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Telephone scams, with their profound psychological impact, often compel victims to make hasty and severe decisions. Studying these scams is challenging due to the scarcity of comprehensive datasets, a result of the private nature of telephone interactions. In this paper, we introduce ScamGen , a template-based data augmentation technique designed to enhance Chinese telephone scam data. ScamGen leverages psychological insights to generate diverse and realistic scam scenarios, focusing on the psychological dynamics between scammers and victims. This novel approach integrates psychological theory with data augmentation, diverging from traditional methods by emphasizing scammer–victim interactions. Our method begins with a multi-source data collection framework, compiling an initial seed dataset of tele-scam samples. Using sentence- and word-level perturbations, we expand this seed data to create a comprehensive and diverse dataset covering a wide range of scam scenarios. Rigorous evaluations demonstrate that ScamGen outperforms large language models in generating high-quality, varied datasets. Additionally, we develop five deep learning models for intent detection on this dataset, with BERT achieving the highest precision at 86.68%. The dataset, which will be made publicly available, marks a significant step toward understanding scammer tactics and improving tele-scam detection systems. • Introduces ScamGen , a technique for enhancing Chinese telephone scam data. • Leverages psychological insights to generate diverse scam scenarios. • Expands a seed dataset using sentence- and word-level perturbations. • Outperforms large language models in quality and variety of datasets. • Develops deep learning models for intent detection, with BERT achieving 86.68%. [ABSTRACT FROM AUTHOR]

Computer-Aided Sperm Analysis (CASA) is a widely studied topic in the diagnosis and treatment of male reproductive health. Although CASA has been evolving, there is still a lack of publicly available large-scale image datasets for CASA. To fill this gap, we provide the Sperm Videos and Images Analysis (SVIA) dataset, including three different subsets, subset-A, subset-B and subset-C, to test and evaluate different computer vision techniques in CASA. For subset-A, in order to test and evaluate the effectiveness of SVIA dataset for object detection, we use five representative object detection models and four commonly used evaluation metrics. For subset-B, in order to test and evaluate the effectiveness of SVIA dataset for image segmentation, we used eight representative methods and three standard evaluation metrics. Moreover, to test and evaluate the effectiveness of SVIA dataset for object tracking, we have employed the traditional k NN with progressive sperm (PR) as an evaluation metric and two deep learning models with three standard evaluation metrics. For subset-C, to prove the effectiveness of SVIA dataset for image denoising, nine denoising filters are used to denoise thirteen kinds of noise, and the mean structural similarity is calculated for evaluation. At the same time, to test and evaluate the effectiveness of SVIA dataset for image classification, we evaluate the results of twelve convolutional neural network models and six visual transformer models using four commonly used evaluation metrics. Through a series of experimental analyses and comparisons in this paper, it can be concluded that this proposed dataset can evaluate not only the functions of object detection, image segmentation, object tracking, image denoising, and image classification but also the robustness of object detection and image classification models. Therefore, SVIA dataset can fill the gap of the lack of large-scale public datasets in CASA and promote the development of CASA. Dataset is available at: https://github.com/Demozsj/Detection-Sperm. [ABSTRACT FROM AUTHOR]

Sensor fusion plays a key part in autonomous surface vehicles, however, the high cost of sensors makes the barrier of entry in this research field quite high. In this work, we present a complete system for sensor fusion on the milliAmpere autonomous ferry research platform as well as an open sensor fusion dataset for maritime tracking across two environments. Individual sensors and their detection pipelines are evaluated across various detection metrics. We also evaluate the tracking performance of the sensors both individually and in fusion using a multi-sensor extension of the JIPDA multi-target tracker. We find that the different environments have distinct challenges precluding the use of only a single sensor. Utilizing multiple sensors, either individually or in fusion, can mitigate these issues increasing the safety margins of the situational awareness system. • Experimental validation of a multi-sensor extension of the JIPDA multi-target tracker. • In-depth description of a complete sensor fusion pipeline from sensor data to detection and tracking. • Open sensor fusion dataset for maritime multi-target tracking with multiple environments and scenarios. • Multiple sensors improve safety margins across differing environments. [ABSTRACT FROM AUTHOR]

We introduce a novel dataset containing 3-dimensional biomechanical and wearable sensor data from 22 able-bodied adults for multiple locomotion modes (level-ground/treadmill walking, stair ascent/descent, and ramp ascent/descent) and multiple terrain conditions of each mode (walking speed, stair height, and ramp inclination). In this paper, we present the data collection methods, explain the structure of the open dataset, and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics as a function of the gait phase. This dataset offers a comprehensive source of locomotion information for the same set of subjects to motivate applications in locomotion recognition, developments in robotic assistive devices, and improvement of biomimetic controllers that better adapt to terrain conditions. With such a dataset, models for these applications can be either subject-dependent or subject-independent, allowing greater flexibility for researchers to advance the field. [ABSTRACT FROM AUTHOR]