Hardware-assisted in-process compartmentalization reduces attack surface at low cost, but existing methods face practical challenges: inefficient dynamic permission management, weak metadata/instruction protection, and limited resource isolation. To tackle these problems, this paper proposes DASICS, a lightweight and efficient design of hardware-assisted in-process compartmentalization. DASICS partitions the process code segments into trusted and untrusted compartments and implements a user-mode protection runtime in the trusted compartment for dynamic permission management. It employs boundary registers to enforce dynamic access-control restrictions on instructions within different untrusted compartments. Additionally, it applies metadata access restriction, control-flow checks, and systemcall filtering for the untrusted compartments to achieve comprehensive protection. We implemented a hardware prototype of DASICS on the RISC-V XiangShan superscalar out-of-order processor and validated its effectiveness on FPGA. Our prototype increases less than 5% LUTs cost, and experimental results show that DASICS isolation incurs an average overhead of${6.02 \%}$on Memcached key-value store and 8.18% on NGINX webserver. DASICS project is publicly available at github.com/DASICS-ICT.

Physical Unclonable Functions (PUFs) are widely used in hardware security due to their inherent unclonability and randomness. However, the temporal instability of strong PUFs remains a barrier to their adoption in latest PUF-based cryptographic protocols, as it incurs significant overhead from error correction. In this paper, we propose PS-PUF, a novel architecture that leverages weak PUFs and cryptographically secure pseudorandom functions (PRFs) to construct a pseudo-strong PUF with stable and reproducible outputs. Our design includes a PRF for secure mapping, and a buffer to optimize performance in batch-access scenarios. We formally analyze the threat surface of PS-PUF and provide cryptographic security proofs showing resistance against modeling attacks. Implemented on the Genesys 2 FPGA, PS-PUF achieves at least 2.72× in batch scenarios with negligible hardware overhead and a maximum performance reduction of 10.7%, enabled by reusing the PRF module in integrated environments.

Organizations across industries increasingly rely on sharing data to drive collaboration, innovation, and business performance. However, securely and efficiently sharing live data across diverse platforms and adhering to varying governance requirements remains a significant challenge. Traditional approaches, such as FTP and proprietary in-data-warehouse solutions, often fail to meet the demands of interoperability, cost, scalability, and low overhead. This paper introduces Delta Sharing, an open protocol we developed in collaboration with industry partners, to overcome these limitations. Delta Sharing leverages open formats like Delta Lake and Apache Parquet alongside simple HTTP APIs to enable seamless, secure, and live data sharing across heterogeneous systems. Since its launch in 2021, Delta Sharing has been adopted by over 4000 enterprises and supported by hundreds of major software and data vendors. We discuss the key challenges in developing Delta Sharing and how our design addresses them. We also present, to our knowledge, the first large-scale study of production data sharing workloads offering insights into this emerging data platform capability.

Analysts who explore high-dimensional data usually want three answers at once: Which samples belong together, how close the resulting groups are, and who influences whom accordingly. Classical clustering provides only hard labels, hiding both inter-cluster affinities and correlation flow. We introduce Hamiltonian Cycle Clustering with Asymmetric Correlation HCC-AC, a framework that converts the clustering task into an interpretable map where structure and directionality are visible at a single glance. HCC-AC first learns soft memberships by optimizing a joint global–local loss, preserving manifold structure while turning each label into a probability. These probabilities drive a Hamiltonian-cycle embedding: cluster anchors are ordered by affinity and placed evenly on a circle; samples fall radially towards their most-likely anchor, so clusters, their similarities (arc lengths), and outliers emerge immediately. Directed arrows connect anchors, their lengths showing correlation strength, transforming the map into a legible narrative of influence. Experiments on five benchmark datasets demonstrate that HCC-AC improves the knowledge discovery in clustering, i.e., indexes the clustering results, flags outliers reliably, and uncovers correlation pathways.

Clustering aims to group unlabelled samples based on their similarities and is widespread in high-dimensional data analysis. However, most of the clustering methods merely generate pseudo labels and thus are unable to simultaneously present the similarities between different clusters and outliers. This paper proposes a new framework called High-dimensional Clustering onto Hamiltonian Cycle (HCHC) to solve the above problems. First, HCHC combines global structure with local structure in one objective function for deep clustering, improving the labels as relative probabilities, to mine the similarities between different clusters while keeping the local structure in each cluster. Then, the anchors of different clusters are sorted on the optimal Hamiltonian cycle generated by the cluster similarities and mapped on the circumference of a circle. Finally, a sample with a higher probability of a cluster will be mapped closer to the corresponding anchor. In this way, our framework allows us to appreciate three aspects visually and simultaneously - clusters (formed by samples with high probabilities), cluster similarities (represented as circular distances), and outliers (recognized as dots far away from all clusters). The theoretical analysis and experiments illustrate the superiority of HCHC.

By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters. Such nodes are typically found either at the interface between clusters (the undecided) or at their boundaries (the skeptics). Identifying these nodes is relevant in marketing applications like voter targeting, because the persons represented by such nodes are often more likely to be affected in marketing campaigns than nodes deeply within clusters. So far this identification task is not as well studied as other network analysis tasks like clustering, identifying central nodes, and detecting motifs. We approach this task by deriving novel geometric features from the network structure that naturally lend themselves to an interactive visual approach for identifying interface and boundary nodes.

Wireless charging technology greatly improves the flexibility and convenience of electric vehicles (EVs) charging. The alignment of coils directly affects the performance of the wireless charging system. The shift of the secondary coil leads to the decrease in transmission power and efficiency. Aiming to solve the abovementioned problems, in this article a mesh-based accurate positioning strategy for the EV wireless charging coils has been proposed. The strategy takes the center of the primary coil as the origin and divides the meshed reference points in the plane of the secondary coil. It is based on the voltage principle induced by the detection coils installed on the secondary coil. The coordinate calculation formula is derived by fitting the curve of the induction voltage with distance. The centimeter-level positioning of the secondary coil is realized in the X and Y directions of the horizontal plane. The proposed strategy is verified by simulations and experiments. The effectiveness and stability of the proposed method are verified by many simulations and experiments. Without considering the rotation of the coil, the maximum error is within 3 cm in a 5 cm mesh width. The positioning strategy helps to align the coils and improves the electrical transmission performance. It is also helpful for automatic parking, unmanned vehicle, and other industrial applications.