Assessing data quality in multivariate time-series datasets is crucial for reliable analysis, particularly when dealing with missing values, inconsistent feature availability, and massive records in large-scale edge computing and IoT clusters. Existing methods often fall short of capturing intricate patterns of missingness and co-coverage, restricting the capacity to make well-informed decisions regarding the usability of the data. In order to systematically extract reliable data segments, this paper presents a comprehensive framework that combines a heuristic model with temporal coverage, period-specific missingness, and co-coverage metrics. By integrating these metrics with visualizations such as temporal coverage heatmaps and parallel coordinates plots, the framework reveals complex patterns of missingness while supporting human involvement in validating data subsets. Our approach effectively balances automation with expert judgment, enhancing the interpretability of data quality assessments. The findings show that the proposed methods satisfy the design specifications for revealing patterns, quantifying missingness impact, measuring feature availability, guiding feature selection, and facilitating scalable, multi-scale data summarization. The framework offers a solid way to improve the quality of data in multivariate time-series analysis, opening the door to more precise and trustworthy insights for assessing data gathered from edge computing infrastructures and large-scale, heterogeneous IoT deployments, where data consistency and completeness are frequently very variable.

Accurate detection of Tumor Spread Through Air Spaces (STAS) is pivotal for patient prognosis assessment and therapeutic strategies. Current mainstream histopathology images detection methodologies rely on single Field-of-View (FoV), which overlook global context and are susceptible to misclassifying STAS. In addition, despite good progress in utilizing multi FoV for histopathology images detection, the semantic gap that exists between different FoV tasks reduces model performance. To address these issues, we introduce the Multi FoV Channel-wise Transformer Detection Model (MFCT), which harnesses cross attention mechanisms to fuse multi FoV features. MFCT enhances the capacity of small FoV to capture contextual information, thereby augmenting model precision. Moreover, In order to enhance the application scenarios of the model, the model employs a detection head and a segmentation head respectively. To evaluate MFCT’s performance, experiments were carried out using both single and multi FoV setups, employing the publicly available Ocelot dataset as well as our custom STAS dataset. MFCT achieved a F1 score of 73.4% on the Ocelot dataset and 85.2% on our STAS dataset. Specifically, F1 scores improved by 5% compared to the single FoV model and by 3% compared to the multi FoV baseline model on the Ocelot, and the results indicate that MFCT outperforms the other method compared. The empirical evidence suggests that our model furnishes a detection framework that facilitates and accelerates the exploitation of multi FoV information in histopathological image detection. • Leveraging multi Field of View information for accurate pathology image detection. • Building a channel-based Transformer to leverage large Field of View information. • Model performance exceeds current methods using large Field of View information. • Detection model uses large Field of View info without extra annotations.

Federated Learning (FL) has surged in popularity, allowing machine learning models to be collaboratively trained using decentralized client data, all while upholding privacy and security standards. However, leveraging locally-stored data introduces challenges related to data heterogeneity. While many past studies have addressed this non-IID problem, they often overlook the dynamic nature of each individual client’s data or disrupt its continuous shift. In this paper, our emphasis is on the challenges posed by temporal data distribution shift alongside non-IID data across clients, a more prevalent yet complex situation in real-world FL. We propose to analytically capture the evolving nature of each local data distribution, by modeling them as a time-varying composite of multiple latent Gaussian distributions. We then employ the expectation maximization (EM) algorithm to deduce the distribution model parameters based on the prevailing observed training data, ensuring that the learned mixture proportion weights mirror a consistent trajectory. Additionally, by embedding an adaptive data partitioning method into the EM algorithm and using each partition to train a distinct sub-model, we realize an intuitive and novel personalized FL paradigm. This refines the FL training by exploiting the heterogeneity and temporal shifts of clients’ datasets. We derive analytical results to guarantee the convergence of our training method. Comprehensive tests across diverse datasets and distribution configurations also underscore our enhanced efficacy compared to several state-of-the-art.

Abstract In this study, a dual‐function radar‐communications (DFRC) system based on the circulating code array is presented to address the contradiction between radar and communications system in beam scanning and beam coverage. Processed orthogonal frequency‐division multiplexing (OFDM) signal is transmitted by the circulating code array as the base signal to improve the data rate. Following the spatial angle of the communication receiver, the communication symbols are modulated to part of OFDM signal subcarriers occupying a specific frequency band. A significant property of the circulating code array, which provides a relationship between the baseband frequency of the base signal and the spatial angles, implements a basis for safe telecommunication transmission towards the cooperative receiver and demodulation. Moreover, the circulating code array transmits the same signal and introduces the same time interval between adjacent array elements. Therefore, the complex problems of multi‐dimensional orthogonal signal design in the traditional multiple‐input‐multiple‐output‐based DFRC system design are transformed into a simple base signal design. Finally, an omnidirectional coverage pattern is obtained. Thus, whether the communication receiver is in the mainlobe or the sidelobe of the radar beam, the communication connection can be established between the designed DFRC system and the communication users. The performance of the described DFRC system is verified through theoretical analysis and simulations.

Requirements terminology defines and unifies key specialized and/or technical concepts of the software system, which is significant for understanding the application domain in requirements engineering (RE). However, manual terminology extraction from natural language requirements is laborious and expensive, especially with large scale requirements specifications. In this paper, we aim to employ natural language processing (NLP) techniques and machine learning (ML) algorithms to automatically extract and rank the requirements terms to support high-level feature modeling. To this end, we propose an automatic framework composed of noun phrase identification technique for requirements terms extraction and TextRank combined with semantic similarity for terms ranking. The final ranked terms are organized as a hierarchy, which can be used to help name elements when performing feature modeling. In the quantitative evaluation, our extraction method performs better than three baseline methods in recall with comparable precision. Moreover, our adapted TextRank algorithm can rank more relevant terms at the top positions in terms of average precision compared with most baselines. An illustrative example on the smart home domain further shows the usefulness of our framework in aiding elements naming during feature modeling. The research results suggest that proper adoption and adaption of NLP and ML techniques according to the characteristics of specific RE task could provide automation support for problem domain understanding.

Radio tomographic imaging (RTI) based on received signal strength (RSS) measurements has emerged to be one of promising and effective technologies to reveal the obstacle in the resulting attenuation image. Suffering from the coarse elliptical weighting model and the multipath interference, the traditional RTI is unable to accurately map the obstacle, especially in outline recognition. In this paper, we demonstrate an improved RTI method for obstacle mapping. Since the detail mapping requires radio propagation more concentrated, we apply the inverse area elliptical propagation model to describe the RSS attenuation occurred in the propagation path across the obstacle. Moreover, introducing the directional information into the spatial correlation matrix, we enhance the imaging accuracy by a modified Tikhonov regularizer with a non-negative constraint. Field mapping experiments using directional antennas are performed with obstacles built of different materials. Experimental results suggest that the obstacle mapping quality of the improved method is better than that of the traditional RTI method.

We examined the production and perception of (contrastive) prosodic focus, using a paradigm based on digit strings, in which the same material and discourse contexts can be used in different languages. We found a striking difference between languages like English and Mandarin Chinese, where prosodic focus is clearly marked in production and accurately recognized in perception, and languages like Korean, where prosodic focus is neither clearly marked in production nor accurately recognized in perception. We also present comparable production data for Suzhou Wu, Japanese, and French.