Machine learning models have achieved, and in some cases surpassed, human-level performance in various tasks, mainly through centralized training of static models and the use of large models stored in centralized clouds for inference. However, this centralized approach has several drawbacks, including privacy concerns, high storage demands, a single point of failure, and significant computing requirements. These challenges have driven interest in developing alternative decentralized and distributed methods for AI training and inference. Distribution introduces additional complexity, as it requires managing multiple moving parts. To address these complexities and fill a gap in the development of distributed AI systems, this work proposes a novel framework, Data and Dynamics-Aware Inference and Training Networks (DA-ITN). The different components of DA-ITN and their functions are explored, and the associated challenges and research areas are highlighted.

Plagiarism involves using another person's work or concepts without proper attribution, presenting them as original creations. With the growing amount of data communicated in regional languages such as Marathi -- one of India's regional languages -- it is crucial to design robust plagiarism detection systems tailored for low-resource languages. Language models like Bidirectional Encoder Representations from Transformers (BERT) have demonstrated exceptional capability in text representation and feature extraction, making them essential tools for semantic analysis and plagiarism detection. However, the application of BERT for low-resource languages remains under-explored, particularly in the context of plagiarism detection. This paper presents a method to enhance the accuracy of plagiarism detection for Marathi texts using BERT sentence embeddings in conjunction with Term Frequency-Inverse Document Frequency (TF-IDF) feature representation. This approach effectively captures statistical, semantic, and syntactic aspects of text features through a weighted voting ensemble of machine learning models.

Large Language Models (LLMs) demonstrate impressive capabilities in the domain of program synthesis. This level of performance is not, however, universal across all tasks, all LLMs and all prompting styles. There are many areas where one LLM dominates, one prompting style dominates, or where calling a symbolic solver is a better choice than an LLM. A key challenge for the user then, is to identify not only when an LLM is the right choice of solver, and the appropriate LLM to call for a given synthesis task, but also the right way to call it. A non-expert user who makes the wrong choice, incurs a cost both in terms of results (number of tasks solved, and the time it takes to solve them) and financial cost, if using a closed-source language model via a commercial API. We frame this choice as an online learning problem. We use a multi-armed bandit algorithm to select which symbolic solver, or LLM and prompt combination to deploy in order to maximize a given reward function (which may prioritize solving time, number of synthesis tasks solved, or financial cost of solving). We implement an instance of this approach, called CYANEA, and evaluate it on synthesis queries from the literature in ranking function synthesis, from the syntax-guided synthesis competition, and fresh, unseen queries generated from SMT problems. CYANEA solves 37.2\% more queries than the best single solver and achieves results within 4\% of the virtual best solver.

The development of Automatic Question Generation (QG) models has the potential to significantly improve educational practices by reducing the teacher workload associated with creating educational content. This paper introduces a novel approach to educational question generation that controls the topical focus of questions. The proposed Topic-Controlled Question Generation (T-CQG) method enhances the relevance and effectiveness of the generated content for educational purposes. Our approach uses fine-tuning on a pre-trained T5-small model, employing specially created datasets tailored to educational needs. The research further explores the impacts of pre-training strategies, quantisation, and data augmentation on the model's performance. We specifically address the challenge of generating semantically aligned questions with paragraph-level contexts, thereby improving the topic specificity of the generated questions. In addition, we introduce and explore novel evaluation methods to assess the topical relatedness of the generated questions. Our results, validated through rigorous offline and human-backed evaluations, demonstrate that the proposed models effectively generate high-quality, topic-focused questions. These models have the potential to reduce teacher workload and support personalised tutoring systems by serving as bespoke question generators. With its relatively small number of parameters, the proposals not only advance the capabilities of question generation models for handling specific educational topics but also offer a scalable solution that reduces infrastructure costs. This scalability makes them feasible for widespread use in education without reliance on proprietary large language models like ChatGPT.

The Greek Language Multimodal Lip Reading with Integrated Sign Language Accessibility (GLaM-Sign) [1] is a groundbreaking resource in accessibility and multimodal AI, designed to support Deaf and Hard-of-Hearing (DHH) individuals. Developed from the FEELIT project [2], it integrates high-resolution audio, video, textual transcriptions, and Greek Sign Language translations for applications like real-time sign language translation and enhanced subtitle synchronization. While its primary focus is on promoting inclusivity in the Greek tourism sector, its adaptability extends to education, healthcare, and public services. Future advancements will enhance word-level precision and scalability to additional languages, supported by advanced AI methodologies and collaborations with diverse stakeholders. This dataset underscores the transformative potential of multimodal resources in bridging communication gaps, fostering innovation, and setting a benchmark for ethical AI and inclusive technologies.

Expanding the frame of evaluation to include other parties, as well as the ecosystem in which the system is deployed, leads us to a multistakeholder view of recommender system evaluation as defined in [2]: "A multistakeholder evaluation is one in which the quality of recommendations is assessed across multiple groups of stakeholders." In this article, we provide (i) an overview of the types of recommendation stakeholders that can be considered in conducting such evaluations, (ii) a discussion of the considerations and values that enter into developing measures that capture outcomes of interest for a diversity of stakeholders, (iii) an outline of a methodology for developing and applying multistakeholder evaluation, and (iv) three examples of different multistakeholder scenarios including derivations of evaluation metrics for different stakeholder groups in these different scenarios. The variety of possible stakeholders we identified that are part of the general recommendation ecosystem is suggested in Figure 1 and defined here, using the terminology from [1, 2]: Recommendation consumers are the traditional recommender system users to whom recommendations are delivered and to which typical forms of recommender system evaluation are oriented. Item providers form the general class of individuals or entities who create or otherwise stand behind the items being recommended.

