Wi-Fi-based device-free localization and tracking demonstrates advantages including strong privacy protection, low cost, and wide coverage. Current Wi-Fi device-free localization methods primarily fall into two categories: model-driven and data-driven approaches. Compared to model-driven methods, data-driven device-free localization based on Wi-Fi Channel State Information (CSI) offers easier deployment and better adaptability, yet remains constrained by the challenge of fingerprint collection. Conventional Wi-Fi device-free systems can only obtain discrete positional fingerprints, lacking dynamic and continuous fingerprint acquisition, which leads to limited localization and tracking accuracy. Compared to radar, LiDAR, and wearable sensors, cameras can acquire target positional information at lower costs. In this paper, we propose VilaFi, a Visual-label-driven device-free localization system using Wi-Fi. Our monocular camera-based framework delivers relatively precise and continuous positional labeling, enabling the acquisition of rich sequential Wi-Fi CSI fingerprints. We implement device-free target localization and tracking using a hybrid architecture deep neural network. The system achieves device-free tracking accuracies of approximately 0.16 m and 0.2 m in Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments covering 38.8 m2and 78.6m2areas respectively, outperforming state-of-the-art approaches.

The widespread deployment of deep learning-based face recognition systems raises significant concerns regarding the security and privacy of their stored facial templates. Unlike passwords, a compromised template is irrevocable and can be inverted to reconstruct the user’s original biometric data, leading to permanent privacy loss. To address this problem, various biometric cryptosystems based on fuzzy commitment(FC) have been introduced. However, FC suffers from two critical challenges when applied to facial templates: the real-valued templates and their high intra-class variations, which typically exceed the error-correction capacity of FC. Our scheme addresses these issues by binarizing the deep features via Sliding Window Grouping MinHash, subsequently applying a Fisher score-based mask to select stable bits, ultimately minimizing intra-class dissimilarity to enable robust matching with LDPC codes. Extensive experiments were conducted on publicly available benchmark datasets, including LFW, ColorFERET, CASIA-WebFace, and FEI. The results demonstrate that our scheme achieves high authentication accuracy while also fulfilling the critical security requirements for template protection: irreversibility, revocability, and unlinkability.

The multi-agent characteristics of integrated energy systems are becoming increasingly prominent, rendering the energy management problems more intricate. Additionally, the distributed agents are faced with challenges from the communication layer, such as packet losses and privacy disclosure issues. Therefore, a privacy-preserving neurodynamic-based optimization strategy considering packet losses is proposed in this article. A privacy-preserving communication model is constructed based on differential privacy (DP). Unlike traditional DP methods, the privacy preservation mechanism employed in this article can achieve a quantified high level of privacy while ensuring the solution accuracy of the optimization algorithm. Moreover, communication packet loss models based on both the two-state Markov process and the Bernoulli process are established. The proposed fully distributed scheme only requires communication between adjacent agents. The efficiency of the neurodynamic-based approach, the high-level privacy preservation, and the robustness against communication packet loss are demonstrated through several case studies.

Graph Database Management Systems (GDBMSs) store data in a graph format, facilitating rapid querying of nodes and relationships. This structure is particularly advantageous for applications like social networks and recommendation systems, which often involve frequent writing operations—such as adding new nodes, creating relationships, or modifying existing data—that potentially introduce bugs. However, existing GDBMS testing approaches tend to overlook these writing functionalities, failing to detect bugs arising from such operations. In this paper we present GraspDB, the first metamorphic testing approach specifically designed to identify bugs related to writing operations in graph database systems. GraspDB employs the Graph-State Persistence oracle, which is based on the Labeled Property Graph Isomorphism (LPG-Isomorphism) and Labeled Property Subgraph Isomorphism (LPSG-Isomorphism) relations. We also develop three classes of mutation rules aimed at engaging more diverse writing-related code logic. GraspDB has successfully detected 77 unique, previously unknown bugs across four popular open source graph database engines, among which 58 bugs are confirmed by developers, 43 bugs have been fixed and 31 are related to writing operations.

Because of the development of the technology, people's material and cultural conditions is developing faster and faster, life conditions is getting better and better living conditions. However, the improvement of inherent virtue of people has failed to match the growing material and cultural criteria. Many parents are busy in work, leading to many children cannot obtain a culture etiquette and upbringing at an early age, children cannot treat people how to do things correctly, this is more harm than good for children's emotional intelligence development and future life. But the growing material conditions has created a possibility for the popularity of computer games, children can come into contact with computers and other modern electronic products when they are very small, which also getting children premature contact with, even hooked on computer games. So game developers can combine computer games with children etiquette education, let the children play games, but also by the influence of culture and etiquette. This game is arranged two scenes which are sitting room scene and bus scene, the player can choose a scene casually. After entering the game scenes, there will be a plot unfolds, players need to react differently and politely according to different scenarios before clearance. Meanwhile, the game also leave interface for future game developers, the child's answer can be speech recognized in the future.