Reflexivity & Reflection (R&R) for Sociotechnical Safety:Creating a Space for Collective Learning

Researchers in CSCW have long examined the sociotechnical aspects of digital security, privacy, and safety, building knowledge not only on the security challenges faced by (at-risk) communities, but also on the challenges of conducting responsible research. The burgeoning subfield of “sociotechnical safety" within computer security & privacy (S&P) has grown alongside this work, including topics like the S&P of at-risk users. These two research fields are distinct in epistemological and methodological approaches, but share a common goal: improving the digital safety of (at-risk) populations. During this critical time, we see an opportunity to gather as one community, to encourage honest conversation about the “hows" and “whys" of sociotechnical safety research. We invite researchers in both fields to discuss how CSCW’s methods, norms, and theories might bridge this emergent community, e.g., building meaningful collaborations with participants, researcher/participant safety. To cultivate reflexivity and reflection (R&R), we will host a closed-door panel of experienced researchers to share learnings from their work before collaboratively developing artifacts outlining actions that researchers can take to address these challenges. By fostering a collective learning environment at CSCW, we will assist researchers across disciplines to conduct responsible sociotechnical safety research by prioritising reflexivity

Hyperparameter Optimization Techniques for Enhanced Machine Learning Energy Forecasting:A Comparative Analysis

Advanced machine learning (ML) models are essential for power system forecasting, yet their performance critically depends on architecture structure and parameter definition. Manual parameter tuning is time-consuming and forecasting errors can significantly impact utilities economically, making ML model optimization vital. This paper presents a comparative analysis of optimization techniques for tuning ML models across diverse energy data sources (photovoltaic (PV), mains, and battery energy storage systems (BESS)) and varying dataset sizes. Evaluation with real-world data on a Deep Neural Network (DNN) for 1-second ahead predictions revealed that Bayesian and meta-learning approaches consistently deliver superior performance with lower computational time. Grid search showed unexpected strength with smaller datasets, while random search and Population-Based Training (PBT) performed well with extensive data but degraded with small datasets. The Bayesian multi objective approach performed comparably to standard Bayesian optimization but with increased computational demands. Results revealed that all models showed 10-15% lower performance with mains data compared to PV, while BESS data yielded results approximately 3% below PV performance. The significant variance across data sources underscores the importance of tailoring optimization strategies to each energy data type’s inherent characteristics, including temporal volatility patterns, noise profiles, and feature correlations. Therefore, effective hyperparameter tuning must consider both computational constraints and the fundamental stochastic properties of the underlying energy systems

Protecting Public Interest in Sovereign and Municipal Restructurings

This paper explores the intersection of public interest and debt restructuring frameworks for sovereign and municipal debtors. The analysis underscores the need for legal systems to prioritize transparency, service continuity, and equitable burden-sharing among stakeholders, especially in the context of rising fiscal pressures and governance deficits

Optimal Threshold Singular Spectrum Analysis for Efficient Electrocardiogram Interference Removal

Singular Spectrum Analysis (SSA) has become well known for its ability to effectively separate mixtures of signals with overlapping spectral content but with different statistical natures. In this paper, we show how a new approach to grouping the singular values that efficiently denoise biomedical signals, specifically, mixtures of Electrocardiogram and Electromyogram signals. It is based on optimal Singular Value Hard Thresholding (SVHT) but for signals that are periodic or quasi-periodic in nature. An optimal thresholding technique can provide similar results with much smaller trajectory matrices and thus significantly reduced computational burden. The resultant Singular Value Decomposition process is significantly faster and shows similar performance to kurtosis based sliding SSA with a reduction in computational complexity of the order of 12,500 times. This technique is well suited to real-time implementation for de-noising biomedical signals on the fly