The problem of scalar topological interference management with heterogeneous erasures (STIME) is formalized, by adopting a one-shot model that favors ultra low latency communication, partially connected interference network topologies that are motivated by directional transmission, limited channel knowledge at the transmitters that is restricted to the network connectivity, erasures that model sporadic disruptions that are detectable at the receiver, and a performance metric that is closer to Hamming than Shannon, seeking the shortest multiuser-code that is robust to a set of specified erasure patterns for each user. In the absence of erasures, STIME reduces to the scalar TIM (STIM) problem which, based on prior work, corresponds to scalar index coding, and as such admits a well known minrank optimal solution. By the same token, STIME can be viewed as essentially a scalar index coding problem with heterogeneous erasures at each receiver. The optimal solution to STIME is bounded within a factor of 2 based on its connections to STIM for heterogeneous erasures. The bounds are shown to be tight in several cases, e.g., for topologies where alignment graphs have no internal conflicts, for neighboring interference models, and for arbitrary topologies if the erasure thresholds are identical across users. A mapping from any STIME setting to a corresponding STIM setting is identified such that the two problems have the same solution.

Audio-driven talking face video generation has attracted increasing attention due to its huge industrial potential. Some previous methods focus on learning a direct mapping from audio to visual content. Despite progress, they often struggle with the ambiguity of the mapping process, leading to flawed results. An alternative strategy involves facial structural representations (e.g., facial landmarks) as intermediaries. This multi-stage approach better preserves the appearance details but suffers from error accumulation due to the independent optimization of different stages. Moreover, most previous methods rely on generative adversarial networks, prone to training instability and mode collapse. To address these challenges, our study proposes a novel landmark-based diffusion model for talking face generation, which leverages facial landmarks as intermediate representations while enabling end-to-end optimization. Specifically, we first establish the less ambiguous mapping from audio to landmark motion of lip and jaw. Then, we introduce an innovative conditioning module called TalkFormer to align the synthesized motion with the motion represented by landmarks via differentiable cross-attention, which enables end-to-end optimization for improved lip synchronization. Besides, TalkFormer employs implicit feature warping to align the reference image features with the target motion for preserving more appearance details. Extensive experiments demonstrate that our approach can synthesize high-fidelity and lip-synced talking face videos, preserving more subject appearance details from the reference image.

In recent years, neural networks (NN) have achieved remarkable performance improvement in text classification due to their powerful ability to encode discriminative features by incorporating label information into model training. Inspired by the success of NN in text classification, we propose a pseudo-supervised neural network approach for text clustering. The neural network is trained in a supervised fashion with pseudo-labels, which are provided by the cluster labels of pre-clustering on unsupervised document representations. To enhance the quality of pseudo-labels, a consensus analysis is employed to select training samples for the neural network. The experimental results demonstrate that the proposed approach can improve the clustering performance significantly.

Abstract People‐centric sensing (PCS), which collects information closely related to human activity and interactions in societies, is stepping into a flourishing time. Along with its great benefits, PCS poses new security challenges such as data integrity and participant privacy. Hierarchical identity‐based signature (HIBS) scheme can efficiently provide high‐integrity messaging, secure communication, and privacy protection to PCS. However, key escrow problem and low computation efficiency primarily hinder the adoption of HIBS scheme. In this paper, we propose an escrow‐free online/offline HIBS scheme for securing PCS. By utilizing user‐selected‐secret signing algorithm and splitting the signing phase into online and offline procedures, our scheme solves the key escrow problem and achieves high scheme performance. Copyright © 2016 John Wiley & Sons, Ltd.

For Securing BGP Protocol, this paper proposes an identity-based private key infrastructure (iPKI) for managing self-attested IP (sIP) addresses. An sIP address endows the current IP address self-attested characteristic, which does not rely on any credential based PKI. Based on the sIP address, we design the In-Band Self Origin Verification (IBSOV) protocol and self Route Origin Authorization (sROA) to provide a lightweight origin verification for the BGP protocol, which has a much lower overhead than the existing works.