To realize a wide range of functions in a complex environment, the transition is necessary between various motions suited to each environment. This article presents a transformable hexapod robot, RHex-T3, which is capable of switching to leg, wheel, and RHex mobile modes to improve its flexibility, and climbing the ladders with the hook-mode. The mechanical design and the implementation of the RHex-T3 are introduced, especially the coaxial transmission mechanism and the innovative 2-degree-of-freedom transformable leg. As the result, experiments are conducted to demonstrate the feasibility and successful performance of the proposed coaxial transformable mechanism. The locomotion performance under three basic mobile modes is evaluated through a series of indoor and outdoor experiments. Furthermore, the ladder climbing function is also verified and its boundary conditions are discussed.

Sarcasm often manifests itself in some implicit language and exaggerated expressions. For instance, an elongated word, a sarcastic phrase, or a change of tone. Most research on sarcasm detection has recently been based on text and image information. In this paper, we argue that most image data input to the sarcasm detection model is redundant, for example, complex background information and foreground information irrelevant to sarcasm detection. Since facial details contain emotional changes and social characteristics, we should pay more attention to the image data of the face area. We, therefore, treat text, audio, and face images as three modalities and propose a multimodal deep-learning model to tackle this problem. Our model extracts the text, audio, and image features of face regions and then uses our proposed feature fusion strategy to fuse these three modal features into one feature vector for classification. To enhance the model’s generalization ability, we use the IMGAUG image enhancement tool to augment the public sarcasm detection dataset MUStARD. Experiments show that although using a simple supervised method is effective, using a feature fusion strategy and image features from face regions can further improve the F1 score from 72.5% to 79.0%.

Echo‐hiding has been widely studied for audio watermarking. This study proposes a more secure echo‐hiding method based on modified pseudo‐noise (PN) sequence and robust principal component analysis (RPCA). In the proposed method, the RPCA is used to decompose the original audio signal into low‐rank and sparse parts and then a pair of opposite modified PN sequences is employed to embed watermarks. The modified PN sequence improves the robustness of watermark detection by providing additional correlation peaks. Meanwhile, benefit from the RPCA and the opposite PN sequences, the security of the proposed method is improved since watermarks cannot be detected from the whole signal even if the PN sequence is known, which is an obvious improvement compared with the previous PN‐based echo‐hiding methods. In the watermark detection process, the authors make use of the low‐rank and sparse characteristics of the watermarked signal to detect watermarks from the low‐rank and sparse parts, respectively. Based on this basic framework, they also propose a multi‐bit embedding scheme, which obtains a doubled embedding capacity compared with the previous PN‐based echo‐hiding methods. The proposed method was evaluated with respect to inaudibility, security, and robustness. The experiment results verified the effectiveness of the proposed method.

The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human key point detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of key points. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model’s robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to reweight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded datasets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores.

Audio watermarking has been developed to protect the copyright of audio signals. We considered the use of the distribution of the phase spectrum and propose an effective multi-bit audio watermarking method based on phase shifting. The proposed method is implemented on the basis of a frame-wise framework. The phase spectrum of each frame is first calculated, then, in accordance with the embedded bit number of each frame, the phase bins are segmented into several subsets. An artificial phase pattern is constructed in one of these subsets to carry a specific multi-bit watermark. In the water-mark extraction process, each frame is synchronized by identifying the phase pattern, then watermarks are extracted. We investigate the performance of the proposed method, and the results indicated that the proposed method balances the trade-off of inaudibility and robustness. From comparative evaluations, the proposed method also performed better than several other audio watermarking methods.