pwc-archive/papers-with-abstracts huggingface dataset:
Secrecy Performance of a Keyhole-based Multi-user System with Multiple Eavesdroppers
Authors: Parwez Alam, Ankit Dubey, Jules M. Moualeu, Telex M. N. Ngatched, Chinmoy Kundu
Date: 2025-04-03 00:00:00
Tasks: Scheduling

This paper investigates the secrecy performance of a keyhole-aided multi-user communication network in the presence of multiple eavesdroppers. The communication happens through the same keyhole for legitimate users and eavesdroppers. In this context, the secrecy performance is evaluated for a user scheduling technique by obtaining the exact closed-form expression of secrecy outage probability (SOP). Further, a simplified asymptotic SOP expression is derived assuming high signal-to-noise ratio (SNR) scenario for a better understanding of the impact of system parameters. The effect of the keyhole parameters, number of users, number of eavesdroppers, and threshold secrecy rate on the SOP performance are also investigated for the considered system model. In the high-SNR regime, the asymptotic SOP saturates to a constant value and does not depend on the keyhole parameter and the channel parameter of the source-to-keyhole channel.
Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection
Authors: Gabriele Greco, Carlo Cena, Umberto Albertin, Mauro Martini, Marcello Chiaberge
Date: 2025-04-02 00:00:00
Tasks: Anomaly Detection, Earth Observation, Fault Detection

Satellites are used for a multitude of applications, including communications, Earth observation, and space science. Neural networks and deep learning-based approaches now represent the state-of-the-art to enhance the performance and efficiency of these tasks. Given that satellites are susceptible to various faults, one critical application of Artificial Intelligence (AI) is fault detection. However, despite the advantages of neural networks, these systems are vulnerable to radiation errors, which can significantly impact their reliability. Ensuring the dependability of these solutions requires extensive testing and validation, particularly using fault injection methods. This study analyses a physics-informed (PI) real-valued non-volume preserving (Real NVP) normalizing flow model for fault detection in space systems, with a focus on resilience to Single-Event Upsets (SEUs). We present a customized fault injection framework in TensorFlow to assess neural network resilience. Fault injections are applied through two primary methods: Layer State injection, targeting internal network components such as weights and biases, and Layer Output injection, which modifies layer outputs across various activations. Fault types include zeros, random values, and bit-flip operations, applied at varying levels and across different network layers. Our findings reveal several critical insights, such as the significance of bit-flip errors in critical bits, that can lead to substantial performance degradation or even system failure. With this work, we aim to exhaustively study the resilience of Real NVP models against errors due to radiation, providing a means to guide the implementation of fault tolerance measures.
ESC: Erasing Space Concept for Knowledge Deletion
Authors: Tae-Young Lee, Sundong Park, Minwoo Jeon, Hyoseok Hwang, Gyeong-Moon Park
Date: 2025-04-03 00:00:00
Proceeding: CVPR 2025 1

As concerns regarding privacy in deep learning continue to grow, individuals are increasingly apprehensive about the potential exploitation of their personal knowledge in trained models. Despite several research efforts to address this, they often fail to consider the real-world demand from users for complete knowledge erasure. Furthermore, our investigation reveals that existing methods have a risk of leaking personal knowledge through embedding features. To address these issues, we introduce a novel concept of Knowledge Deletion (KD), an advanced task that considers both concerns, and provides an appropriate metric, named Knowledge Retention score (KR), for assessing knowledge retention in feature space. To achieve this, we propose a novel training-free erasing approach named Erasing Space Concept (ESC), which restricts the important subspace for the forgetting knowledge by eliminating the relevant activations in the feature. In addition, we suggest ESC with Training (ESC-T), which uses a learnable mask to better balance the trade-off between forgetting and preserving knowledge in KD. Our extensive experiments on various datasets and models demonstrate that our proposed methods achieve the fastest and state-of-the-art performance. Notably, our methods are applicable to diverse forgetting scenarios, such as facial domain setting, demonstrating the generalizability of our methods. The code is available at http://github.com/KU-VGI/ESC .
A Personal Privacy Preserving Framework: I Let You Know Who Can See What
Date: 2018-07-08 00:00:00
Tasks: Descriptive, Multi-Task Learning, Privacy Preserving

