Stream processing engines need to process multiple queries over streams simultaneously, and continuous window aggregation plays a critical role in various applications as a part of data analysis pipelines. However, the system suffers from scalability issues when dealing with massive queries with different window and slide sizes over data streams with high input rates. To address this problem, we propose LSiX (longest-shortest-window-based indexing) to aggregate multiple queries over data streams efficiently. More precisely, we employ two arrays based on the longest and shortest windows among all registered queries, and all query results are computed by using the shared partial aggregations in the two arrays using only two operations at most for each query, enabling efficient aggregation computation. We have conducted extensive experiments, and the results show that LSiX can be at least 3 times faster than the comparative methods, including the state-of-the-art method, MCQA.

The generalization performance of machine learning models deteriorates when the models are trained with mislabeled data. Existing methods to address mislabeled data rely on pre-processing or in-processing of the training. However, those methods require retraining when applied to trained models. As the model size and dataset size increase, the cost of retraining the model becomes a significant issue, necessitating the development of new approaches. In this paper, we propose a new method to remove mislabeled data from trained models without retraining via machine unlearning. Our proposed method consists of two stages: first, detecting mislabeled data from trained models, and second, unlearning these data from the models. We conduct extensive experiments on the MNIST dataset to evaluate our proposed method. To comprehensively evaluate the effectiveness of our proposed method, we perform individual experiments for the detection stage and the unlearning stage. Our findings demonstrate that the detection stage performs well when the proportion of mislabeled data is low, and the unlearning stage effectively enhances model accuracy. However, in an integrated experiment involving both stages, we observed intriguing yet negative results: despite the effectiveness of individual stages, model accuracy did not improve due to the high proportion of mislabeled data. Our code is available at https://github.com/speed1313/mislabel-unlearning.

In this study, we propose a method to efficiently retrieve BERT pre-trained models that achieve good performance on a specific document classification task. In natural language processing problems, the common practice involves fine-tuning existing pre-trained models rather than building new ones from the ground up due to the extensive time and computational resources required. The challenge, however, lies in identifying the most suitable model from a large number of available pre-trained models. To address this problem, our proposed method utilizes the k-nearest neighbor algorithm to retrieve appropriate BERT pre-trained models without the necessity for fine-tuning. We conducted experiments by constructing a benchmark dataset with 28 document classification tasks and 20 BERT models.

Dynamic Time Warping (DTW) is a well-known similarity measure between time series data. Although DTW can calculate the similarity between time series with different lengths, it is computationally expensive. Therefore, fast algorithms that approximate the DTW have been desired. SSH (Sketch, Shingle & Hash) is a representative hash-based approximation algorithm. It extracts a set of quantized subsequences from a given time series and finds similar time series by means of Min-Hash, a hash-based set similarity search. However, Min-Hash does not care about the location of set elements (i.e., quantized subsequences) in the time series, so that hash collisions have rather weak correlation with DTW. In this paper, to strengthen the correlation between hash collisions and DTW, we propose a new method termed Section Min-Hash that can couple the hash values with the positions of quantized subsequences. After quantizing subsequences in a time series based on Euclidean distance, Section Min-Hash explicitly specifies multiple sections within the time series and generates the hash values from all the sections.

The data collected by Internet of Things (IoT) devices equipped with sensors enable smart home services such as monitoring of elderly, pets, and the indoor environment. Building an IoT system to collect data from individual households in the cloud requires measures to reduce communication latency and the amount of data transferred and protect privacy. Installing sensors in multiple indoor locations is necessary when collecting diverse data in an indoor environment. However, installing numerous sensors increases costs and makes it challenging to relocate them and obtain the necessary information. In this study, we indicate the effectiveness of an IoT system using a wheeled mobile robot implemented in a Robot Operating System (ROS). We attempt to demonstrate the effectiveness of sensor data collection using a robot by developing a prototype system that collects indoor environment information and performs analysis processing in a cloud via an edge server for a monitoring application of indoor carbon dioxide concentration. We also investigate the performance characteristics of ROS and ROS 2 communication between the sensor robot and the edge server and IoT communication between the edge server and the cloud server to identify technical issues in a smart home.

