Self-adaptive cyber defense for sustainable IoT: A DRL-based IDS optimizing security and energy efficiency
Saeid Jamshidi
Ashkan Amirnia
Amin Nikanjam
Kawser Wazed Nafi
Samira Keivanpour
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
Shamsuddeen Hassan Muhammad
Nedjma OUSIDHOUM
Idris Abdulmumin
Seid Muhie Yimam
Jan Philip Wahle
Terry Lima Ruas
Meriem Beloucif
Christine de Kock
Tadesse Belay
Ibrahim Ahmad
Nirmal Surange
Daniela Teodorescu
Alham Fikri Aji
Felermino Ali
Vladimir Araujo
Abinew Ayele
Oana Ignat
Alexander Panchenko
Yi Zhou … (see 1 more)
Saif M. Mohammad
Spectral State Space Model for Rotation-Invariant Visual Representation Learning
Sahar Dastani
Ali Bahri
Moslem Yazdanpanah
Mehrdad Noori
David Osowiechi
Gustavo Adolfo Vargas Hakim
Farzad Beizaee
Milad Cheraghalikhani
Arnab Kumar Mondal
Christian Desrosiers
A three-state coupled Markov switching model for COVID-19 outbreaks across Quebec based on hospital admissions
Dirk Douwes-Schultz
Alexandra M. Schmidt
Yannan Shen
Tractable Representations for Convergent Approximation of Distributional HJB Equations
Julie Alhosh
Harley Wiltzer
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction
Shravan Nayak
Xiangru Jian
Kevin Qinghong Lin
Juan A. Rodriguez
Montek Kalsi
Rabiul Awal
M. T. ¨Ozsu
David Vazquez
Perouz Taslakian
Spandana Gella
Sai Rajeswar
Human Annotator
Unveiling Inefficiencies in LLM-Generated Code: Toward a Comprehensive Taxonomy
Altaf Allah Abbassi
Leuson Da Silva
Amin Nikanjam
PRISM: High-Resolution&Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
Amar Kumar
Anita Kriz
Mohammad Havaei
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, da… (see more)ta imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures robust to the unique complexities posed by medical imaging data. The rapid advancements in vision-language foundation models within the natural image domain prompt the question of how they can be adapted for medical imaging tasks. In this work, we present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals using Stable Diffusion. Our approach demonstrates unprecedented precision in selectively modifying spurious correlations (the medical devices) and disease features, enabling the removal and addition of specific attributes while preserving other image characteristics. Through extensive evaluation, we show how PRISM advances counterfactual generation and enables the development of more robust downstream classifiers for clinically deployable solutions. To facilitate broader adoption and research, we make our code publicly available at https://github.com/Amarkr1/PRISM.
Steering Large Language Model Activations in Sparse Spaces
Reza Bayat
Ali Rahimi-Kalahroudi
Mohammad Pezeshki
Steering Large Language Model Activations in Sparse Spaces
Reza Bayat
Ali Rahimi-Kalahroudi
Mohammad Pezeshki
A key challenge in AI alignment is guiding large language models (LLMs) to follow desired behaviors at test time. Activation steering, which… (see more) modifies internal model activations during inference, offers a potential solution. However, prior work in dense activation spaces struggles with superposition, wherein multiple features become entangled, limiting interpretability and precise control. In contrast, sparse representations provide an untapped opportunity for more interpretable behavior modulation. In this work, we introduce sparse activation steering (SAS), a method that leverages sparse autoencoders (SAEs) to steer LLM behavior in sparse spaces. By isolating behavior-specific features through a contrastive prompt-pairing approach, we define a set of features that can selectively reinforce or suppress behaviors. Experiments on Gemma 2 LLMs show that SAS vectors enable nuanced behavioral modulation and finer-grained control. Furthermore, scaling SAEs improves monosemanticity of SAS vectors, suggesting more reliable and interpretable interventions.
Assessing the adoption of security policies by developers in terraform across different cloud providers
Alexandre Verdet
Mohammad Hamdaqa
Leuson Da Silva
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Rashid A. Mushkani
Shravan Nayak
Hugo Berard
Allison Cohen
Hadrien Bertrand
We introduce the Local Intersectional Visual Spaces (LIVS) dataset, a benchmark for multi-criteria alignment of text-to-image (T2I) models i… (see more)n inclusive urban planning. Developed through a two-year participatory process with 30 community organizations, LIVS encodes diverse spatial preferences across 634 initial concepts, consolidated into six core criteria: Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity, through 37,710 pairwise comparisons. Using Direct Preference Optimization (DPO) to fine-tune Stable Diffusion XL, we observed a measurable increase in alignment with community preferences, though a significant proportion of neutral ratings highlights the complexity of modeling intersectional needs. Additionally, as annotation volume increases, accuracy shifts further toward the DPO-tuned model, suggesting that larger-scale preference data enhances fine-tuning effectiveness. LIVS underscores the necessity of integrating context-specific, stakeholder-driven criteria into generative modeling and provides a resource for evaluating AI alignment methodologies across diverse socio-spatial contexts.