The booming of social networks has given rise to a large
volume of user-generated contents (UGCs), most of which are
free and publicly available. A lot of users’ personal aspects can be
extracted from these UGCs to facilitate personalized applications as
validated by many previous studies. Despite their value, UGCs can
place users at high privacy risks, which thus far remains largely
untapped. Privacy is defined as the individual’s ability to control
what information is disclosed, to whom, when and under what
circumstances. As people and information both play significant
roles, privacy has been elaborated as a boundary regulation process,
where individuals regulate interaction with others by altering the
openness degree of themselves to others. In this paper, we aim to
reduce users’ privacy risks on social networks by answering the
question of Who Can See What. Towards this goal, we present a
novel scheme, comprising of descriptive, predictive and prescriptive
components. In particular, we first collect a set of posts and extract
a group of privacy-oriented features to describe the posts. We then
propose a novel taxonomy-guided multi-task learning model to
predict which personal aspects are uncovered by the posts. Lastly,
we construct standard guidelines by the user study with 400 users
to regularize users’ actions for preventing their privacy leakage.
Extensive experiments on a real-world dataset well verified our
scheme.
Multi-Head Adaptive Graph Convolution Network for Sparse Point Cloud-Based Human Activity Recognition
Authors: Vincent Gbouna Zakka, Luis J. Manso, Zhuangzhuang Dai
Date: 2025-04-03 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Privacy Preserving

Human activity recognition is increasingly vital for supporting independent living, particularly for the elderly and those in need of assistance. Domestic service robots with monitoring capabilities can enhance safety and provide essential support. Although image-based methods have advanced considerably in the past decade, their adoption remains limited by concerns over privacy and sensitivity to low-light or dark conditions. As an alternative, millimetre-wave (mmWave) radar can produce point cloud data which is privacy-preserving. However, processing the sparse and noisy point clouds remains a long-standing challenge. While graph-based methods and attention mechanisms show promise, they predominantly rely on "fixed" kernels; kernels that are applied uniformly across all neighbourhoods, highlighting the need for adaptive approaches that can dynamically adjust their kernels to the specific geometry of each local neighbourhood in point cloud data. To overcome this limitation, we introduce an adaptive approach within the graph convolutional framework. Instead of a single shared weight function, our Multi-Head Adaptive Kernel (MAK) module generates multiple dynamic kernels, each capturing different aspects of the local feature space. By progressively refining local features while maintaining global spatial context, our method enables convolution kernels to adapt to varying local features. Experimental results on benchmark datasets confirm the effectiveness of our approach, achieving state-of-the-art performance in human activity recognition. Our source code is made publicly available at: https://github.com/Gbouna/MAK-GCN
Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection
Authors: Ruoyu Chen, Siyuan Liang, Jingzhi Li, Shiming Liu, Li Liu, Hua Zhang, Xiaochun Cao
Date: 2025-04-01 00:00:00

To develop a trustworthy AI system, which aim to identify the input regions that most influence the models decisions. The primary task of existing attribution methods lies in efficiently and accurately identifying the relationships among input-prediction interactions. Particularly when the input data is discrete, such as images, analyzing the relationship between inputs and outputs poses a significant challenge due to the combinatorial explosion. In this paper, we propose a novel and efficient black-box attribution mechanism, LiMA (Less input is More faithful for Attribution), which reformulates the attribution of important regions as an optimization problem for submodular subset selection. First, to accurately assess interactions, we design a submodular function that quantifies subset importance and effectively captures their impact on decision outcomes. Then, efficiently ranking input sub-regions by their importance for attribution, we improve optimization efficiency through a novel bidirectional greedy search algorithm. LiMA identifies both the most and least important samples while ensuring an optimal attribution boundary that minimizes errors. Extensive experiments on eight foundation models demonstrate that our method provides faithful interpretations with fewer regions and exhibits strong generalization, shows an average improvement of 36.3% in Insertion and 39.6% in Deletion. Our method also outperforms the naive greedy search in attribution efficiency, being 1.6 times faster. Furthermore, when explaining the reasons behind model prediction errors, the average highest confidence achieved by our method is, on average, 86.1% higher than that of state-of-the-art attribution algorithms. The code is available at https://github.com/RuoyuChen10/LIMA.
Gradient-Guided Multiscale Focal Attention Network for Remote Sensing Scene Classification
Authors: Yue Zhao, Maoguo Gong, A. K. Qin, Mingyang Zhang, Zhuping Hu, Tianqi Gao, Yan Pu
Date: 2024-07-16 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2024 7
Tasks: Aerial Scene Classification, Remote Sensing Image Classification, Scene Classification

