Predictions on subseasonal-to-seasonal ( S2S) timescales--ranging from two weeks to two months--are crucial for early warning systems but remain challenging owing to chaos in the climate system. Teleconnections, such as the stratospheric polar vortex (SPV) and Madden-Julian Oscillation (MJO), offer windows of enhanced predictability, however, their complex interactions remain underutilized in operational forecasting. Here, we developed and evaluated deep learning architectures to predict North Atlantic-European (NAE) weather regimes, systematically assessing the role of remote drivers in improving S2S forecast skill of deep learning models. We implemented (1) a Long Short-term Memory (LSTM) network predicting the NAE regimes of the next six weeks based on previous regimes, (2) an Index-LSTM incorporating SPV and MJO indices, and (3) a ViT-LSTM using a Vision Transformer to directly encode stratospheric wind and tropical outgoing longwave radiation fields. These models are compared with operational hindcasts as well as other AI models. Our results show that leveraging teleconnection information enhances skill at longer lead times. Notably, the ViT-LSTM outperforms ECMWF's subseasonal hindcasts beyond week 4 by improving Scandinavian Blocking (SB) and Atlantic Ridge (AR) predictions. Analysis of high-confidence predictions reveals that NAO, SB, and AR opportunity forecasts can be associated with SPV variability and MJO phase patterns aligning with established pathways, also indicating new patterns. Overall, our work demonstrates that encoding physically meaningful climate fields can enhance S2S prediction skill, advancing AI-driven subseasonal forecast.

Affective polarization, or, inter-party hostility, is increasingly recognized as a pervasive issue in democracies worldwide, posing a threat to social cohesion. The digital media ecosystem, now widely accessible and ever-present, has often been implicated in accelerating this phenomenon. However, the precise causal mechanisms responsible for driving affective polarization have been a subject of extensive debate. While the concept of echo chambers, characterized by individuals ensconced within like-minded groups, bereft of counter-attitudinal content, has long been the prevailing hypothesis, accumulating empirical evidence suggests a more nuanced picture. This study aims to contribute to the ongoing debate by employing an agent-based model to illustrate how affective polarization is either fostered or hindered by individual news consumption and dissemination patterns based on ideological alignment. To achieve this, we parameterize three key aspects: (1) The affective asymmetry of individuals' engagement with in-party versus out-party content, (2) The proportion of in-party members within one's social neighborhood, and (3) The degree of partisan bias among the elites within the population. Subsequently, we observe macro-level changes in affective polarization within the population under various conditions stipulated by these parameters. This approach allows us to explore the intricate dynamics of affective polarization within digital environments, shedding light on the interplay between individual behaviors, social networks, and information exposure.

Abstract--As AI systems increasingly influence critical sectors like telecommunications, finance, healthcare, and pub lic services, ensuring fairness in decision-making is essenti al to prevent biased or unjust outcomes that disproportionately affect vulnerable entities or result in adverse impacts. This need is particularly pressing as the industry approaches the 6G era, where AI will drive complex functions like autonomous netwo rk management and hyper-personalized services. However, as AI applications diversify, this standard requires enhanceme nt to strengthen its impact and broaden its applicability. This p aper proposes an expansion of the TEC Standard to include fairnes s assessments for images, unstructured text, and generative AI, including large language models, ensuring a more comprehen - sive approach that keeps pace with evolving AI technologies . By incorporating these dimensions, the enhanced framework will promote responsible and trustworthy AI deployment acr oss various sectors. The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies has driven transforma-tive advancements across critical sectors, including tele communications, healthcare, finance, and public services.

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any cur rent or future media. Caroline Panggabean Departement of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka carolinepgabean@gmail.com Sincy John Departement of CSE (AIM) JAIN (Deemed - to - be University) Bangalore, Karnataka sincyjohn@jainuniversity.ac.in Chandrasekar Venkatachalam Departement of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka chandrasekar.v@jainuniversity.ac.in Renuka Devi P Departement of CSE (AIML) JAIN (Deemed - to - be University) Bangalore, Karnataka renukadevi.p@jainuniversity.ac.in Priyanka Shah Departement of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka priyankashah8324@gmail.com Shanmugavalli Venkatachalam Department of CSE KSR College of Engineering Namakkal, Tamil N adu drvshanmugavalli@gmail.com Abstract -- Detecting Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks remains a critical challenge in cybersecurity. This research introduces a hybrid deep learning model combining Gated Recurrent Units (GRUs) and a Neural Turing Machine (NTM) for enhanced intrusion detection. Trained on UNSW - NB15 and BoT - IoT datasets, the model employs GRU layers for sequential data processing and an NTM for long - term pattern recognition.

This paper explores the use of large language models (LLMs), here represented by GPT 3.5-Turbo (henceforth "GPT"), to perform coding for a thematic analysis. Coding is highly labor intensive, making it infeasible for most researchers to conduct exhaustive thematic analyses of large corpora. Recent advances in large language models (LLMs) have opened the door to novel approaches for automating aspects of qualitative research, including thematic analysis (TA). Prior work has shown that LLMs can generate plausible thematic codes for text data (Dai, Xiong, and Ku, 2023; Morgan, 2023; De Paoli, 2024). This paper focuses on the development and evaluation of an AI-assisted coding methodology designed to enhance the thematic coding of text passages using large language models.

