The rise of Large Visual-Language Models (LVLMs) has unlocked new possibilities for seamlessly integrating visual and textual information. However, their ability to interpret cartographic maps remains largely unexplored. In this paper, we introduce CartoMapQA, a benchmark specifically designed to evaluate LVLMs' understanding of cartographic maps through question-answering tasks. The dataset includes over 2000 samples, each composed of a cartographic map, a question (with open-ended or multiple-choice answers), and a ground-truth answer. These tasks span key low-, mid- and high-level map interpretation skills, including symbol recognition, embedded information extraction, scale interpretation, and route-based reasoning. Our evaluation of both open-source and proprietary LVLMs reveals persistent challenges: models frequently struggle with map-specific semantics, exhibit limited geospatial reasoning, and are prone to Optical Character Recognition (OCR)-related errors. By isolating these weaknesses, CartoMapQA offers a valuable tool for guiding future improvements in LVLM architectures. Ultimately, it supports the development of models better equipped for real-world applications that depend on robust and reliable map understanding, such as navigation, geographic search, and urban planning. Our source code and data are openly available to the research community at: https://github.com/ungquanghuy-kddi/CartoMapQA.git

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.

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.

In the domain of connected vehicles or advanced driver assistance systems, electroencephalogram (EEG) data is measured in vehicles and used for applications in driver safety. These analysis modules are designed to detect abnormal driver states such as drowsiness, fatigue, and dangerous driving by using EEG data in real-time on edge devices since these conditions reflect a driver’s current cognitive state. However, there are few approaches to forecasting EEG data to prevent dangerous driving in advance using recent deep learning techniques. In this paper, we propose a novel generative adversarial network (W-GAN) which aims to forecast EEGs as a multivariate multi-step times series data. It consists of dilated causal convolutional layers to maintain EEG characteristics. We also propose a new performance measure reflecting the reproducibility of frequency components which confirms the feasibility of the forecasted EEG data. We conducted an experiment to evaluate our proposed model using EEG analysis research data. In the experiment, it was shown that our model outperformed several deep learning models in reproducibility of both EEG waveform and frequency components.

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.

The landscape of power grids is gradually changing. The growing number of electrical appliances as well as the outbreak of Electric Vehicles (EVs) is increasing the need for electricity. As a consequence, high-accuracy consumption predictions are necessary in order to both schedule and plan production and operations accordingly. The emergence of connected “tracking” devices opens up new data-sets into both Internet-of-Things and Big Data worlds. It provides information on human dynamics (people mobility behavior) and with it several opportunities. Electricity consumption is impacted by people movements as while moving they are not “connected” to the power grid. Therefore, predicting such movement patterns and volume could help electricity providers improve their own consumption predictions. This paper presents a system and methods used to predict people movement behaviors as well as detect any anomaly. A scoring system is used to both evaluate the dynamics predictions and raise alerts when the computed score surpasses established thresholds. This proposal is tested over a scenario using available datasets and demonstrates that modifications in people movement behavior is affecting the consumption profile.

How can you specify a device in the case of file sharing among multiple devices on that spot (e.g., a meeting picture of a social gathering or an alumni association)? One solution is to specify the partner device address (e.g., e-mail address or Bluetooth device address) on your own, but users sometimes feel annoyed or make a mistake with this operation. In this paper, we newly propose a device specification method by user operation of describing a circle in the air, enabling the user to be unaware of the device address. The proposed method uses the Doppler Effect of acoustic waves generated by the operation, enabling to specify multiple partner devices simultaneously and distinguish the device with which you do not want to share, without any pre-planned infrastructure. We measure its performance via an implemented application. The experimental results show that its performance is sufficient for the above use case.

In this poster, we propose a new tool, i.e. a node placement adviser, which supports deployment workers of sensor networks. The adviser tool is realized in the form of mobile terminal software and acts as a proxy of an additional node, as well as communicating with those already placed. Based on the communication results, it indicates how favorable the current location is to a worker.

In this paper, we discuss a hierarchical group protocol aiming at reducing communication and computation supporting for realizing a scalable group. In traditional hierarchical groups, a pair of parent and child subgroups communicate with one another through a gateway process. A gateway process implies performance bottleneck and single point of failure. In order to increase the throughput and reliability of inter-subgroup communication, messages are in parallel transmitted in a striping way through multiple channels between the subgroups. We discuss how to design a hierarchical group for realizing high-performance multimedia communication among large number of peer processes.