http://hdl.handle.net/1893/478 Electronic copies of Computing Science and Mathematics conference papers and proceedings. Sun, 25 Oct 2026 18:16:11 GMT 2026-10-25T18:16:11Z http://hdl.handle.net/1893/38410 Title: An Explainable Estimation Model for Financial Time Series Author(s): Takahashi, Osamu; Cuyt, Annie; O'Hare, Anthony; Lee, Wen-shin; Ferrara, Massimiilinao Editor(s): Vantaggi, Barbara; Coletti, Giulianella; Denoeux, Thierry; Laurent, Anne; Petturiti, Davide; Miranda, Enrique; Medina, Jesus; Bouchon-Meunier, Bernadette; Yager, Ronald R Abstract: Recent developments in artificial intelligence have led to powerful machine learning approaches to model fluctuating dynamics in financial time series, even in the presence of non-stationary behavior. However, such methods often suffer from limited interpretability while uncertainty, arising from complex data fluctuations, remains difficult to quantify. We present a method that decomposes financial data into different frequency components and trends, thereby enabling explicit identification of cyclical patterns and long-term trends. Concurrently, noise components are separated from signal components. Its effectiveness and its high predictive accuracy have been demonstrated in the interpretation of some financial time series. Mon, 15 Jun 2026 00:00:00 GMT http://hdl.handle.net/1893/38410 2026-06-15T00:00:00Z http://hdl.handle.net/1893/38384 Title: What Do AI Agents Actually Change? An Empirical Taxonomy of Mutation Patterns in Performance-Improving Pull Requests Anonymous Anonymous Institution Author(s): Dovhoshliubnyi, llia; Soroush, Nima; Sami, Ashkan; Brownlee, Alexander Abstract: AI coding agents are black boxes: we cannot inspect how they generate code, but we can inspect what they change. This distinction matters for search-based software engineering (SBSE), where techniques such as genetic improvement depend on mutation operators that reflect how code is actually transformed. Of the 33,596 agent PRs in the AIDev dataset, less than 400 target performance (fewer than 1%), making each successful case a valuable window into otherwise opaque agent behaviour. We classify 1,254 performance-relevant diff hunks from 216 of these PRs, spanning five agent systems, against the 18-category syntactic mutation taxonomy of Even-Mendoza et al. (2025) using an LLM-as-a-judge pipeline. Three categories dominate: name modification (36.9%), object creation (26.3%), and type change (22.6%), a profile strikingly different from prior genetic improvement corpora where no change accounted for 84%. Each agent commits to a distinctive mutation vocabulary, and each performance strategy activates a largely disjoint category subset. Agent identity and target strategy are therefore informative priors that narrow the effective SBSE operator space from 18 categories to a handful per context. Replication package: https://anonymous.4open.science/r/ssbse-challenge-2026-710C/ Fri, 10 Jul 2026 00:00:00 GMT http://hdl.handle.net/1893/38384 2026-07-10T00:00:00Z http://hdl.handle.net/1893/38347 Title: Bridging Learning Outcomes and Module Specifications: GenAI Framework for Curriculum Development in CS Education Author(s): Elawady, Mohamed; Ali, Hazrat Abstract: Recent developments in generative artificial intelligence (GenAI) have enabled the production of high-quality content in many domains , including education and computing. These advances offer opportunities to support tutors in curriculum development and to enhance the learning experience for students. This paper presents an end-to-end pipeline that automatically suggests a set of module specifications based on user input and structured guidelines. Thu, 03 Sep 2026 00:00:00 GMT http://hdl.handle.net/1893/38347 2026-09-03T00:00:00Z http://hdl.handle.net/1893/38262 Title: Optimizing Appliance Scheduling for Solar Energy Management Us-ing Metaheuristic Algorithms Author(s): Ahmed, Hiba; Brownlee, Alexander E I; Adair, Jason; Powers, Simon T Abstract: Solar energy generation is often misaligned with when households use power, creating a scheduling challenge. We optimize appliance start times in an island microgrid setting to minimize user dissatisfaction while promoting solar use and respecting system constraints. A sequential multi-day scheduling framework using Iterated Local Search (ILS) and Simulated Annealing (SA) considers power consumption, active duration, inverter size, battery limits, and solar forecasts, opening potential to explore trade-offs between cost, system size, and satisfaction. Thu, 13 Aug 2026 00:00:00 GMT http://hdl.handle.net/1893/38262 2026-08-13T00:00:00Z