This paper provides an early effort to systematically examine nation-level biases exhibited by Large Language Models (LLMs) within the domain of International Relations (IR), a dimension that has remained largely unexplored in prior research. Leveraging historical records from the United Nations Security Council (UNSC), we developed a bias evaluation framework comprising three distinct tests to explore nation-level bias in various LLMs, with a particular focus on the five permanent members of the UNSC. Experimental results show that, even with the general bias patterns across models (e.g., favorable biases toward the western nations, and unfavorable biases toward Russia), these still vary based on the LLM. Notably, even within the same LLM, the direction and magnitude of bias for a nation change depending on the evaluation context. This observation suggests that LLM biases are fundamentally multidimensional, varying across models and tasks. We also observe that models with stronger reasoning abilities show reduced bias and better prediction performance. Building on this finding, we introduce a debiasing framework that improves LLMs’ factual reasoning combining Retrieval-Augmented Generation with Reflexion-based self-reflection techniques. Experiments show it effectively reduces nation-level bias, and improves performance, particularly in GPT-4o-mini and LLama-3.3-70B. Our findings emphasize the need to assess nation-level bias alongside prediction performance when applying LLMs in the IR domain.

Question and Answering(QA) in low-resource languages remains a significant challenge due to the scarcity of high-quality training data.To address this, we propose a robust framework for lowresource QA.Our framework enhances performance through the integration of pivot-based translation, sub-question decomposition, and semantic consistency verification.Our proposed approach utilizes pivoting by translating questions into a high-resource language, and then translating the answer back into the original language.To improve the handling of complex queries, we introduce sub-question decomposition, which breaks down the original question into simpler sub-units for independent QA.Also, we incorporate a reverse QA mechanism that generates a new question from the predicted answer and measures its semantic similarity to the original question, thereby validating answer consistency.Evaluated on the TyDi QA benchmark, the proposed framework achieves a 19.21 chrF score and 0.67 BERTScore, corresponding to at least a 12% improvement in metrics over direct generation baselines.

Predicting the unprecedented, nonlinear nature of COVID-19 presents a significant public health challenge. Recent advances in deep learning, such as graph neural networks (GNNs), recurrent neural networks (RNNs), and Transformers, have enhanced predictions by modeling regional interactions, managing autoregressive time series, and identifying long-term dependencies. However, prior works often feature shallow integration of these models, leading to simplistic graph embeddings and inadequate analysis across different graph types. Additionally, excessive reliance on historical COVID-19 data limits the potential of utilizing time-lagged data, such as intervention policy information. To address these challenges, we introduce ReGraFT, a novel sequence-to-sequence (Seq2Seq) model designed for robust long-term forecasting of COVID-19. ReGraFT integrates multigraph-gated recurrent units (MGRU) with adaptive graphs, leveraging data from individual states, including infection rates, policy changes, and interstate travel. First, ReGraFT employs adaptive MGRU cells within an RNN framework to capture inter-regional dependencies, dynamically modeling complex transmission dynamics. Second, the model features a self-normalizing priming (SNP) layer using Scaled Exponential Linear Units (SeLU) to enhance stability and accuracy across short, medium, and long-term forecasts. Third, ReGraFT systematically compares and integrates various graph types, such as fully connected layers, pooling mechanisms, and attention-based structures, to provide a nuanced representation of inter-regional relationships. By incorporating lagged COVID-19 policy data, ReGraFT refines forecasts, demonstrating a 2.39% to 35.92% reduction in the root mean square error (RMSE) compared to state-of-the-art models. This work provides accurate long-term predictions, aiding in better public health decisions.

Influenza leads to many deaths every year and is a threat to human health. For effective prevention, traditional national-scale statistical surveillance systems have been developed, and numerous studies have been conducted to predict influenza outbreaks using web data. Most studies have captured the short-term signs of influenza outbreaks, such as one-week prediction using the characteristics of web data uploaded in real time; however, long-term predictions of more than 2-10 weeks are required to effectively cope with influenza outbreaks. In this study, we determined that web data uploaded in real time have a time-precedence relationship with influenza outbreaks. For example, a few weeks before an influenza pandemic, the word "colds" appears frequently in web data. The web data after the appearance of the word "colds" can be used as information for forecasting future influenza outbreaks, which can improve long-term influenza prediction accuracy. In this study, we propose a novel long-term influenza outbreak forecast model utilizing the time precedence between the emergence of web data and an influenza outbreak. Based on the proposed model, we conducted experiments on: 1) selecting suitable web data for long-term influenza prediction; 2) determining whether the proposed model is regionally dependent; and 3) evaluating the accuracy according to the prediction timeframe. The proposed model showed a correlation of 0.87 in the long-term prediction of ten weeks while significantly outperforming other state-of-the-art methods.

