Predicting online video popularity faces a critical challenge: prediction drift, where models trained on historical data rapidly degrade due to evolving viral trends and user behaviors. To address this temporal distribution shift, we propose an Anchored Multi-modal Clustering and Feature Generation (AMCFG) framework that discovers temporally-invariant patterns across data distributions. Our approach employs multi-modal clustering to reveal content structure, then leverages Large Language Models (LLMs) to generate semantic Anchor Features-high-level concepts such as audience demographics, content themes, and engagement patterns-that transcend superficial trend variations. These semantic anchors, combined with cluster-derived statistical features, enable prediction based on stable principles rather than ephemeral signals. Experiments demonstrate that AMCFG significantly enhances both predictive accuracy and temporal robustness, achieving superior performance on out-of-distribution data and providing a viable solution for real-world video popularity prediction.

Shadows are a common factor degrading image quality. Single-image shadow removal (SR), particularly under challenging indirect illumination, is hampered by non-uniform content degradation and inherent ambiguity. Consequently, traditional methods often fail to simultaneously recover intra-shadow details and maintain sharp boundaries, resulting in inconsistent restoration and blurring that negatively affect both downstream applications and the overall viewing experience. To overcome these limitations, we propose the DenseSR, approaching the problem from a dense prediction perspective to emphasize restoration quality. This framework uniquely synergizes two key strategies: (1) deep scene understanding guided by geometric-semantic priors to resolve ambiguity and implicitly localize shadows, and (2) high-fidelity restoration via a novel Dense Fusion Block (DFB) in the decoder. The DFB employs adaptive component processing-using an Adaptive Content Smoothing Module (ACSM) for consistent appearance and a Texture-Boundary Recuperation Module (TBRM) for fine textures and sharp boundaries-thereby directly tackling the inconsistent restoration and blurring issues. These purposefully processed components are effectively fused, yielding an optimized feature representation preserving both consistency and fidelity. Extensive experimental results demonstrate the merits of our approach over existing methods. Our code can be available on https://github.com/VanLinLin/DenseSR

Social media popularity (SMP) prediction is a complex task involving multi-modal data integration. While pre-trained vision-language models (VLMs) like CLIP have been widely adopted for this task, their effectiveness in capturing the unique characteristics of social media content remains unexplored. This paper critically examines the applicability of CLIP-based features in SMP prediction, focusing on the overlooked phenomenon of semantic inconsistency between images and text in social media posts. Through extensive analysis, we demonstrate that this inconsistency increases with post popularity, challenging the conventional use of VLM features. We provide a comprehensive investigation of semantic inconsistency across different popularity intervals and analyze the impact of VLM feature adaptation on SMP tasks. Our experiments reveal that incorporating inconsistency measures and adapted text features significantly improves model performance, achieving an SRC of 0.729 and an MAE of 1.227. These findings not only enhance SMP prediction accuracy but also provide crucial insights for developing more targeted approaches in social media analysis.

Abstract The more recent advances in network communication and cloud computing technologies have the potential of significantly improving traveling experience by providing value‐added services, such as multimedia on demand (MoD), in vehicular ad hoc networks, where the involved vehicles are provided with enough communication, storage, and computing capabilities. In this scenario, despite many consolidated mobile cloud solutions and pay‐per‐view systems have been developed and widely deployed in public transportation, the security and privacy of mobile users, mainly concerning the association between users' identities and requested multimedia contents, still presents several open challenges. Mainly, providing pay‐per‐view services in vehicular clouds while protecting the passenger's anonymity and simultaneously ensuring the robustness of the payment system has become an important issue.Accordingly, we present a novel smart card‐based MoD payment solution, to be used in mobile cloud‐empowered public transportation systems, that not only guarantees the passenger's anonymity but also uses a personal trusted device to protect the passenger's sensitive information so that he can enjoy the multimedia contents during the long hours of travel. In the future, such scheme can become common practice on MoD‐related equipment in vehicular ad hoc networks, thereby enhancing the competitiveness of public transport companies. Copyright © 2016 John Wiley & Sons, Ltd.

Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different news categories on a specific day by utilizing the influence propagation among communities and news categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers' attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.

Recently, Hsiang and Shih proposed remote user authentication scheme using smart cards, they claims that their schemes defended against parallel session attack, and password guessing attacks, In this paper, we show that Hsiang and Shih's schemes are still vulnerable to off-line password guessing attacks and undetectable on-line password guessing attacks. Notably, problems remain in situations where the user loses a smart card. To remedy these flaws, this paper proposes an improvement on Hsiang and Shih's remote user authentication schemes using smart cards.