Although student learning satisfaction has been widely studied, modern techniques such as interpretable machine learning and neural networks have not been sufficiently explored. This study demonstrates that a recent model that combines boosting with interpretability, automatic piecewise linear regression(APLR), offers the best fit for predicting learning satisfaction among several state-of-the-art approaches. Through the analysis of APLR's numerical and visual interpretations, students' time management and concentration abilities, perceived helpfulness to classmates, and participation in offline courses have the most significant positive impact on learning satisfaction. Surprisingly, involvement in creative activities did not positively affect learning satisfaction. Moreover, the contributing factors can be interpreted on an individual level, allowing educators to customize instructions according to student profiles.

Adversarial learning is critical for enhancing model robustness, aiming to defend against adversarial attacks that jeopardize machine learning systems. Traditional methods often lack efficient mechanisms to explore diverse adversarial perturbations, leading to limited model resilience. Inspired by game-theoretic principles, where adversarial dynamics are analyzed through frameworks like Nash equilibrium, exploration mechanisms in such setups allow for the discovery of diverse strategies, enhancing system robustness. However, existing adversarial learning methods often fail to incorporate structured exploration effectively, reducing their ability to improve model defense comprehensively. To address these challenges, we propose a novel Exploration-enhanced Adversarial Learning Algorithm (ExAL), leveraging the Exponentially Weighted Momentum Particle Swarm Optimizer (EMPSO) to generate optimized adversarial perturbations. ExAL integrates exploration-driven mechanisms to discover perturbations that maximize impact on the model's decision boundary while preserving structural coherence in the data. We evaluate the performance of ExAL on the MNIST Handwritten Digits and Blended Malware datasets. Experimental results demonstrate that ExAL significantly enhances model resilience to adversarial attacks by improving robustness through adversarial learning.

Every company betting that artificial intelligence will transform how we work and live has a big--and growing--problem: AI is inherently ravenous for electricity. Some experts project that global electricity consumption for AI systems could soon require adding the equivalent of a small country's worth of power generation to our planet. That demand comes as the world is trying to electrify as much as possible and decarbonize how that power is generated in the face of climate change.

Emotion recognition in conversations (ERC) is a crucial task for building human-like conversational agents. While substantial efforts have been devoted to ERC for chit-chat dialogues, the task-oriented counterpart is largely left unattended. Directly applying chit-chat ERC models to task-oriented dialogues (ToDs) results in suboptimal performance as these models overlook key features such as the correlation between emotions and task completion in ToDs. In this paper, we propose a framework that turns a chit-chat ERC model into a task-oriented one, addressing three critical aspects: data, features and objective. First, we devise two ways of augmenting rare emotions to improve ERC performance. Second, we use dialogue states as auxiliary features to incorporate key information from the goal of the user. Lastly, we leverage a multi-aspect emotion definition in ToDs to devise a multi-task learning objective and a novel emotion-distance weighted loss function. Our framework yields significant improvements for a range of chit-chat ERC models on EmoWOZ, a large-scale dataset for user emotion in ToDs. We further investigate the generalisability of the best resulting model to predict user satisfaction in different ToD datasets. A comparison with supervised baselines shows a strong zero-shot capability, highlighting the potential usage of our framework in wider scenarios.

For the past ten years we have been working on the problem of getting a computer to understand natural language.

Specialization and generalization are accomplished by adding or deleting elements in these lists. The use of symbolic categories of belief (definite, probable, and possible) provides a specifiable means for manipulating the rules. While based on a simple idea, the SEEK program convincingly demonstrates the value of a rich('v structured representation and of reasoning from cases as a way of constructing a model. That is, exjJert knowledge is inseparable from case experience (Schank, 1983), in so far as knov.Jledge explains the cases. The use of a knowledge base to provide an explanatm), model has characterized other recent AIM work as well (cf.

... we began to program a computer understanding system thatwould attempt to process input texts. An item crucial to our ability to accomplishthis task was what we called a script. A script is a frequently repeated causalchain of events that describes a standard situation. In understanding, when it ispossible to notice that one of these standard event chains has been initiated,then it is possible to understand predictively. That is, if we know we are in arestaurant then we can understand where an "order" fits with what we justheard, who might be ordering what from whom, what preconditions (menu,sitting down) might have preceded the "order", and what is likely to happennext. All this information comes from the restaurant script.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.