Artificial Intelligence (AI) is becoming increasingly indispensable across diverse domains as technology rapidly advances. As traditional energy sources dwindle, there's a noticeable pivot towards renewable energy sources (RES). However, to effectively meet energy demands, integrating these RES into smart grids to bolster efficiency is imperative. Despite the transition, ongoing technical challenges persist, specifically in accurately predicting and optimizing smart grid parameters. To tackle these hurdles and enhance smart grid efficiency, various AI techniques are being harnessed. This study leverages real-time energy generation data (MWh) from solar and wind plants over a year, dependent on parameters such as POA and wind speed, respectively. Prediction outcomes are derived using three machine learning (ML) models (XGBoost, CatBoost, and LightGBM) and three deep learning (DL) models (LSTM, BiLSTM, and GRU). From these individual models, two hybrid ML and DL models are developed, yielding promising results. Subsequently, these outcomes are further refined through a parallel fusion approach (PFA), resulting in heightened accuracy and reliability. The implementation of this technique notably reduces error rates to 15.05% for hybrid ML, 19.18% for hybrid DL, and 8.1432% for PFA. This methodology holds substantial potential for future research endeavors, supplementing existing AI models for enhanced efficiency.

Abstract Closed sign language interpreting makes media accessible to deaf and hard-of-hearing viewers who use sign language as their primary mode of communication. Analogous to subtitles, this feature allows to toggle sign language interpretation on and off, and customize its appearance in conjunction with videos. This paper provides information on designing closed interpreting in a media player through a pair of mixed-method studies. The first study assesses the usability of technical sign language interpreting features, while the second one assesses how users interact with the content. Results indicate above-average usability for the technical features. Additionally, preliminary results suggest that the optimal configuration of the SLI depends on the type of content viewed and that user preferences vary. Overall, the customizability of features and placement will be important in closed-interpreting implementations.

With the proliferation of new hardware architectures and ever-evolving user requirements, the software stack is becoming increasingly bloated. In practice, only a limited subset of the supported functionality is utilized in a particular usage context, thereby presenting an opportunity to eliminate unused features. In the past, program specialization has been proposed as a mechanism for enabling automatic software debloating. In this work, we show how existing program specialization techniques lack the analyses required for providing code simplification for real-world programs. We present an approach that uses stronger analysis techniques to take advantage of constant configuration data, thereby enabling more effective debloating. We developed Trimmer, an application specialization tool that leverages user-provided configuration data to specialize an application to its deployment context. The specialization process attempts to eliminate the application functionality that is unused in the user-defined context. Our evaluation demonstrates Trimmer can effectively reduce code bloat. For 13 applications spanning various domains, we observe a mean binary size reduction of 21% and a maximum reduction of 75%. We also show specialization reduces the surface for code-reuse attacks by reducing the number of exploitable gadgets. For the evaluated programs, we observe a 20% mean reduction in the total gadget count and a maximum reduction of 87%.