Analyzing ideological discourse even in the age of LLMs remains a challenge, as these models often struggle to capture the key elements that shape real-world narratives. Specifically, LLMs fail to focus on characteristic elements driving dominant discourses and lack the ability to integrate contextual information required for understanding abstract ideological views. To address these limitations, we propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events. Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion. It then constructs a vocabulary of repeating themes - prominent talking points, that are used to generate ideology-specific viewpoints (or partisan perspectives). We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation. Additionally, we demonstrate straightforward applicability of our framework in creating event snapshots, a visual way of interpreting event discourse. We release resulting dataset and model to the community to support further research.

Climate science studies the structure and dynamics of Earth's climate system and seeks to understand how climate changes over time, where the data is usually stored in the format of time series, recording the climate features, geolocation, time attributes, etc. Recently, much research attention has been paid to the climate benchmarks. In addition to the most common task of weather forecasting, several pioneering benchmark works are proposed for extending the modality, such as domain-specific applications like tropical cyclone intensity prediction and flash flood damage estimation, or climate statement and confidence level in the format of natural language. To further motivate the artificial general intelligence development for climate science, in this paper, we first contribute a multi-modal climate benchmark, i.e., ClimateBench-M, which aligns (1) the time series climate data from ERA5, (2) extreme weather events data from NOAA, and (3) satellite image data from NASA HLS based on a unified spatial-temporal granularity. Second, under each data modality, we also propose a simple but strong generative method that could produce competitive performance in weather forecasting, thunderstorm alerts, and crop segmentation tasks in the proposed ClimateBench-M. The data and code of ClimateBench-M are publicly available at https://github.com/iDEA-iSAIL-Lab-UIUC/ClimateBench-M.

Continual learning techniques employ simple replay sample selection processes and use them during subsequent tasks. Typically, they rely on labeled data. In this paper, we depart from this by automatically selecting prototypes stored without labels, preserving cluster structures in the latent space across tasks. By eliminating label dependence in the replay buffer and introducing cluster preservation loss, it is demonstrated that the proposed method can maintain essential information from previously encountered tasks while ensuring adaptation to new tasks. "Push-away" and "pull-toward" mechanisms over previously learned prototypes are also introduced for class-incremental and domain-incremental scenarios. These mechanisms ensure the retention of previously learned information as well as adaptation to new classes or domain shifts. The proposed method is evaluated on several benchmarks, including SplitCIFAR100, SplitImageNet32, SplitTinyImageNet, and SplitCaltech256 for class-incremental, as well as R-MNIST and CORe50 for domain-incremental setting using pre-extracted DINOv2 features. Experimental results indicate that the label-free replay-based technique outperforms state-of-the-art continual learning methods and, in some cases, even surpasses offline learning. An unsupervised variant of the proposed technique for the class-incremental setting, avoiding labels use even on incoming data, also demonstrated competitive performance, outperforming particular supervised baselines in some cases. These findings underscore the effectiveness of the proposed framework in retaining prior information and facilitating continual adaptation.

Large Language Models (LLMs) have recently emerged as powerful tools in cybersecurity, offering advanced capabilities in malware detection, generation, and real-time monitoring. Numerous studies have explored their application in cybersecurity, demonstrating their effectiveness in identifying novel malware variants, analyzing malicious code structures, and enhancing automated threat analysis. Several transformer-based architectures and LLM-driven models have been proposed to improve malware analysis, leveraging semantic and structural insights to recognize malicious intent more accurately. This study presents a comprehensive review of LLM-based approaches in malware code analysis, summarizing recent advancements, trends, and methodologies. We examine notable scholarly works to map the research landscape, identify key challenges, and highlight emerging innovations in LLM-driven cybersecurity. Additionally, we emphasize the role of static analysis in malware detection, introduce notable datasets and specialized LLM models, and discuss essential datasets supporting automated malware research. This study serves as a valuable resource for researchers and cybersecurity professionals, offering insights into LLM-powered malware detection and defence strategies while outlining future directions for strengthening cybersecurity resilience.

Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance by dynamically assigning queries to the most appropriate model based on query complexity. Despite recent advances showing that preference-data-based routers can outperform traditional methods, current evaluation benchmarks remain limited. They largely focus on general model capabilities while overlooking task-specific behaviors and critical concerns such as privacy, safety, and potential backdoor vulnerabilities introduced through preference data. In response, we propose the DSC benchmark: Diverse, Simple, and Categorized, an evaluation framework that categorizes router performance across a broad spectrum of query types, including coding, translation, mathematics, human instructions, general knowledge, and LLM jailbreaking. Additionally, it integrates privacy and safety assessments to reveal hidden risks. Our experiments on three preference-based routers and two commercial counterparts demonstrate that while these systems improve efficiency, they often make suboptimal, category-driven decisions. For instance, a BERT-based router directs all coding and mathematics queries to the most powerful LLM even when simpler models would suffice, while routing jailbreaking attempts to weaker models, thereby elevating safety risks.