Context. Developing secure and reliable software remains a key challenge in software engineering (SE). The ever-evolving technological landscape offers both opportunities and threats, creating a dynamic space where chaos and order compete. Secure software engineering (SSE) must continuously address vulnerabilities that endanger software systems and carry broader socio-economic risks, such as compromising critical national infrastructure and causing significant financial losses. Researchers and practitioners have explored methodologies like Static Application Security Testing Tools (SASTTs) and artificial intelligence (AI) approaches, including machine learning (ML) and large language models (LLMs), to detect and mitigate these vulnerabilities. Each method has unique strengths and limitations. Aim. This thesis seeks to bring order to the chaos in SSE by addressing domain-specific differences that impact AI accuracy. Methodology. The research employs a mix of empirical strategies, such as evaluating effort-aware metrics, analyzing SASTTs, conducting method-level analysis, and leveraging evidence-based techniques like systematic dataset reviews. These approaches help characterize vulnerability prediction datasets. Results. Key findings include limitations in static analysis tools for identifying vulnerabilities, gaps in SASTT coverage of vulnerability types, weak relationships among vulnerability severity scores, improved defect prediction accuracy using just-in-time modeling, and threats posed by untouched methods. Conclusions. This thesis highlights the complexity of SSE and the importance of contextual knowledge in improving AI-driven vulnerability and defect prediction. The comprehensive analysis advances effective prediction models, benefiting both researchers and practitioners.

Document-Level Biomedical Relation Extraction (Bio-RE) aims to identify relations between biomedical entities within extensive texts, serving as a crucial subfield of biomedical text mining. Existing Bio-RE methods struggle with cross-sentence inference, which is essential for capturing relations spanning multiple sentences. Moreover, previous methods often overlook the incompleteness of documents and lack the integration of external knowledge, limiting contextual richness. Besides, the scarcity of annotated data further hampers model training. Recent advancements in large language models (LLMs) have inspired us to explore all the above issues for document-level Bio-RE. Specifically, we propose a document-level Bio-RE framework via LLM Adaptive Document-Relation Cross-Mapping (ADRCM) Fine-Tuning and Concept Unique Identifier (CUI) Retrieval-Augmented Generation (RAG). First, we introduce the Iteration-of-REsummary (IoRs) prompt for solving the data scarcity issue. In this way, Bio-RE task-specific synthetic data can be generated by guiding ChatGPT to focus on entity relations and iteratively refining synthetic data. Next, we propose ADRCM fine-tuning, a novel fine-tuning recipe that establishes mappings across different documents and relations, enhancing the model's contextual understanding and cross-sentence inference capabilities. Finally, during the inference, a biomedical-specific RAG approach, named CUI RAG, is designed to leverage CUIs as indexes for entities, narrowing the retrieval scope and enriching the relevant document contexts. Experiments conducted on three Bio-RE datasets (GDA, CDR, and BioRED) demonstrate the state-of-the-art performance of our proposed method by comparing it with other related works.

Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data, which makes them struggle to understand non-English input and fail to generate output in the desired target language. Existing efforts mitigate these issues by adding multilingual training data, but do so in a largely ad-hoc manner, lacking insight into how different training mixes tip the scale for different groups of languages. In this work, we present a comprehensive investigation into the training strategies for massively multilingual LVLMs. First, we conduct a series of multi-stage experiments spanning 13 downstream vision-language tasks and 43 languages, systematically examining: (1) the number of training languages that can be included without degrading English performance and (2) optimal language distributions of pre-training as well as (3) instruction-tuning data. Further, we (4) investigate how to improve multilingual text-in-image understanding, and introduce a new benchmark for the task. Surprisingly, our analysis reveals that one can (i) include as many as 100 training languages simultaneously (ii) with as little as 25-50\% of non-English data, to greatly improve multilingual performance while retaining strong English performance. We further find that (iii) including non-English OCR data in pre-training and instruction-tuning is paramount for improving multilingual text-in-image understanding. Finally, we put all our findings together and train Centurio, a 100-language LVLM, offering state-of-the-art performance in an evaluation covering 14 tasks and 56 languages.

In this paper, we introduce LLaVA-Octopus, a novel video multimodal large language model. LLaVA-Octopus adaptively weights features from different visual projectors based on user instructions, enabling us to leverage the complementary strengths of each projector. We observe that different visual projectors exhibit distinct characteristics when handling specific tasks. For instance, some projectors excel at capturing static details, while others are more effective at processing temporal information, and some are better suited for tasks requiring temporal coherence. By dynamically adjusting feature weights according to user instructions, LLaVA-Octopus dynamically selects and combines the most suitable features, significantly enhancing the model's performance in multimodal tasks. Experimental results demonstrate that LLaVA-Octopus achieves excellent performance across multiple benchmarks, especially in tasks such as multimodal understanding, visual question answering, and video understanding, highlighting its broad application potential.