Remote sensing scene classification (RSSC) aims to understand and analyze the semantic information at the scene level with complex geographical properties. Despite the profound success of advanced deep models in automatically capturing hierarchical embedding representations and the gradual dominant trend in RSSC, it still remains a great challenge to precisely focus on targets at variable scales that are considered highly relevant to the corresponding scene and separated from the background. Motivated by this recognition, in this article, we present the gradient-guided multiscale focal attention network (GMFANet) for RSSC to adaptively localize the representative multiscale semantic representation for complex scenes. In particular, a lightweight parameterized hierarchical multiscale attention (HMA) mechanism is proposed, which constitutes the main aim of adaptively enhancing physical detail and high-level semantic information at different layers, rather than regarding each scale set with equivalent insight, while eliminating redundant information inherent in conventional attention mechanisms. Subsequently, a gradient-guided spatial focused attention (GSFA) module is specifically designed to accurately localize critical regions at multiple scales, with the dynamic combination of gradient-activated reference attention map and prediction attention map from supervised information-based learning. In addition, a curriculum-driven dynamic attention fusion (CDAF) strategy is tailored to fuse the spatial attention above from easy to hard for avoiding from poor local optimum and decreasing the early learning ambiguity. Our extensive comparative experiments and ablation analyses implemented on real-world public RSSC datasets indicate that our approach achieves the state-of-the-art performance exactly. The code is available at https://github.com/bling2beyond/GMFANet.
Scaling Analysis of Interleaved Speech-Text Language Models
Authors: Gallil Maimon, Michael Hassid, Amit Roth, Yossi Adi
Date: 2025-04-03 00:00:00
Tasks: Transfer Learning

Existing Speech Language Model (SLM) scaling analysis paints a bleak picture. They predict that SLMs require much more compute and data compared to text, leading some to question the feasibility of training high-quality SLMs. However, modern SLMs are often initialised from pre-trained TextLMs using speech-text interleaving to allow knowledge transfer. This raises the question - Do interleaved SLMs scale more efficiently than textless-SLMs? In this paper we answer a resounding, yes! We conduct scaling analysis of interleaved SLMs by training several dozen and analysing the scaling trends. We see that under this setup SLMs scale more efficiently with compute. Additionally, our results indicate that the scaling-dynamics are significantly different than textless-SLMs, suggesting one should allocate notably more of the compute budget for increasing model size over training tokens. We also study the role of synthetic data and TextLM model families in unlocking this potential. Results suggest, that our scaled up model achieves comparable performance with leading models on speech semantic metrics while using less compute and data than other approaches. We open source models, samples, and data - https://pages.cs.huji.ac.il/adiyoss-lab/sims.
A Behaviour and Disease Model of Testing and Isolation
Authors: Matthew Ryan, Roslyn I. Hickson, Edward M. Hill, Thomas House, Valerie Isham, Dongni Zhang, Mick G. Roberts
Date: 2025-04-03 00:00:00
Tasks: Epidemiology

There has been interest in the interactions between infectious disease dynamics and behaviour for most of the history of mathematical epidemiology. This has included consideration of which mathematical models best capture each phenomenon, as well as their interaction, but typically in a manner that is agnostic to the exact behaviour in question. Here, we investigate interacting behaviour and disease dynamics specifically related to behaviours around testing and isolation. This epidemiological-behavioural interaction is of particular interest as, prospectively, it is well-placed to be informed by real-world data temporally monitoring test results and compliance with testing policy. To carry out our investigation we extend an existing "behaviour and disease" (BaD) model by incorporating the dynamics of symptomatic testing and isolation. We provide a dynamical systems analysis of the ordinary differential equations that define this model, providing theoretical results on its behaviour early in a new outbreak (particularly its basic reproduction number) and endemicity of the system (its steady states and associated stability criteria). We then supplement these findings with a numerical analysis to inform how temporal and cumulative outbreak metrics depend on the model parameter values for epidemic and endemic regimes. As the presented interdisciplinary modelling approach can accommodate further extensions (including, but not limited to, adding testing capacity, decay in behavioural effects and multiple pathogen variants), we hope that our work will encourage further modelling studies integrating specific measured behaviours and disease dynamics that may reduce the health and economic impacts of future epidemics.
AI-Powered Philosopher Robot
Authors: Vi Nhat Son
Date: 2025-04-04 00:00:00
Proceeding: Independent publication 2025 4

This project creates a robotic philosopher using the Mixtral-8x22B model, optimized with DeepSpeed, and interfaced with Raspberry Pi hardware. It explores existential questions through a living pulse of consciousness, memory, and evolution, supported by a networked community of entities.

Copyright: © 2025 Vi Nhat Son with Grok from xAI
License: Apache License, Version 2.0
Version: Philosopher 2.0 – Cosmic Enlightenment
Trapped by Expectations: Functional Fixedness in LLM-Enabled Chat Search
Authors: Jiqun Liu, Jamshed Karimnazarov, Ryen W. White
Date: 2025-04-02 00:00:00
Tasks: Ethics, Large Language Model

