Sexism in online content is a pervasive issue that necessitates effective classification techniques to mitigate its harmful impact. Online platforms often have sexist comments and posts that create a hostile environment, especially for women and minority groups. This content not only spreads harmful stereotypes but also causes emotional harm. Reliable methods are essential to find and remove sexist content, making online spaces safer and more welcoming. Therefore, the sEXism Identification in Social neTworks (EXIST) challenge addresses this issue at CLEF 2024. This study aims to improve sexism identification in bilingual contexts (English and Spanish) by leveraging natural language processing models. The tasks are to determine whether a text is sexist and what the source intention behind it is. We fine-tuned the XLM-RoBERTa model and separately used GPT-3.5 with few-shot learning prompts to classify sexist content. The XLM-RoBERTa model exhibited robust performance in handling complex linguistic structures, while GPT-3.5's few-shot learning capability allowed for rapid adaptation to new data with minimal labeled examples. Our approach using XLM-RoBERTa achieved 4th place in the soft-soft evaluation of Task 1 (sexism identification). For Task 2 (source intention), we achieved 2nd place in the soft-soft evaluation.

This paper describes the creation, optimization, and assessment of a question-answering (QA) model for a personalized learning assistant that uses BERT transformers customized for the Arabic language. The model was particularly finetuned on science textbooks in Palestinian curriculum. Our approach uses BERT's brilliant capabilities to automatically produce correct answers to questions in the field of science education. The model's ability to understand and extract pertinent information is improved by finetuning it using 11th and 12th grade biology book in Palestinian curriculum. This increases the model's efficacy in producing enlightening responses. Exact match (EM) and F1 score metrics are used to assess the model's performance; the results show an EM score of 20% and an F1 score of 51%. These findings show that the model can comprehend and react to questions in the context of Palestinian science book. The results demonstrate the potential of BERT-based QA models to support learning and understanding Arabic students questions.

Autonomous vehicles are expected to transform transportation systems with rapid technological advancement. Human mobility would become more accessible and safer with the emergence of driverless vehicles. To this end, autonomous shuttle services are currently introduced in different urban conditions throughout the world. As a result, studies are needed to assess the safety and mobility performance of such autonomous shuttle services. However, calibrating the movement of autonomous shuttles in a simulation environment has been a difficult task due to the absence of any real-world data. This study aims to calibrate autonomous shuttles in a microscopic traffic simulation model and consequently assess the impact of the shuttle service on urban road capacity through simulation experiments. For this analysis, a prototype of an operational shuttle system at Lake Nona, Orlando, Florida is emulated in a microscopic traffic simulator during different times of the day. The movements of autonomous vehicles are calibrated using real-world trajectory data which help replicate the driving behavior of the shuttle in the simulation. The analysis reveals that with increasing frequency of the shuttle service the delay time percentage of the shared road sections increases and traveling speed decreases. It is also found that increasing the speed of shuttles up to 5 mph during off-peak hours and 10 mph during peak hours will improve traffic conditions. The findings from this study will assist policymakers and transportation agencies to revise policies for deploying autonomous shuttles and for planning road infrastructures for shared road-use of autonomous shuttles and human driven vehicles.

Large Language Models (LLMs) exhibit extensive knowledge about the world, but most evaluations have been limited to global or anglocentric subjects. This raises the question of how well these models perform on topics relevant to other cultures, whose presence on the web is not that prominent. To address this gap, we introduce BertaQA, a multiple-choice trivia dataset that is parallel in English and Basque. The dataset consists of a local subset with questions pertinent to the Basque culture, and a global subset with questions of broader interest. We find that state-of-the-art LLMs struggle with local cultural knowledge, even as they excel on global topics. However, we show that continued pre-training in Basque significantly improves the models' performance on Basque culture, even when queried in English. To our knowledge, this is the first solid evidence of knowledge transfer from a low-resource to a high-resource language. Our analysis sheds light on the complex interplay between language and knowledge, and reveals that some prior findings do not fully hold when reassessed on local topics. Our dataset and evaluation code are available under open licenses at https://github.com/juletx/BertaQA.

Embedders play a central role in machine learning, projecting any object into numerical representations that can, in turn, be leveraged to perform various downstream tasks. The evaluation of embedding models typically depends on domain-specific empirical approaches utilizing downstream tasks, primarily because of the lack of a standardized framework for comparison. However, acquiring adequately large and representative datasets for conducting these assessments is not always viable and can prove to be prohibitively expensive and time-consuming. In this paper, we present a unified approach to evaluate embedders. First, we establish theoretical foundations for comparing embedding models, drawing upon the concepts of sufficiency and informativeness. We then leverage these concepts to devise a tractable comparison criterion (information sufficiency), leading to a task-agnostic and self-supervised ranking procedure. We demonstrate experimentally that our approach aligns closely with the capability of embedding models to facilitate various downstream tasks in both natural language processing and molecular biology. This effectively offers practitioners a valuable tool for prioritizing model trials.

