Deceptive reviews are fabricated feedback designed to artificially manipulate the perceived quality of products. Within modern e-commerce ecosystems, these reviews remain a critical governance challenge. Despite advances in review-level and graph-level detection methods, two pivotal limitations remain: inadequate generalization and lack of interpretability. To address these challenges, we propose JARVIS, a framework providing Judgment via Augmented Retrieval and eVIdence graph Structures. Starting from the review to be evaluated, it retrieves semantically similar evidence via hybrid dense–sparse multimodal retrieval, expands relational signals through shared entities, and constructs a heterogeneous evidence graph. The large language model then performs evidence-grounded adjudication to produce interpretable risk assessments. Offline experiments demonstrate that JARVIS enhances performance on our constructed review dataset, achieving a precision increase from 0.953 to 0.988 and a recall boost from 0.830 to 0.901. In the production environment, our framework achieves a 27% increase in the recall volume and reduces manual inspection time by 75%. Furthermore, the adoption rate of the model-generated analysis reaches 96.4%.

Ensuring the ethical deployment of text-to-image models requires effective techniques to prevent the generation of harmful or inappropriate content. While concept erasure methods offer a promising solution, existing finetuning-based approaches suffer from notable limitations. Anchor-free methods risk disrupting sampling trajectories, leading to visual artifacts, while anchor-based methods rely on the heuristic selection of anchor concepts. To overcome these shortcomings, we introduce a finetuning framework, dubbed ANT, which Automatically guides deNoising Trajectories to avoid unwanted concepts. ANT is built on a key insight: reversing the condition direction of classifier-free guidance during mid-to-late denoising stages enables precise content modification without sacrificing early-stage structural integrity. This inspires a trajectory-aware objective that preserves the integrity of the early-stage score function field-which steers samples toward the natural image manifold-without relying on heuristic anchor concept selection. For single-concept erasure, we propose an augmentation-enhanced weight saliency map to precisely identify the critical parameters that most significantly contribute to the unwanted concept, enabling more thorough and efficient erasure. For multi-concept erasure, our objective function offers a versatile plug-and-play solution that significantly boosts performance. Extensive experiments demonstrate that ANT achieves state-of-the-art results in both single and multi-concept erasure, delivering high-quality, safe outputs without compromising the generative fidelity. Code is available at https://github.com/lileyang1210/ANT

Videos offer rich audiovisual information that can support people in performing activities of daily living (ADLs), but they remain largely inaccessible to blind or low-vision (BLV) individuals.In cooking, BLV people often rely on non-visual cues-such as touch, taste, and smell-to navigate their environment, making it difficult to follow UIST '25, September 28-October 01, 2025, Busan, Republic of Korea Ning et al.the predominantly audiovisual instructions found in video recipes.To address this problem, we introduce Aroma, an AI system that provides timely responses to the user based on real-time, contextaware assistance by integrating non-visual cues perceived by the user, a wearable camera feed, and video recipe content.Aroma uses a mixed-initiative approach: it responds to user requests while also proactively monitoring the video stream to offer timely alerts and guidance.This collaborative design leverages the complementary strengths of the user and AI system to align the physical environment with the video recipe, helping the user interpret their current state and make sense of the steps.We evaluated Aroma through a study with eight BLV participants and offered insights for designing interactive AI systems to support BLV individuals in performing ADLs.

The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content. In this paper, we introduce MACE, a finetuning framework for the task of MAss Concept Erasure. This task aims to prevent models from generating images that embody unwanted concepts when prompted. Existing concept erasure methods are typically restricted to handling fewer than five concepts simultaneously and struggle to find a balance between erasing concept synonyms (generality) and maintaining unrelated concepts (specificity). In contrast, MACE differs by successfully scaling the erasure scope up to 100 concepts and by achieving an effective balance between generality and specificity. This is achieved by leveraging closed-form cross-attention refinement along with LoRA finetuning, collectively eliminating the information of undesirable concepts. Furthermore, MACE integrates multiple LoRAs without mutual interference. We conduct extensive evaluations of MACE against prior methods across four different tasks: object erasure, celebrity erasure, explicit content erasure, and artistic style erasure. Our results reveal that MACE surpasses prior methods in all evaluated tasks. Code is available at https://github.com/Shilin-LU/MACE.

Object detection in aerial images is a challenging task because of the complex background and various orientations of the objects. Currently, many detectors have made significant progress in improving mAP scores, but they do not achieve the improvements in efficiency and model size. In this letter, we propose a detector for resource-limited satellite network, namely, the simple convolutional neural networks (simple-CNNs), which can be directly applied in actual application scenarios using small sample data. Specifically, a new sample 16-layer network is devised to reduce the model size. Meanwhile, we calculate a new anchor for ten classes and carefully design a change-IOU Loss (CI-Loss) function for horizontal bounding box detection to improve the accuracy. Extensive experiments on two remote sensing public data sets (NWPU VHR-10 and part of DIOR) show good performance for the efficiency of our detector. In particular, our simple-CNN achieves 68.9 mAP on the NWPU VHR-10 test-dev data set with 90 M parameters and a test speed of 72 ms, and it also has good performance on a part of DIOR.

Thanks to recent advances in hierarchical video coding and overlay network, layered overlay multicast has emerged as an important solution for video streaming over heterogeneous network. In this paper, we formulate it into a joint network flow control and performance optimization problem where adaptive layer rates are determined through a greedy algorithm close to optimal. The overlay network is constrained by a practical two-level hierarchical overlay model where the bandwidth sharing is imposed on underlying edge-bottleneck links. To maintain less computational complexity and avoidance of global information, the M-layer maximization problem is transformed into multiple one-layer minimization subproblems. In turn, a fully distributed algorithm is developed by decomposing the one-layer minimization problem into both inequality constrained transportation subproblem and the shortest path subproblem by using Lagrangian duality. We demonstrate that the distributed algorithm converges quickly and attains effective performance in both static and dynamic networks.

Nowadays with time series accounting for an increasingly large fraction of world's supply of data, there has been an explosion of interest in mining time series data. This paper proposes a multivariate time series classification model which is both effective in classifier's accuracy and comprehensibility. It is composed of two stages: a supervised clustering for pattern extraction and soft discretization decision forest. In supervised clustering, some real time series instances from the training dataset will be selected as class dedicated patterns. While in decision forest, the rule induction helps to improve the knowledge acquisition of the classifier. In addition, soft discretization would further improve the accuracy and comprehensibility of the classifier.

In this paper, we seek the optimal overlay multicast performance for scalable video streaming. To adapt to layered multicast with multiple paths, a new metric layer stretch is introduced, measuring video layer's dissemination latency along the overlay. Multi-path routing, network coding based layer subscription and congest control are jointly optimized, formulating a linear programming to minimize the average layer stretch over all receivers. It not only solves the issue of inter-layer dependency of scalable video coding, but allows each receiver to determine the required video quality. Moreover, a heuristic algorithm that can be implemented in a distributed manner is provided to approximate the optimal solution. Simulation results validate the network performance of the proposed scheme.