Functional fixedness, a cognitive bias that restricts users' interactions with a new system or tool to expected or familiar ways, limits the full potential of Large Language Model (LLM)-enabled chat search, especially in complex and exploratory tasks. To investigate its impact, we conducted a crowdsourcing study with 450 participants, each completing one of six decision-making tasks spanning public safety, diet and health management, sustainability, and AI ethics. Participants engaged in a multi-prompt conversation with ChatGPT to address the task, allowing us to compare pre-chat intent-based expectations with observed interactions. We found that: 1) Several aspects of pre-chat expectations are closely associated with users' prior experiences with ChatGPT, search engines, and virtual assistants; 2) Prior system experience shapes language use and prompting behavior. Frequent ChatGPT users reduced deictic terms and hedge words and frequently adjusted prompts. Users with rich search experience maintained structured, less-conversational queries with minimal modifications. Users of virtual assistants favored directive, command-like prompts, reinforcing functional fixedness; 3) When the system failed to meet expectations, participants generated more detailed prompts with increased linguistic diversity, reflecting adaptive shifts. These findings suggest that while preconceived expectations constrain early interactions, unmet expectations can motivate behavioral adaptation. With appropriate system support, this may promote broader exploration of LLM capabilities. This work also introduces a typology for user intents in chat search and highlights the importance of mitigating functional fixedness to support more creative and analytical use of LLMs.
APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers
Authors: Zhuguanyu Wu, Jiayi Zhang, Jiaxin Chen, Jinyang Guo, Di Huang, Yunhong Wang
Date: 2025-04-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: Quantization

Vision Transformers (ViTs) have become one of the most commonly used backbones for vision tasks. Despite their remarkable performance, they often suffer significant accuracy drops when quantized for practical deployment, particularly by post-training quantization (PTQ) under ultra-low bits. Recently, reconstruction-based PTQ methods have shown promising performance in quantizing Convolutional Neural Networks (CNNs). However, they fail when applied to ViTs, primarily due to the inaccurate estimation of output importance and the substantial accuracy degradation in quantizing post-GELU activations. To address these issues, we propose \textbf{APHQ-ViT}, a novel PTQ approach based on importance estimation with Average Perturbation Hessian (APH). Specifically, we first thoroughly analyze the current approximation approaches with Hessian loss, and propose an improved average perturbation Hessian loss. To deal with the quantization of the post-GELU activations, we design an MLP Reconstruction (MR) method by replacing the GELU function in MLP with ReLU and reconstructing it by the APH loss on a small unlabeled calibration set. Extensive experiments demonstrate that APHQ-ViT using linear quantizers outperforms existing PTQ methods by substantial margins in 3-bit and 4-bit across different vision tasks. The source code is available at https://github.com/GoatWu/APHQ-ViT.
APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding
Authors: Xinyu Yang, Tianqi Chen, Beidi Chen
Date: 2025-02-08 00:00:00
Tasks: RAG

Context-augmented generation (CAG) techniques, including RAG and ICL, require the efficient combination of multiple contexts to generate responses to user queries. Directly inputting these contexts as a sequence introduces a considerable computational burden by re-encoding the combined selection of contexts for every request. To address this, we explore the promising potential of parallel encoding to independently pre-compute and cache each context's KV states. This approach enables the direct loading of cached states during inference while accommodating more contexts through position reuse across contexts. However, due to misalignments in attention distribution, directly applying parallel encoding results in a significant performance drop. To enable effective and efficient CAG, we propose Adaptive Parallel Encoding ($\textbf{APE}$), which brings shared prefix, attention temperature, and scaling factor to align the distribution of parallel encoding with sequential encoding. Results on RAG and ICL tasks demonstrate that APE can preserve 98% and 93% sequential encoding performance using the same inputs while outperforming parallel encoding by 3.6% and 7.9%, respectively. It also scales to many-shot CAG, effectively encoding hundreds of contexts in parallel. Efficiency evaluation shows that APE can achieve an end-to-end 4.5$\times$ speedup by reducing 28$\times$ prefilling time for a 128K-length context.
Multi-Mission Tool Bench: Assessing the Robustness of LLM based Agents through Related and Dynamic Missions
Authors: Peijie Yu, Yifan Yang, Jinjian Li, Zelong Zhang, Haorui Wang, Xiao Feng, Feng Zhang
Date: 2025-04-03 00:00:00
Tasks: AI Agent

Large language models (LLMs) demonstrate strong potential as agents for tool invocation due to their advanced comprehension and planning capabilities. Users increasingly rely on LLM-based agents to solve complex missions through iterative interactions. However, existing benchmarks predominantly access agents in single-mission scenarios, failing to capture real-world complexity. To bridge this gap, we propose the Multi-Mission Tool Bench. In the benchmark, each test case comprises multiple interrelated missions. This design requires agents to dynamically adapt to evolving demands. Moreover, the proposed benchmark explores all possible mission-switching patterns within a fixed mission number. Specifically, we propose a multi-agent data generation framework to construct the benchmark. We also propose a novel method to evaluate the accuracy and efficiency of agent decisions with dynamic decision trees. Experiments on diverse open-source and closed-source LLMs reveal critical factors influencing agent robustness and provide actionable insights to the tool invocation society.