The digitization of health records due to technological developments has paved the way for patients to be collaboratively treated by different healthcare institutions. In collaborative ehealth systems, a patient's health data is stored remotely in the cloud for sharing with different healthcare service providers. However, the use of third parties for storage exposes the data to several privacy and security violation threats. Ciphertext policy attribute-based encryption (CP-ABE) which provides a fine-grained access control is a promising solution to privacy and security issues in the cloud environment and as a result, it has been widely studied for secure sharing of health data in cloud-based ehealth systems. Addressing the aspects of expressiveness, efficiency, user collusion resistance and attribute/user revocation in CP-ABE have been at the forefront of these studies. Thus, in this article, we proposed a novel expressive, efficient and collusion-resistant access control scheme with immediate attribute/user revocation for secure sharing of health data in collaborative ehealth systems. The proposed scheme additionally achieves forward and backward security. To realize these features, our access control is based on the ordered binary decision diagram (OBDD) access structure and it binds the user keys to the user identities. Security and performance analysis show that our proposed scheme is secure, expressive and efficient.

Although there have been many researches on the disease information system with the increased interest in disease, the existing systems have limitations in terms of emerging disease monitoring and internationalization. The purpose of this study is to develop a worldwide emerging disease-related information extraction system from news data, which provides nation-specific disease related information, disease-related topic ranking, map-based number of news articles per region, and various charts showing top disease regions and diseases. Our system is available on the web through http://www.epidemic.co.kr/worldwide.

Due to its usefulness and wide deployment, IEEE 802.11 has been the subject of numerous studies, but still lacks a complete analytical model. Hidden terminals are common in IEEE 802.11 and cause the degradation of throughput. Despite the importance of the hidden terminal problem, there have been a relatively small number of studies that consider the effect of hidden terminals on IEEE 802.11 throughput, and many are not accurate for a wide range of conditions. In this paper, we present an accurate new analytical saturation throughput model for the infrastructure case of IEEE 802.11 in the presence of hidden terminals. Simulation results show that our model is accurate in a wide variety of cases.

Advances in microprocessors, memory, and radio technology have enabled the emergence of embedded systems that rely on communication systems to exchange information and coordinate their activities in spatially distributed applications. However, developing embedded communication systems that satisfy specific application requirements is a challenge due to the many tradeoffs imposed by different choices of underlying protocols and their parameters. Furthermore, evaluating the correctness and performance of the design and implementation before deploying it is a nontrivial task due to the complexity of the resulting system. This article presents the design and implementation of RaPTEX, a rapid prototyping tool for embedded communication systems, especially well suited for wireless sensor networks (WSNs), consisting of three major subsystems: a toolbox, an analytical performance estimation framework, and an emulation environment. We use a hierarchical approach in the design of the toolbox to facilitate the composition of the network stack. For fast exploration of the tradeoff space at design time, we build an analytical performance estimation model for energy consumption, delay, and throughput. For realistic performance evaluation, we design and implement a hybrid, accurate, yet scalable, emulation environment. Through three use cases, we study the tradeoff space for different protocols and topologies, and highlight the benefits of using RaPTEX for designing and evaluating embedded communication systems for WSNs.

Energy efficiency of the MAC protocol is a key design factor for wireless sensor networks (WSNs). Due to the importance of the problem, a number of energy efficient MAC protocols have been developed for WSNs. Preamble-sampling based MAC protocols (e.g., B-MAC and X-MAC) have overheads due to their preambles, and are inefficient at large wakeup intervals. SCP-MAC, a very energy efficient scheduling MAC protocol, minimizes the preamble by combining preamble sampling and scheduling techniques; however, it does not prevent energy loss due to overhearing; in addition, due to its synchronization procedure, it results in increased contention and delay. In this paper, we present an energy efficient MAC protocol for WSNs that avoids overhearing and reduces contention and delay by asynchronously scheduling the wakeup time of neighboring nodes. To validate our design and analysis, we implement the proposed scheme on the MicaZ platform. Experimental results show that AS-MAC considerably reduces energy consumption, packet loss and delay when compared with SCP-MAC.