Abstract
Advancements in large language model (LLM)-based conversational assistants have transformed search experiences into more natural and context-aware dialogues that resemble human conversation. However, limited access to interaction log data hinders a deeper understanding of their real-world usage. To address this gap, we analyzed 16,952 prompt logs from 904 unique users of Labadain Chat, an LLM-based conversational assistant designed for Tetun speakers, to uncover patterns in user search behavior, engagement, and intent. Our findings show that most users (29.87%) spent between one and five minutes per session, with an average of 43 unique daily users. The majority (93.97%) submitted multiple prompts per session, with an average session duration of 16.9 minutes. Most users (95.22%) were based in Timor-Leste, with education and science (28.75%) and health (28.00%) being the most searched topics. We compared our findings with a study on Google Bard logs in English, revealing similar search characteristics-including engagement duration, command-based instructions, and requests for specific assistance. Furthermore, a comparison with two conventional search engines suggests that LLM-based conversational systems have influenced user search behavior on traditional platforms, reflecting a broader trend toward command-driven queries. These insights contribute to a deeper understanding of how user search behavior evolves, particularly within low-resource language communities. To support future research, we publicly release LabadainLog-17k+, a dataset of over 17,000 real-world user search logs in Tetun, offering a unique resource for investigating conversational search in this language.

Abstract
Heterogeneous CPU-FPGA C/C++ applications may rely on High-level Synthesis (HLS) tools to generate hardware for critical code regions. As typical HLS tools have several restrictions in terms of supported language features, to increase the size and variety of offloaded regions, we propose several code transformations to improve synthesizability. Such code transformations include: struct and array flattening; moving dynamic memory allocations out of a region; transforming dynamic memory allocations into static; and asynchronously executing host functions, e.g., printf(). We evaluate the impact of these transformations on code region size using three realworld applications whose critical regions are limited by nonsynthesizable C/C++ language features.

Abstract
Critical performance regions of software applications are often accelerated by offloading them onto an FPGA. An efficient end result requires the judicious application of two processes: hardware/software (hw/sw) partitioning, which identifies the regions for offloading, and the optimization of those regions for efficient High-level Synthesis (HLS). Both processes are commonly applied separately, not relying on any potential interplay between them, and not revealing how the decisions made in one process could positively influence the other. This paper describes our primary efforts and contributions made so far, and our work-in-progress, in an approach that combines both hw/sw partitioning and optimization into a unified, holistic process, automated using source-to-source compilation. By using an Extended Task Graph (ETG) representation of a C/C++ application, and expanding the synthesizable code regions, our approach aims at creating clusters of tasks for offloading by a) maximizing the potential optimizations applied to the cluster, b) minimizing the global communication cost, and c) grouping tasks that share data in the same cluster.

Abstract
The International Symposium on Applied Reconfigurable Computing (ARC) is an annual forum for the discussion and dissemination of research, notably applying the Reconfigurable Computing (RC) concept to real-world problems. The first edition of ARC took place in 2005, and in 2024, ARC celebrated its 20th edition. During those 20 years, the field of reconfigurable computing saw a tremendous growth in its underlying technology. ARC contributed very significantly to the presentation and dissemination of new ideas, innovative applications, and fruitful discussions, all of which have resulted in the shaping of novel lines of research. Here, we present selected papers from the first 20 years of ARC, that we believe represent the corpus of work and reflect the ARC spirit by covering a broad spectrum of RC applications, benchmarks, tools, and architectures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract

Abstract
Online reviews are a crucial asset for e-commerce platforms as they provide consumers with valuable insights into products. It is important to note that these reviews are subjective and may contain biases. Therefore, it is essential to approach them with a critical eye. Despite this, online reviews remain a valuable tool for consumers when making purchasing decisions. This study focuses on developing web-based mini-games that target cognitive biases. The games are specifically designed to enhance the perception of e-commerce online reviews. A pilot study involving 85 participants was conducted to explore the potential of integrating these cognitive bias games into web platforms. The findings indicate promising avenues for leveraging these games to enhance cognitive personalization and improve the quality of e-commerce online reviews.