In-Context Learning (ICL) approaches, such as Zero-Shot and Few-Shot prompting, allow Large Language Models (LLMs) to tackle reasoning tasks without additional fine-tuning. However, Zero-Shot prompting often struggles with more complex tasks, whereas Few-Shot prompting demands considerable manual effort and domain expertise to design effective prompts. Although existing work has attempted to alleviate these issues by extracting reasoning rules from carefully crafted, task-specific representative examples, creating or obtaining such examples can be impractical in real-world scenarios. In this paper, we propose a novel approach that enhances the inference accuracy by injecting reasoning principles extracted from QA data, without relying on representative Few-Shot exemplars. This offers a lightweight yet adaptive way to boost accuracy on complex reasoning tasks, while avoiding manual effort and the high exploration costs typical of prior methods. Experiments on benchmarks show that, using GPT-4o, our method outperforms similarity-based Few-Shot and Zero-Shot prompting methods on challenging benchmarks such as GPQA-diamond, achieving an absolute accuracy improvement of up to 2% in scenarios where carefully crafted Few-Shot examples are unavailable.

Understanding fine-grained urban population distribution based on GPS location data is important for urban applications such as traffic management and new store openings for retailers. However, GPS-based population distribution relies heavily on the number of users who agree to provide GPS logs. With only a limited number of users, the fine-grained population distribution becomes sparse and must be aggregated as coarse-grained. In this paper, we present the challenge of developing a model to estimate fine-grained population distributions from coarse-grained population distributions and propose a model capable of incorporating extensive auxiliary information using a CNN-based image super-resolution approach. Our experiments with real data reveal two key findings: (i) traditional regression models tend to estimate similar populations for adjacent grids, which is often overlooked by existing metrics, and (ii) CNN-based image super-resolution models reproduce population distribution features of adjacent grids having different population volumes, although they sometimes provide simplistic estimates depending on auxiliary information. Based on these findings, we present our vision for developing a promising model and improving the evaluation metrics tailored to this challenge.

We introduce a novel feature-based active learning approach that leverages leading feature selection methods from explainable AI (XAI) to elicit user corrections on both the classification label and the leading features contributing to the classification decision. Uniquely, our approach can handle such user corrections, which identify missing and superfluous features in a model-agnostic manner that can be applied to any supervised classification model. Our approach generates augmented samples in a fine-grained manner according to user corrections, and then adds them to the training data to teach relevant and irrelevant features. Our results show that our approach outperforms traditional active learning and a leading baseline in two domains, which includes improvement in classification performance from 0.572 to 0.637 given the same number of queries.

We posit that predicting sensor event sequence (SES) in a smart home can proactively support resident activities or recognize activities that have not been completed as intended and alert the resident. To realize this application, we propose a framework to support accurate SES prediction by leveraging online activity recognition. Our framework includes a novel method of applying a GPT2-based model, which is a sentence generation model, for SES prediction by taking advantage of the property that the relationship between ongoing activity and SES patterns is similar to the relationship between topic and word sequence patterns in NLP. We evaluated our method empirically using two real-world datasets where residents perform their usual daily activities. Our experimental results show the use of the GPT2-based model significantly improves the F1 value of SES prediction from 0.461 to 0.708 compared to the state-of-the-art method, and that using ongoing activity can further improve performance to 0.837. We found that the performance of the online activity recognition model required to achieve these SES predictions was about 80%, which could be achieved using simple feature engineering and modeling.

Smart home environments are designed to provide services that help improve the quality of life for the occupant via a variety of sensors and actuators installed throughout the space. Many automated actions taken by a smart home are governed by the output of an underlying activity recognition system. However, activity recognition systems may not be perfectly accurate, and therefore inconsistencies in smart home operations can lead users reliant on smart home predictions to wonder “Why did the smart home do that?” In this work, we build on insights from Explainable Artificial Intelligence (XAI) techniques and introduce an explainable activity recognition framework in which we leverage leading XAI methods (Local Interpretable Model-agnostic Explanations, SHapley Additive exPlanations (SHAP), Anchors) to generate natural language explanations that explain what about an activity led to the given classification. We evaluate our framework in the context of a commonly targeted smart home scenario: autonomous remote caregiver monitoring for individuals who are living alone or need assistance. Within the context of remote caregiver monitoring, we perform a two-step evaluation: (a) utilize Machine Learning experts to assess the sensibility of explanations and (b) recruit non-experts in two user remote caregiver monitoring scenarios, synchronous and asynchronous, to assess the effectiveness of explanations generated via our framework. Our results show that the XAI approach, SHAP, has a 92% success rate in generating sensible explanations. Moreover, in 83% of sampled scenarios users preferred natural language explanations over a simple activity label, underscoring the need for explainable activity recognition systems. Finally, we show that explanations generated by some XAI methods can lead users to lose confidence in the accuracy of the underlying activity recognition model, while others lead users to gain confidence. Taking all studied factors into consideration, we make a recommendation regarding which existing XAI method leads to the best performance in the domain of smart home automation and discuss a range of topics for future work to further improve explainable activity recognition.

We propose a method to clarify the evolution of users' information needs related to a user's interests and actions based upon life events such as "childbirth." First, we extract topic transitions using dynamic topic models from blogs posted by users who have experienced life events. Next, we select the topics by computing the differences in topic probabilities before and after the life event. We evaluated our method based on three life events: "childbirth," "finding employment," and "marriage." Our method selected life event-relevant topics such as "child development," "working life," and "wedding ceremony." We found mothers' information needs such as "how to introduce baby food," employees' information needs such as "preparing an induction programme," and couples' information needs such as "wedding reception planning" in each topic.