Hardware Trojans (HTs) embedded in integrated circuits often remain dormant and activate only under rare conditions, making run-time detection particularly challenging. The problem is more severe for stealthy designs whose small impedance perturbations produce weak electromagnetic signatures that elude conventional side-channel analysis. This work presents an on-chip EM backscattering framework for HT detection based on a Programmable Sensor Array (PSA). Instead of measuring switching-current emissions, the method captures impedance-modulated reflections from a continuous-wave excitation. The PSA is repurposed as a reconfigurable on-chip H-field receiver whose coil geometry can be dynamically tuned to improve magnetic coupling and spatial observability. We validate the approach on a fabricated TSMC 65 nm AES-128 test chip containing four digital Trojans and an analog A2 Trojan. The PSA achieves a 40–55 dB SNR improvement over external probes and enables reliable detection with fewer than five measured traces, including the A2 Trojan, which is difficult to observe using conventional EM side-channel analysis.

These years have witnessed remarkable progress in modeling user behaviour from personalized online services, especially knowledge graph-based recommendation systems. Meanwhile, more studies are focusing on aspects beyond recommendation performance, since such an observational data-driven paradigm is posing threats to both users and society in terms of trustworthiness. In fact, existing problem-oriented solutions still face significant challenges, as almost all of them suffer from the generality limitations to improve their trustworthiness in a uniform fashion. To address these issues, we propose a plug-and-playDebiasing framework forKnowledgeGraph-basedRecommendationSystems, also known as DiKGRS. Specifically, the Knowledge-augmented Pseudo-Samples Generation (KPSG) method, a novel data augmentation perspective, is proposed to explore more auxiliary information beyond observational user behaviors. Furthermore, the Debiasing Value Networks (DVN), is also developed to evaluate the reliability of generated pseudo-samples by modeling both the item popularity and user demographic bias in the platform. Moreover, an adaptive weighting coordination module is performed to coordinate the proposed DiKGRS framework and its backbones. Experimental results on four real-world datasets from different online service personalization scenarios have illustrated that the proposed framework can significantly improve the trustworthiness of existing knowledge graph-based recommendation systems. The code has been released public available at:https://github.com/alipay/A-Knowledge-augmented-Method-DiKGRS.

The advent and growing popularity of Virtual Reality (VR) and Mixed Reality (MR) solutions have revolutionized the way we interact with digital platforms. The cutting-edge gaze-controlled typing methods, now prevalent in high-end models of these devices, e.g., Apple Vision Pro, have not only improved user experience but also mitigated traditional keystroke inference attacks that relied on hand gestures, head movements and acoustic side-channels. However, this advancement has paradoxically given birth to a new, potentially more insidious cyber threat, GAZEploit.

Side-channel analysis has been proven effective at detecting hardware Trojans in integrated circuits (ICs). However, most detection techniques rely on large external probes and antennas for data collection and require a long measurement time to detect Trojans. Such limitations make these techniques impractical for run-time deployment and ineffective in detecting small Trojans with subtle side-channel signatures. To overcome these challenges, we propose a Programmable Sensor Array (PSA) for run-time hardware Trojan detection, localization, and identification. PSA is a tampering-resilient integrated on-chip magnetic field sensor array that can be re-programmed to change the sensors' shape, size, and location. Using PSA, EM side-channel measurement results collected from sensors at different locations on an IC can be analyzed to localize and identify the Trojan. The PSA has better performance than conventional external magnetic probes and state-of-the-art on-chip single-coil magnetic field sensors. We fabricated an AES-128 test chip with four AES Hardware Trojans. They were successfully detected, located, and identified with the proposed on-chip PSA within 10 milliseconds using our proposed cross-domain analysis.

In the realm of human mobility data analysis, a multitude of constraints result in the publication of sparse, non-uniform implicit trajectories without explicit location information, such as coordinates. Researchers have dedicated substantial efforts towards trajectory recovery, aiming to densify trajectories and gain a more comprehensive understanding of human mobility. However, existing trajectory recovery methods focus on explicit trajectories, and require extensive historical data to capture users' mobility patterns. Nevertheless, implicit trajectories are usually more sparse than explicit trajectories. Addressing these challenges, we propose TrajBERT, an innovative BERT-based trajectory recovery method with spatial-temporal refinement. TrajBERT employs the Transformer encoder to learn mobility patterns bi-directionally and enhances the predictions by cross-stage temporal refinement. Subsequently, we design an output layer with global spatial refinement with a novel spatial-temporal aware loss function. To evaluate the performance of TrajBERT, we conduct a series of experiments on real-world datasets. Remarkably,TrajBERT yields at least 8.2% performance improvement compared to the state-of-the-art trajectory recovery approachs. Furthermore, TrajBERT successfully mitigates the cold start problem commonly experienced with new users lacking historical trajectories. It also shows superior robustness when faced with extremely sparse trajectories, thus demonstrating its potential as a practical tool in the field of human mobility analysis.

Hardware Trojans (HTs) are malicious circuits planted in Integrated Circuits (ICs). Multiple techniques using Side-Channel signals to detect HTs have been developed over the past decade. However, most of this research focuses on HT detection. Few of them explore the possibility of either identifying different Hardware Trojans implemented inside ICs or detecting inactive HTs. We propose a runtime EM side-channel analysis workflow (HT-EMIS) that uses a convolutional neural network to address the shortcomings above. By analyzing EM side-channel leakage from an FPGA, our tool can identify known types of HTs implemented inside a design and reports whether they are inactive or active with 100% accuracy. Additionally, we are able to successfully detect new unseen HTs with this model in 98.7% of test cases, due to the fact that HTs inserted at the Register Transfer Level with similar triggers and payloads often have similar effects on a floorplan, and thus the EM radiation of a device.

In recent years, traffic flow prediction has attracted more and more interest from both academia and industry since such information can provide effective guidance for traffic management or driving planning and enhance traffic safety and efficiency. But due to the complicated spatial-temporal dependence in actual roads and the limitation of intersection monitoring equipment, there are still many challenges in spatial-temporal traffic flow prediction. In this paper, we propose a novel hierarchical traffic flow prediction protocol based on spatial-temporal graph convolutional network (ST-GCN), which incorporates both spatial and temporal dependence of intersection traffic to achieve a more accurate traffic flow prediction. Different from existing works, our proposed protocol with the Adjacent-Similar algorithm can also effectively predict the traffic flow of the intersections without historical data. Experiments based on practical traffic data of the city of Qingdao, China demonstrate that our proposed ST-GCN-based traffic flow prediction protocol outperforms the state-of-the-art baseline models. Moreover, as for the intersections without historical data, we can also obtain a good prediction accuracy.