a wearable foot pressure measurement system is devised. Furthermore, the center of pressure during moving is computed by using the pressure data. Besides, the signal transmitting security issue is also considered. The physical layer security authentication and the cross-layer encryption are employed and integrated within the security strategy. As a case study, we evaluate the proposed method on fall detection tasks in smart home and the experimental results establish strong evidence of a satisfying performance.

The seven papers in this special section focus on advanced machine learning methods for brain machine interfacing. Particular emphasis is on novel theories and methods using transfer learning and deep learning proposed for Brain-Machine Interfacing (BMI) or Brain-Computer Interfacing (BCI). Our purpose is to review the new progress and achievements on transfer learning, deep learning, and their applications in BMI or BCI in recent years.

Understanding temporal dependencies of videos is fundamental for vision problems, but deep learning–based models are still insufficient in this field. In this article, we propose a novel deep multiplicative neural network (DMNN) for learning hierarchical long-term representations from video. The DMNN is built upon the multiplicative block that remembers the pairwise transformations between consecutive frames using multiplicative interactions rather than the regular weighted-sum ones. The block is slided over the timesteps to update the memory of the networks on the frame pairs. Deep architecture can be implemented by stacking multiple layers of the sliding blocks. The multiplicative interactions lead to exact, rather than approximate, modeling of temporal dependencies. The memory mechanism can remember the temporal dependencies for an arbitrary length of time. The multiple layers output multiple-level representations that reflect the multi-timescale structure of video. Moreover, to address the difficulty of training DMNNs, we derive a theoretically sound convergent method, which leads to a fast and stable convergence. We demonstrate a new state-of-the-art classification performance with proposed networks on the UCF101 dataset and the effectiveness of capturing complicate temporal dependencies on a variety of synthetic datasets.

Biometric identification has verified its effectiveness in personal identity verification because of the uniqueness and noninvasion. In this research, we tend to apply the detection of biometric information to a remote sensing system for the purpose of security area monitoring. Our system is established by collecting signals from the coming individuals via the remote measurement in the specific condition where both kinds of data are detected to determine the identity. Specifically, the measuring of gait signals and facial images is integrated to provide a way of improving the detection accuracy and the robustness. In addition, the fuzzy association rule (FAR) is employed for data analysis in line with the outcomes of different methods. As such, the signals are integrated and transmitted for further processing and remote identification. Experiments are conducted to demonstrate the capability of the proposed system. With the training data increases, a high detection accuracy of 95.2% is obtained, which makes it a promising basis for the realization of remote identity verification.

Abstract The development of wireless technologies and the popularity of mobile devices is responsible for generating large amounts of trajectory data for moving objects. Trajectory datasets have spatiotemporal features and are a rich information source. The mining of trajectory data can reveal interesting patterns of human activities and behaviors. However, trajectory data can also be exploited to disclose users’ privacy information, e.g., the places they live and work, which could be abused by a malicious user. Therefore, it is very important to protect the users’ privacy before publishing any trajectory data. While most previous research on this subject has only considered the privacy protection of stay points, this paper distinguishes itself by modeling and processing semantic trajectories, which not only contain spatiotemporal data but also involve POI information and the users’ motion modes such as walking, running, driving, etc. Accordingly, in this research, semantic trajectory anonymizing based on the k-anonymity model is proposed that can form sensitive areas that contain k − 1 POI points that are similar to the sensitive points. Then, trajectory ambiguity is executed based on the motion modes, road network topologies and road weights in the sensitive area. Finally, a similarity comparison is performed to obtain the recordable and releasable anonymity trajectory sets. Experimental results show that this method performs efficiently and provides high privacy levels.

The advancement of wireless communication technology and sensor technology, as well as the popularity of portable devices has promoted the location-based services (LBS) to a new stage, while new spatial-temporal query techniques and scenarios have emerged continuously. One of the interesting scenarios is the spatial skyline query that retrieves those points of P not dominated by any other point in P with respect to their derived spatial attributes. However, being point-based, the former query method is not suitable for privacy protection. In this article, we propose a range-based spatial skyline query solution, including two algorithms, namely VRSSA for static query points, and DDOAA and DDODA for the dynamic change of the data points queried.