As large language models become increasingly prevalent, their safe and secure application, particularly in preventing the generation of child pornographic content in real-world open-domain dialogues, has become a crucial concern. Despite the urgency of this issue, research efforts are hindered by the lack of dedicated datasets for this area. Addressing this gap, we introduce a pioneering benchmark dataset specifically designed for the detection of child pornography in open-domain dialogues. Recognizing the intrinsic complexities involved in labling such data, we developed a novel Distillation-Based Recurrent Extraction method. This approach enables us to efficiently gather, annotate, and refine the data collection process. Our dataset categorizes dialogues into three distinct sections: non-pornographic, child pornographic, and adult pornographic, ensuring clear differentiation between child and adult content. Through extensive experiments, we demonstrate that LLMs including BERT, RoBERTa, LLaMA, among others, significantly enhance their detection capabilities when fine-tuned with our dataset. This improvement not only attests to the dataset’s immediate utility but also highlights its importance for guiding future research. Furthermore, our findings indicate substantial potential for further advancements in detection performance, emphasizing the critical role of our benchmark dataset in ongoing research efforts.

Lifelong person re-identification (LReID) is developed for dynamic domains where domain distribution is constantly changing due to climate changes, scene changes, etc., and the data can only be collected for a specific scenario over a period of time. With the development of ReID, the issue of clothing changes has also attracted attention. Clothing change itself should be solved more from the perspective of lifelong learning because pedestrians may wear new clothes and the time span of their appearance can be long which can also cause domain changes. Meanwhile, it is difficult to know in advance whether a pedestrian is cloth-changing or cloth-consistent. However, current LReID tasks overlook these issues. To overcome these limitations, we introduce a more practical LReID task, denoted as L4C-ReID (Lifelong Person Re-Identification in Cloth-Changing and Cloth-Consistent Scenarios). This novel task empowers ReID models capable of adapting to incrementally encountered cloth-changing and cloth-consistent domains without prior knowledge of the scenario type and generalizing to unseen domains. A key challenge supposed to be fixed for LReID is the stability-plasticity dilemma. Unlike current LReID methods, which implement plasticity and stability by two contradictory loss items to achieve a sub-optimal balance, we propose an effective scheme termed Unified Stability and Plasticity (USP) that unifies these seemingly disparate concepts to achieve both harmoniously. Taking inspiration from the cognitive processes in the human brain, we decompose the cognitive processes into two independent processes: knowledge representation and knowledge operation. We then design a Knowledge Representation and Operation (KRO) framework to represent and operate the knowledge like the human brain which can better learn new knowledge and consolidate old knowledge to coordinate plasticity and stability. Additionally, we introduce Plasticizing with Stability (PWS) to generalize and optimize the learned knowledge, which integrates the implementation of plasticity and stability into one common objective item to achieve both simultaneously. To simulate the L4C-ReID setup, we gather existing cloth-changing and cloth-consistent datasets to provide a new benchmark. Extensive experiments conducted both on this new benchmark and previous benchmarks established for previous LReID setup, demonstrate the superiority of our method.

Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.

This article presents the analysis of a vernier machine with spoke-V (SV) array permanent magnet (PM), termed SV-PMV. By combining the spoke- and V-array PMs, the topology of SV-PMV is obtained. Based on the general air-gap flux modulation theory, the operating principle of the SV-PMV is investigated. It is found that the main working harmonics can be effectively enhanced by the SV array PM. To provide general design guidelines, the parametric analysis of the SV-PMV is conducted. The electromagnetic performances of three vernier machines are compared by using finite element method (FEM). It is found that the SV-PMV exhibits the highest torque density, the highest power factor, the best overloading capability, and the highest efficiency among three vernier machines.

Most existing Re-ID studies focus on the short-term cloth-consistent setting and thus dominate by the visual appearance of clothing. However, the same person would wear different clothes and different people would wear the same clothes in reality, which invalidates these methods. To tackle the challenge of clothes change, we propose a Universal Clothing Attribute Disentanglement network (UCAD) which can effectively weaken the influence of clothing (identity-unrelated) and force the model to learn identity-related features that are unrelated to the worn clothing. For further study of Re-ID in cloth-changing scenarios, we construct a large-scale dataset called CSCC with the following unique features: (1) Severe: A large number of people have cloth-changing over four seasons. (2) High definition: The resolution of the cameras ranges from 1920×1080 to 3840×2160, which ensures that the recorded people are clear. Furthermore, we provide two variants of CSCC considering different degrees of cloth-changing, namely moderate and severe, so that researchers can effectively evaluate their models from various aspects. Experiments on several cloth-changing datasets including our CSCC and short-term dataset Market-1501 prove the superiority of UCAD. The dataset is available at https://github.com/yomin-y/UCAD.

This paper presents a new vernier permanent magnet machine (VPMM) with stator-V-shaped permanent-magnet arrangement, termed as (SV-VPMM). The key is to adopt unevenly distributed V-shaped PMs in the stator, which exhibits flux concentration effect and generates abundant working harmonics, thus improving the torque density and PM utilization ratio. Based on the air-gap field modulation theory, the working mechanism of the proposed SV-VPMM is investigated from two perspectives including stator-PM and rotor-PM fields. Then, the electromagnetic performance comparison between the proposed SV-VPMM and an existing VPMM is conducted by using finite element analysis (FEA).

This paper proposes a novel flux switching memory machine (FSMM) in which low coercive force (LCF) magnets are alternatively arranged between adjacent U-shape stator cores. The proposed machine exhibits superiority in flexible online flux regulation capability, large flux regulation range, robust structure and inherent advantage for avoiding uncontrolled generator fault (UGF). Meanwhile, the excitation losses can be almost eliminated since the LCF magnets can be remagnetized or demagnetized by a current pulse of a few milliseconds. The demagnetization risk is avoided due to the parallel pattern between PM flux and armature reaction flux. In addition, the FSMM benefits from a simple and robust salient rotor and easy thermal management as a result. Subsequently, the machine structure and operation principle are illustrated. The stator slot-rotor pole combinations are analyzed. The comprehensive electromagnetic performances are evaluated.