Collaborative Filtering (CF) is a crucial task in recommender systems which tries to exploit past user-item interactions to recommend new items to users. Recently, Graph Convolutional Networks (GCN) based models have achieved great success in CF by exploring various methods to embed user and item nodes in the interaction graph. However, as node embeddings gradually learn how to interact with other nodes, previous models use the same adjacency matrix throughout the training process, which introduces errors during training under the Bayesian Personalized Ranking (BPR) loss function, and ignores the useful information already acquired by the embeddings. In this paper, we theoretically analyze the gradients generated from BPR loss function and prove it cannot train CF models well because of the sparsity of existing CF datasets. Based on such analysis, we propose a method called AdaCF (Adaptive Collaborative Filtering). AdaCF adaptively updates the adjacency matrix by dynamically incorporating the user-item interaction information into the original adjacency matrix during the training process. AdaCF can be applied to any GCN-based CF model, and it is simple to compute and does not require very much extra training time. Experimental results show that AdaCF improves the performance of the base models across all three widely used CF datasets.

Given the finite nature of an organization’s security resources, effectively countering all risks can be quite challenging. Threat hunting involves gathering information to make informed decisions about the allocation of security resources. Part of this responsibility for security personnel includes investigating the attack methods made possible by existing vulnerabilities, identifying potential attackers, and understanding their attack strategies. This study aims to support threat hunting efforts, ultimately aiding in the optimal distribution of security resources. To achieve this goal, we propose a system that combines data from NVD (National Vulnerability Database) and MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge). This system enables us to identify the attack methods that could be executed by exploiting specific vulnerabilities and the potential attackers who may leverage these methods. Through several examples, we have verified that the insights provided by our system align with information available from other sources. By leveraging the proposed system, investigations into attack methods and potential attackers can be conducted more efficiently, requiring fewer steps compared to manual investigations.

Content management systems (CMS) simplify website creation, allowing people without specialized skills, such as designers and corporate public relations departments, to publish their web services. Although the Internet has become more convenient to use, published web services are at risk of various attacks. To realize secure web services, it is essential to incorporate security functions such as user authentication and authorization as well as the detection and blocking of malicious HTTP requests. However, it is difficult to understand and implement appropriate security measures when creating website content. Therefore, this study proposes A+Block, a reverse-proxy-based web security add-on service that provides authentication, authorization, and web application firewall functions for web services. A+Block allows web-service developers to implement these security features by simply pointing to the uniform resource locators of their websites, without the need to modify their websites. By separating the core web-service functionality from security features and offering proxy configuration templates, A+Block simplifies the security implementation for websites and minimizes the configuration burden on web-service operators. We conducted an availability assessment of A+Block and a difficulty assessment of the adoption of WAF, authentication, and authorization in existing web security products. To evaluate the impact of A+Block on web-service availability, we conducted tests on 30 webpages created using the top 30 most frequently used WordPress plugins. Moreover, to evaluate the ease of adoption of A+Block in comparison with existing products, we analyzed the implementation documentation provided by Amazon Web Services (AWS) and Cloudflare. The results confirmed that the solution allows for simple implementation of security functions for web services without compromising their availability.

Stance detection is a key task in natural language processing (NLP) that involves identifying the opinions and attitudes expressed in a text. Cross-target stance detection further extends this task, requiring models to distinguish the stance toward different targets within a text. However, achieving cross-target stance detection remains challenging due to issues such as short and informal text as well as implicit stance expressions. To address this challenge, this paper proposes a multi-level information fusion model for cross-target stance detection. The model first constructs single-target GCN graphs and multi-target GCN graphs, providing each word with a comprehensive semantic framework. Through cross-convolution techniques, the model can obtain weighted information for each word in different contexts, capturing subtle semantic differences of key terms. Then, by utilizing the deep semantic analysis capability of BERT, combined with contrastive learning, the model further refines sentence-level information and enhances its cross-target transferability through adversarial learning. Finally, the overall features are obtained through feature concatenation, enabling effective cross-target stance detection. This approach, which integrates word-level and sentence-level information for cross-target stance detection, not only deeply explores the text’s deep semantics and rich contextual information but also precisely captures the subtle semantic differences at the word level. The proposed method demonstrates excellent performance on the SEM16 and WT-WT datasets, with an average F₁ score 1.7% higher than the best traditional methods, proving its effectiveness and feasibility.

