Siamak Farshidi

Selecting a foundation model from a rapidly expanding model ecosystem requires decision makers to reconcile heterogeneous evidence about capabilities, operational constraints, and quality-related experience. Existing hub interfaces, lexical search, and general-purpose conversational recommenders provide useful discovery support, but they do not consistently expose an explicit, inspectable model of the trade-offs that drive a recommendation.This paper presents \textit{HugSelect}, a knowledge-driven decision-support system that transforms repository metadata, model-card descriptions, and community feedback into structured decision evidence. A curated knowledge base of 71,274 Hugging Face models represents metadata, extracted functional features, perceived quality attributes, and provenance links. Natural-language requirements are mapped to decision criteria, after which a transparent weighted additive model ranks candidates and exposes criterion-level score contributions.We evaluate HugSelect through extraction-pipeline validation, 44 literature-derived selection scenarios against four commercial conversational recommenders, component ablation, and an exploratory user study ($n=10$). Functional-feature extraction achieved F1=0.801; quality-attribute mapping achieved 0.84 accuracy (macro F1=0.65). HugSelect obtained Coverage@10 of 0.61 at model level and 0.91 at family level, with no significant overall family-level ranking difference among the evaluated systems. Ablation reduced model-level Coverage@10 to 0.23 without functional features, 0.41 without perceived-quality attributes, and 0.09 with metadata alone. The results indicate that multi-source evidence integration can support competitive retrieval effectiveness while providing an explicit and inspectable ranking process that clarifies the trade-offs behind recommendations.

Smart contract-based applications are executed in a blockchain environment, and they cannot directly access data from external systems, which is required for the service provision of these applications. Instead, smart contracts use agents known as blockchain oracles to collect and provide data feeds to the contracts. The functionality and compatibility with smart contract applications need to be considered when selecting the best-fit oracle platform. As the number of oracle alternatives and their features increases, the decision-making process becomes increasingly complex. Selecting the wrong or sub-optimal oracle is costly and may lead to severe security risks. This article provides a decision support model for the oracle selection problem. The model supports smart contract decision-makers in selecting a secure, cost-effective, and feasible oracle platform for their applications. We interviewed oracle co-founders and smart contracts experts to refine and validate the decision model. Two real-world smart contract application case studies were used to evaluate the model. Our model prioritises and suggests more than one possible oracle platform based on the developer’s required criteria, security assessment and cost analysis. Moreover, this guided decision model serves to reveal issues that may go unnoticed if done haphazardly, reduce decision-making efforts and provide a cost-effective solution.

User intent modeling in natural language processing deciphers user requests to allow for personalized responses. The substantial volume of research (exceeding 13,000 publications in the last decade) underscores the significance of understanding prevalent models in AI systems, with a focus on conversational recommender systems. We conducted a systematic literature review to identify models frequently employed for intent modeling in conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Furthermore, we conducted two case studies to assess the utility of our proposed decision model in guiding research modelers in selecting user intent modeling models for developing their conversational recommender systems. Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. The study offers practical insights into the domain of user intent modeling, specifically enhancing the development of conversational recommender systems. The introduced decision model provides a structured framework, enabling researchers to navigate the selection of the most apt intent modeling methods for conversational recommender systems.

Environmental, social, and governance accounting (ESGA) aids organizations in achieving their sustainability goals through continuous improvement. Suitable method selection is crucial to prevent rework, additional expenses, trivial outcomes, and reduced confidence in sustainability practices. The current ESGA method selection process lacks comprehensive consideration of alternatives and criteria, occasionally resulting in suboptimal choices. This work aims to achieve optimal ESGA by engineering a method selection framework. The research approach is based on the design cycle, where engineering decisions are informed by empirical evidence. The main findings are that the framework, which includes a decision model and a supporting information system, can reduce the chances of organizations selecting an unsuitable method, whilst sparing decision-making managers time and effort. Firstly, the reusable elements of the framework can help managers of any organization select a suitable method more efficiently since they do not have to produce these elements themselves. Secondly, the results demonstrate how selection frameworks and tools can aid organizations in navigating the complex ESG accounting landscape. Lastly, this study lowers the barrier for organizational impact management; in particular, for measuring and reporting ESG impact, which is a rigorous assessment of the organization’s progress towards sustainable development goals.

Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user’s request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks.

Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user's request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks.