Developing personalized chatbots is crucial in the field of AI, particularly when aiming for dynamic adaptability similar to that of human communication. Traditional methods often overlook the importance of both the speaker’s and the responder’s personalities and their interaction histories, resulting in lower predictive accuracy. Our solution, INTPChat (Interactive Persona Chat), addresses this limitation. INTPChat builds implicit profiles from extensive utterance histories of both speakers and responders and updates these profiles dynamically to reflect current conversational contexts. By employing a co-attention encoding mechanism, INTPChat aligns current contexts with responses while considering historical interactions. This approach effectively mitigates data sparsity issues by iteratively shifting each context backward in time, allowing for a more granular analysis of long-term interactions. Evaluations on long-term Reddit datasets demonstrate that INTPChat significantly enhances response accuracy and surpasses the performance of state-of-the-art persona chat models.

Many researchers have proposed several variants of visual secret sharing scheme (VSSS). In these schemes, the secret image can be recovered by only stacking share images. In this paper, we propose new VSSS embedded a decoding condition, which is called an adaptively decodable VSSS on background color. In the proposed scheme, the secret image cannot be visually recovered when the share images are stacked on a white background, but can be recovered when the shares are stacked on a black background. Furthermore, we propose a systematic method to construct (k, n)-threshold adaptively decodable VSSS for any integer k, n (k ≤ n).

We propose “bio-inspired UX,” a new method based on the defense mechanisms of ecosystems, for preventing intentional internal fraud within organizations. The proposed method features a function for sharing UX information within groups, inspired by the signal transmission mechanism between plants.

This study demonstrates the effectiveness of Domain-Adaptive Pre-Training (DAPT) on pre-trained language models for cybersecurity text mining. Experiments using various versions of BERT and RoBERTa confirm significant improvements in domain-specific tasks, particularly in identifying security-related concepts and terminology, and reveal that DAPT’s effectiveness varies depending on the label type.

Zoom Mail, an email service provided by Zoom Video Communications, features a proprietary end-to-end encryption (E2EE) scheme, which is detailed in a whitepaper available on GitHub. To date, there has been no detailed discussion or third-party evaluation of the security of the E2EE implementation in Zoom Mail. In this paper, we conduct a comprehensive security analysis of Zoom Mail’s E2EE. Specifically, we establishes four types of adversary models: insiders, outsiders, TO/CC (carbon copy) members, and BCC (blind carbon copy) member. We focus on three security goals: confidentiality, integrity, and authenticity. Based on these adversary models and security goals, we present the results of security evaluation of Zoom Mail’s E2EE for the first time.

With the increasing prevalence of mobile devices, wireless LANs, which allow network access without physical connections, have become widely used. Wi-Fi is particularly prevalent among wireless LANs, with a household Wi-Fi router adoption rate of approximately 89% and over 90% adoption in hospitals and schools in Japan. While Wi-Fi routers offer security features such as encryption and authentication, improperly configured or managed routers pose risks of eavesdropping and misuse by malicious actors. Previous studies have highlighted the risks of using vulnerable encryption protocols, such as WEP, and free public Wi-Fi services. However, the risks associated with default SSIDs and passwords on Wi-Fi routers remain largely unexplored. This study investigated the guessability of default Wi-Fi passwords across 44 consumer-grade Wi-Fi routers from 11 vendors commonly distributed in Japan. Our findings revealed that in 30 models from six vendors, default Wi-Fi passwords were generated using specific algorithms, making them vulnerable to being guessed by malicious actors. Based on the findings, we summarize the common pitfalls that product vendors often encounter when generating default Wi-Fi passwords. Additionally, we conducted a field survey across five locations in Tokyo, Japan to assess the prevalence and risk of Wi-Fi routers still operating with default settings.

ARM TrustZone technology is widely used to provide Trusted Execution Environments (TEEs) for sensitive applications. However, most TEE OSes are implemented as monolithic kernels. In such designs, all components run in the kernel which will lead to a big trusted computing base (TCB). It is difficult to guarantee that all components of the kernel have no security vulnerabilities. The functions of trusted computing, such as integrity measurement and data sealing, will provide further security guarantees. This paper presents MicroTEE, a TEE OS with rich trusted computing primitives based on the microkernel architecture. In MicroTEE, the microkernel provides strong isolation for services and applications. The kernel is only responsible for providing core services such as address space management, thread management, and inter-process communication. Other fundamental services, such as Trusted Service, are implemented as applications at the user layer. Trusted computing primitives provide some security features for trusted applications (TAs), including integrity measurement, data sealing, and remote attestation. Our design avoids the compromise of the whole TEE OS if some kernel service is vulnerable. A monitor has also been added to perform the switch between the secure world and the normal world. Finally, we implemented a MicroTEE prototype on the Freescale i.MX6Q Sabre Lite development board and tested its performance. Evaluation results show that MicroTEE only introduces some necessary and acceptable overhead.

Diffusion models are generative models that generate images, videos, and audio through learning samples and have attracted attention in recent years. In this paper, we investigate whether a diffusion model is resistant to membership inference attacks, which evaluate the privacy leakage of a machine learning model. We primarily discuss the diffusion model from the standpoints of comparison with a generative adversarial network (GAN) as a conventional model and hyperparameters unique to the diffusion model, such as timesteps, sampling steps, and sampling variances. We conduct extensive experiments with the denoising diffusion implicit model (DDIM) as a diffusion model and the deep convolutional GAN (DCGAN) as a GAN on the CelebA and CIFAR-10 datasets in both white-box and black-box settings and then show that the diffusion model is comparable to GAN in terms of resistance to membership inference attacks. Next, we demonstrate that the impact of timesteps is significant and that the intermediate steps in a noise schedule the most vulnerable to the attack. We also found two key insights through further analysis. First, we identify that DDIM is more vulnerable to the attack when trained with fewer samples even though it achieves lower Frechet inception distance scores than DCGAN. Second, sampling steps in hyperparameters are important for resistance to the attack, whereas the impact of sampling variances is negligible.

We examine the security of AES-based authenticated encryption schemes including the AEGIS family, Tiaoxin-346, Rocca and Rocca-S. In existing work, Takeuchi et al. evaluated the security against differential attacks for the initialization phase of AEGIS and Rocca and estimated the lower bounds for the number of active S-boxes by byte-wise search. Similarly, Shiraya et al. evaluated the security against differential attacks for the initialization phase of Tiaoxin-346. However, the byte-wise evaluation might underestimate the lower bounds for the number of active S-boxes as it might include invalid characteristics. In this paper, we conduct a bit-wise evaluation of the AEGIS family, Tiaoxin-346, Rocca, and Rocca-S to obtain exact lower bounds for the number of active S-boxes by the boolean satisfiability problem (SAT) tools. Besides, we derive the optimal differential characteristics in the initialization phase. As a result, we update the lower bounds for the number of active S-boxes in some rounds for each targets and derived the probability of optimal differential characteristics for the first time, which allowed us to find longest differential distinguisher of initialization phase on AEGIS family, Tiaoxin-346, Rocca and Rocca-S. As far as we known, our results are the first bit-wise distinguishing attacks on initialization phases of AEGIS, Tiaoxin-346, Rocca, and Rocca-S.

Secure multi-party computation (MPC) allows participating parties to jointly compute a function over their inputs while keeping them private. In particular, MPC based on additive secret sharing has been widely studied as a tool to obtain efficient protocols secure against a dishonest majority, including the important two-party case. In this paper, we propose a two-party protocol for an exponentiation functionality based on an additive secret sharing scheme. Our proposed protocol aims to securely compute a public base exponentiation a^x mod p for an odd prime p, where the exponent x ∈ ℤ_p is a (shared) secret and the base a ∈ ℤ_p is public. Our protocol is based on a new simple but efficient approach involving quotient transfer that allows the parties to perform the most expensive part of the computation locally, but requires the base a ∈ ℤ_p to be a quadratic residue. To address scenarios where the base does not fulfill this, we combine our exponentiation protocol with a new efficient modulus conversion protocol which might be of independent interest. Even taking into account a potential modulus conversion, our exponentiation protocol only requires 3 rounds and 4 invocations of multiplication.

Mobile edge computing (MEC) faces severe challenges in achieving efficient and timely task offloading in heterogeneous network environments. While existing contract-based approaches address incentive compatibility and resource coordination, many either ignore the constraints of age of information (AoI) or suffer from high computational complexity. This paper presents an AoI-guaranteed Optimal Contract (AOC) mechanism that jointly considers information freshness and asymmetric information in MEC systems. We design a three-tier heterogeneous network architecture with non-orthogonal multiple access to enable cooperative task offloading across multiple cells and enhance spectral efficiency. Instead of a model that requires extensive training and is difficult to analyze, our proposed AOC framework uses a lightweight block coordinate descent (BCD) algorithm to solve closed-form contract solutions while ensuring incentive compatibility and individual rationality. Simulation results show that the AOC mechanism significantly improves the utility and AoI performance of the MEC server compared with existing incentive-based methods. In addition, the analysis confirms the robustness and practical deployability of the proposed framework under different system conditions.

Wikipedia stands out as a globally utilized linguistic resource available in over 330 languages, attracting contributions from a diverse group of editors on a global scale. Despite its widespread use, significant disparities persist among language publications, including variations in the number of articles, the spectrum of topics covered, and even the number of contributing community editors. In this paper, we aim to alleviate this gap in the coverage of low-resource languages. Although previous work has focused on multilingual interoperability efforts, the potential of hyperlinks has not been fully realized. Therefore, this study introduces a novel approach focused on hyperlinks, specifically emphasizing hyperlink types derived from Wikidata. We extract and analyze patterns related to these hyperlink types across different languages, using them as recommended solutions to connect the topics of various languages, particularly low-resource languages. Collaborative filtering experiments suggest that using combined languages leads to good overall results while preserving the uniqueness of each language.

With the rapid increase in demand for IoT devices, malicious attacks targeting vulnerabilities in IoT devices have been frequent in recent years. It is highly expected that the vulnerabilities can be removed from them through vulnerability assessment. However, the wide variety of IoT devices is not standardized, and it is difficult to set up vulnerability assessment items mechanically for those IoT devices, which causes a major obstacle to automate the vulnerability assessment for IoT devices. In this paper, we propose a method to prioritize vulnerability assessment items for every IoT device by effectively utilizing large language models (LLMs). The proposed method generates the answers that take into account the specifications of individual IoT devices using an LLM by introducing Retrieval Augmented Generation (RAG), and determines how much suitable each vulnerability assessment item is for every IoT device by calculating the suitability using semantic entropy. At that time, the proposed method introduces hybrid search with reranking as a search method for related chunks in RAG. Through binary classification of vulnerability assessment items, the average area under the curve (AUC) of 0.753 was achieved for five IoT devices. We confirmed that the proposed method is more effective in evaluating the suitability of the items to the target device specifications than the methods using keyword search, vector search, and hybrid search with RRF (Reciprocal Rank Fusion).

Driver distraction is a primary cause of traffic accidents, and the real-time and effective detection of such behaviors can significantly reduce traffic-related injuries and fatalities. In this paper, we enhance the lightweight YOLOv10n model by integrating the BiFPN structure to bolster its multi-scale feature extraction capabilities. Additionally, we design a CASSA module that combines channel attention, spatial attention, and channel shuffle to strengthen the model’s ability to capture long-range dependencies. The model was tested on the CBTDDD dataset, established in this study, which includes data on driver distraction across multiple scenarios involving sedans, passenger buses, and trucks. Compared to the original YOLOv10n model, the proposed model demonstrates a 2.0% improvement in mAP@0.5 and achieves an FPS of 115.3 f/s. These results indicate that the YOLOv10n-BC model developed in this paper is capable of performing real-time and efficient monitoring of driver distraction.

Federated Learning collaborates with multiple organizations to train machine learning models in a way that does not reveal raw data. As a new learning paradigm, FL suffers from statistical challenges on cross-organizational non-IID data, limiting the global model to provide good performance for each client task. In this paper, we propose a personalized federated meta-learning (EPer-FedMeta) algorithm for heterogeneous clients using q-FedAvg as a model aggregation strategy, which helps the global model to optimize a reasonable representation fairly with multiple client personalized models and introduces a contrast loss in the local training to bring the similarity between meta-learner representations closer. Also noteworthy is the potential cold-start problem for new tasks in PFL (Personalized Federated Learning), where EPer-FedMeta simply uses CondConv to make lightweight modifications to the CNN network for more robust model personalization migration. Our extensive empirical evaluation of the LEAF dataset and the actual production dataset shows that EPer-FedMeta further mitigates the challenges of Non-IID data on FL system communication costs and model accuracy. In terms of performance and optimization, EPer-FedMeta achieves optimal model performance with faster convergence and lower communication overhead compared to the leading optimization algorithms in FL.

Currently, research in deepfake speech detection focuses on the generalization of detection systems towards different spoofing methods, mainly for noise-free clean speech. However, the performance of speech anti-spoofing countermeasure (CM) systems often does not work well in more complicated scenarios, such as those involving noise and reverberation. To address the problem of enhancing the robustness of CM systems, we propose a transfer learning-based hybrid approach with Speech Enhancement front-end and CounterMeasure back-end Joint optimization (SECM-Joint), investigating its effectiveness in improving robustness against noise and reverberation. Experimental results show that our SECM-Joint method reduces EER by 19.11% to 64.05% relatively in most noisy conditions and 23.23% to 30.67% relatively in reverberant environments compared to a Conformer-based CM baseline system without pre-training. Additionally, our dual-path U-Net (DUMENet) further enhances the robustness for real-world applications. These results demonstrate that the proposed method effectively enhances the robustness of CM systems in noisy and reverberant conditions. Codes and experimental data supporting this work are publicly available at: https://github.com/ikou-austin/SECM-Joint

Object detection in drone-captured scenarios presents significant challenges due to factors such as varying object scales, motion blur, and dense object clusters. Although existing methods, including attention blocks and feature fusion networks, have shown improvements in detection accuracy, they often come with high computational costs, which hinder real-time performance. In this paper, we propose IFN-YOLOv8, an enhanced version of YOLOv8, designed to address these challenges. By integrating the P2 feature scale, IFN-YOLOv8 enhances small object detection through higher-resolution feature maps. Additionally, we introduce a novel convolutional block, RHAConv, to replace traditional convolution layers, improving feature representation in scenes with dense object clusters. A new Information Fusion Module is also proposed to refine object features, reducing both missed and false detections. Experimental results on the VisDrone and DOTA datasets demonstrate that IFN-YOLOv8 outperforms mainstream methods, achieving an mAP@50 of 45.7% and 68.5%, respectively, while maintaining low resource consumption and high detection speed.

Joint multimodal aspect-based sentiment analysis (JMABSA) aims to extract aspects from multimodal inputs and determine their sentiment polarity. Existing research often faces challenges in effectively aligning aspect features across images and text. To address this, we propose an entity knowledge-guided image-text alignment network that integrates alignment across both modalities, enabling the model to more accurately capture jointly expressed aspect and sentiment information in images and text. Specifically, we introduce an entity class embedding to guide the model in learning entity-related features from text. Additionally, we utilize scene and aspect descriptions in images as entity knowledge, helping the model learn entity-relevant features from visual input. The alignment between entity knowledge in images and the initial text further supports the model in learning consistent aspect and sentiment expressions across modalities. Experimental results on two benchmark datasets demonstrate that our method achieves state-of-the-art performance on two public datasets.

Deep learning has revolutionized complex tasks such as classification, approximation, and prediction, drawing inspiration from mathematical models of the human brain. Among recent breakthroughs, Google’s Transformer architecture has established itself as a leading framework in natural language processing. Its adaptation to computer vision, known as the Vision Transformer (ViT), has set new benchmarks for image-based tasks. In this study, we introduce a novel neural network model that integrates the input layer of the ViT with the dendritic neuron model (DNM). This hybrid architecture combines the advanced feature extraction capabilities of ViT with the adaptability and robustness of DNM to enhance performance. The proposed model is applied to the diagnosis of diabetic retinopathy, effectively identifying critical features associated with the condition. The results underscore its potential to improve the accuracy and reliability of medical image analysis, paving the way for advancements in healthcare diagnostics.

An FPGA-based fire detection system using a back propagation (BP) neural network was designed for early fire detection in key equipment in converter stations. An 8-5-1 BP network structure was trained, achieving a recognition accuracy of 94.08%. Fixed-point data quantization and pipelining were employed to reduce computational complexity, lowering resource consumption and enhancing speed. The FPGA system used 683 LUTs, achieved a 94.6% detection rate, consumed only 1.342 W of power and completed a single detection in 3.25μs,a significant improvement compared to the 8.56 ms detection time on MATLAB. This system demonstrates excellent reliability, real-time performance, and promising application potential for early fire detection in key equipment in converter stations.

Deep Learning-based Fault Localization (DLFL) uses metamorphic testing to locate faults in the absence of test oracles. However, these approaches face the class imbalance problem, i.e., the violated data (i.e., minority class) is much less than the non-violated data (i.e., majority class). To address this issue, we propose MDAug: Metamorphic Diffusion-based Augmentation for improving DLFL without test oracles. MDAug combines metamorphic testing and diffusion model to generate the data of minority class and acquire class balanced data. We apply MDAug to three state-of-the-art DLFL baselines without test oracles, and the results show that MDAug significantly outperforms all the baselines in the absence of test oracles.

Glaucoma is one of the leading causes of irreversible blindness worldwide. Deep learning methods have made significant strides in predicting glaucoma in recent years. However, existing models continue encountering challenges in extracting complex and subtle pathological features from fundus images associated with glaucoma. To address this limitation, we propose a novel DMNet model, which aims to enhance the integration of input signals by simulating the dendritic neuron model. This approach can improve the capture of fine details within glaucoma images and significantly boost classification performance. Experimental results indicate that DMNet outperforms traditional deep learning models on the glaucoma fundus image dataset, demonstrating its substantial performance advantages.

Disaster relief robots have been studied extensively as a promising approach to realize lifesaving and goods transportation without the need for manpower. Most disaster relief robots are designed to search for and find a person in need of rescue. However, it is not always easy for a robot to find a person in need of rescue at a disaster site, and the person in need of rescue may not even notice the presence of a robot approaching very close by. In this study, we therefore investigate the effectiveness of smell as a method of communicating the presence of a robot. We conducted a search experiment with and without smell to evaluate whether the sense of smell is useful for search. The results of the experiment confirmed its high effectiveness in searching with smell.