Fonts play a crucial role in graphic design, conveying both text and information. However, selecting a proper font can be challenging due to the overwhelming variety and the need for semantic consistency between text and font shapes. While previous research has focused on word-level font retrieval, real-world design tasks often require selecting fonts for text sequences, such as titles or slogans. This study addresses these challenges by: (1) Proposing S2Font, a model using contrastive learning to create a multimodal embedding space for texts and fonts. (2) Developing a retrieval strategy based on font frequency weighting to handle similarity in retrieval results and the Pareto principle of font usage. (3) Introducing S2Font@Topic, a topic-based extension allowing identical text to return different fonts based on the topic. The methods offer several advantages: (1) Aligning sentence-level text input with real design tasks. (2) Leveraging existing text-font pairs from the Internet without manual annotations. (3) Achieving scalability by encoding new font candidates with the trained font encoder. Experiments demonstrated the methods’ effectiveness. The top 3 retrieved fonts outperformed baseline models, and S2Font’s top choice rivaled those of expert designers. Designers rated S2Font@Topic highly for usefulness (4.67/5) and interest (4.83/5) in design tasks.

This study introduces data augmentation techniques to enhance training datasets for a Japanese handwritten character classification model, addressing the high cost of collecting extensive handwritten character data. A novel method is proposed to automatically generate a large-scale dataset of handwritten characters from a smaller dataset, utilizing a style transformation approach, particularly Adaptive Instance Normalization (AdaIN). Additionally, the study presents an innovative technique to improve character structural information by integrating features from the Contrastive Language-Image Pre-training (CLIP) text encoder. This approach enables the creation of diverse handwritten character images, including Kanji, by merging content and style elements. The effectiveness of our approach is demonstrated by evaluating a handwritten character classification model using an expanded dataset, which includes Japanese hiragana, katakana, and Kanji from the ETL Character Database. The character classification model’s macro F1 score improved from 0.9733 with the original dataset to 0.9861 using the augmented dataset by the proposed approach. This result indicated that our proposed character generation model was able to generate new character images that were not included in the original dataset and that they effectively contributed to training the handwritten character classification model.

Amidst the COVID-19 pandemic, medical protective masks emerged as essential protective gear for the public. This paper aims to construct a nuanced, portable aspect-level sentiment analysis method, designed to unearth insightful information about attitudes toward such masks. The method is built upon three pivotal functional layers: sentiment intensity prediction, classification, and sentiment score calculation, collaboratively revealing consumer sentiments. For predicting sentiment intensity, we employ the Locally Weighted Linear Regression (LWLR) method, enhancing the Chinese VA sentiment lexicon while considering elements like foreign culture and value variations. Additionally, a context-adaptive modifier learning model adjusts word sentiment intensity. Sentiment classification leverages a dynamic XLNet mechanism and utilizes a Bi-LSTM model with stacked residuals for precise results. The sentiment score is astutely calculated by amalgamating sentiment classification and intensity prediction outcomes through the economically-recognized SRC index method. Through a case study using “User Preferences for Mask Attributes” as an example, the method demonstrated exceptional performance across numerous evaluation metrics. Furthermore, a qualitative analysis of the data elucidates the rationale behind varied sentiments concerning medical protective masks and epidemic prevention products.

Mean Variance Estimation networks are models capable of predicting not only the mean but also the variance of a distribution. A recent study has demonstrated that using separate subnetworks for predicting the mean and variance, and training the subnetwork for predicting the mean first (a process called a warm-up) before training the subnetwork for predicting the variance, is more effective than using a single network. However, that study has only utilized the Adam optimizer for training and has not explored quasi-Newton methods, nor varied the subnetwork structures. In this study, we introduce the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method for training a Mean Variance Estimation network and examines how the selection of subnetwork structures affects performance. We conducted an experiment using a synthetic dataset and 11 experiments using real-world datasets to compare the performance of Adam, BFGS, and three other learning methods, including AdaHessian. Out of the 11 experiments using real-world datasets, BFGS outperformed Adam and AdaHessian in seven cases. This also reveals that BFGS tended to perform better on datasets with a larger number of data points. While underfitting was a problem with learning methods other than BFGS, overfitting was a concern with BFGS when it did not achieve the best performance. This overfitting issue can be mitigated with techniques such as early stopping and regularization. Additionally, BFGS required more hidden units for the subnetwork for predicting the mean than for the subnetwork for predicting the variance, and even 0 hidden units were selected as the optimal number for the subnetwork for predicting the variance. It was also observed that, for the subnetwork for predicting the variance, BFGS tended to select more compact models compared to other methods.

In this paper, we propose a fast and lightweight non-parallel voice conversion method based on minimizing the free energy of a restricted Boltzmann machine (RBM). The proposed method employs an RBM that learns the generative probability of acoustic features conditioned on a target speaker and iteratively updates the input acoustic features until their free energy reaches a local minimum, resulting in converted features. Due to the RBM framework, only a few hyperparameters need to be set, and the number of training parameters is minimal, ensuring stable training. When determining the step size of the update formula using the Newton-Raphson method, we found that the Hessian matrix of the free energy can be approximated by a diagonal matrix. This allows for efficient updates with minimal computational costs. In objective evaluation experiments, the proposed method demonstrated approximately 4.5 times faster conversion speed compared with StarGAN-VC and also outperformed StarGAN-VC in terms of Mel-cepstrum distortion. In subjective evaluation experiments, the performance of the proposed method was comparable to that of StarGAN-VC in similarity mean opinion score.