Reinforcement learning (RL) has recently shown promise in predicting Alzheimer's disease (AD) progression due to its unique ability to model domain knowledge. However, it is not clear which RL algorithms are well-suited for this task. Furthermore, these methods are not inherently explainable, limiting their applicability in real-world clinical scenarios. Our work addresses these two important questions. Using a causal, interpretable model of AD, we first compare the performance of four contemporary RL algorithms in predicting brain cognition over 10 years using only baseline (year 0) data. We then apply SHAP (SHapley Additive exPlanations) to explain the decisions made by each algorithm in the model. Our approach combines interpretability with explainability to provide insights into the key factors influencing AD progression, offering both global and individual, patient-level analysis. Our findings show that only one of the RL methods is able to satisfactorily model disease progression, but the post-hoc explanations indicate that all methods fail to properly capture the importance of amyloid accumulation, one of the pathological hallmarks of Alzheimer's disease. Our work aims to merge predictive accuracy with transparency, assisting clinicians and researchers in enhancing disease progression modeling for informed healthcare decisions. Code is available at https://github.com/rfali/xrlad.

Dropped Head Syndrome (DHS) causes a passively correctable neck deformation. Currently, there is no wearable orthopedic neck brace to fulfill the needs of persons suffering from DHS. Related works have made progress in this area by creating mobile neck braces that provide head support to mitigate deformation while permitting neck mobility, which enhances user-perceived comfort and quality of life. Specifically, passive designs show great potential for fully functional devices in the short term due to their inherent simplicity and compactness, although achieving suitable support presents some challenges. This work introduces a novel compliant mechanism that provides non-restrictive adjustable support for the neck's anterior and posterior flexion movements while enabling its unconstrained free rotation. The results from the experiments on non-affected persons suggest that the device provides the proposed adjustable support that unloads the muscle groups involved in supporting the head without overloading the antagonist muscle groups. Simultaneously, it was verified that the free rotation is achieved regardless of the stiffness configuration of the device.

Spoken Language Understanding (SLU) is a critical component of voice assistants; it consists of converting speech to semantic parses for task execution. Previous works have explored end-to-end models to improve the quality and robustness of SLU models with Deliberation, however these models have remained autoregressive, resulting in higher latencies. In this work we introduce PRoDeliberation, a novel method leveraging a Connectionist Temporal Classification-based decoding strategy as well as a denoising objective to train robust non-autoregressive deliberation models. We show that PRoDeliberation achieves the latency reduction of parallel decoding (2-10x improvement over autoregressive models) while retaining the ability to correct Automatic Speech Recognition (ASR) mistranscriptions of autoregressive deliberation systems. We further show that the design of the denoising training allows PRoDeliberation to overcome the limitations of small ASR devices, and we provide analysis on the necessity of each component of the system.

Efficiently modeling sequences with infinite context length has been a long-standing problem. Past works suffer from either the quadratic computation complexity or the limited extrapolation ability on length generalization. In this work, we present Samba, a simple hybrid architecture that layer-wise combines Mamba, a selective State Space Model (SSM), with Sliding Window Attention (SWA). Samba selectively compresses a given sequence into recurrent hidden states while still maintaining the ability to precisely recall memories with the attention mechanism. We scale Samba up to 3.8B parameters with 3.2T training tokens and show that Samba substantially outperforms the state-of-the-art models based on pure attention or SSMs on a wide range of benchmarks. When trained on 4K length sequences, Samba can be efficiently extrapolated to 256K context length with perfect memory recall and show improved token predictions up to 1M context length. As a linear-time sequence model, Samba enjoys a 3.73x higher throughput compared to Transformers with grouped-query attention when processing user prompts of 128K length, and 3.64x speedup when generating 64K tokens with unlimited streaming. A sample implementation of Samba is publicly available in https://github.com/microsoft/Samba.

A paradigm shift is underway in Software Engineering, with AI systems such as LLMs gaining increasing importance for improving software development productivity. This trend is anticipated to persist. In the next five years, we will likely see an increasing symbiotic partnership between human developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-Driven world and explore the key challenges that our research community should address to realize this vision.