Conventional Multipath QUIC (MPQUIC) scheduler struggles in dynamic networks with multiple clients, significantly hindering its potential. In this letter, a Multi-Agent Reinforcement Learning-based MPQUIC scheduler is designed to optimize communication transmission in dynamic networks for the multi-client scenario. The proposed scheduler is implemented on the server side with a Deep Q-Network (DQN) agent for each client, each agent observes the state of all network flows and adjusts scheduling strategies to enhance the Quality of Service (QoS) for dynamic networks. The simulation results demonstrate that the scheduler significantly outperforms existing schedulers by reducing latency and amplifying throughput for all clients, thus adeptly satisfying the QoS requirements of multiple clients.

The diagnosis of meningioma through magnetic resonance imaging (MRI) holds significant importance in clinical medicine. To enhance the accuracy of meningioma diagnosis, a more effective image classification method is required to comprehensively capture subtle features within MRI images. Although deep learning networks have been successfully applied to this problem, the conventional neural networks based on McCulloch-Pitts neurons suffer from low performance and insufficient feature extraction, due to simplistic structure and neglect of the nonlinear effects of synapses. Therefore, we propose a novel dendritic learning-based ResNeXt model, named DResNeXt. It utilizes the residual structure of ResNeXt and the cardinality method to adequately extract features of MRI images. Then, we innovationally introduce a dendritic neural model to improve the nonlinear information processing of biological neurons for comprehensively handling extracted features. Experimental results demonstrate the outstanding performance of the proposed DResNeXt model in the classification task of meningioma MRI dataset, surpassing the ResNeXt model in preventing overfitting. Additionally, compared to other deep learning models, it exhibits higher accuracy and superior image classification performance.

Ratings of products serve as a crucial indicator for assessing the impact of products in the retail market. Existing methods in rating estimation of product primarily use single-label machine learning methods, where the prediction may fail to represent the whole properties of products. This paper explores a challenging task to obtain product rating distribution estimation (RDE), which predict the distribution of product ratings instead of a single rating. Specifically, we focus on RDE of follower brands product, which provide relatively objective artifacts and easier to collect data. We formulate the RDE task based on a label distribution learning (LDL) framework, which uses the maximum entropy model functions as the output component of LDL, and generate the probability distribution for each category. However, one of the main challenge of conducting the RDE task within the LDL framework is that the large number of labels leads to an exponentially growing output space, which increases model complexity and reduces its performance. To address this problem, we propose a new model, called RDE-LDL, with an adaptive manifold learning module. The RDE-LDL method use uniform manifold approximation and projection (UMAP) to represent the label distribution manifold via fuzzy simplicial sets, which encodes label correlation information, and allows to regularize the maximum entropy model’s output based on label correlation. Quantitative and qualitative experiments conducted on a marketing dataset verified the demonstrates the effectiveness of the RDE-LDL method with the UMAP-based module.

Unmanned Aerial Vehicle (UAV) object detection is impeded by the difficulty of accurately identifying small, densely packed targets. Despite the computational and real-time constraints of UAV platforms, point-based detection methods are favored for their efficiency. However, these methods encounter issues with point competitions due to the dense distribution of targets, resulting in low precision and recall of UAV datasets. This study proposes label reassignment (LR) to mitigate the competitions arising from the label assignment process, focusing on intra-group competitions (IGC) and invasion competitions (IVC). By introducing extended points, our approach enhances accuracy of detectors. Label reassignment also overcomes the secondary competitions (SC) after introducing the extended points. Experimental results demonstrate the effectiveness of our strategy in reducing competitions and improving model accuracy.

This article presents a method to efficiently synthesize high-resolution 3D smoke by using 2D turbulence transfer on cross-sections of the velocity distribution, converting it into a stream function to preserve mass conservation. The goal is to create realistic, high-quality smoke animations with reduced computational cost and time.

This study presents an approach to one-dimensional multi-robot tracking problem using probabilistic model checking. Two of three robots have probabilistic velocity changes that cause unsteady movements and potential collision. The experimental results shows qualitative and quantitative validation indicating applicability of model checking to designing dependable robot control.