Teleoperated robotics, which translates human behavior into robotic actions, remains a critical area of modern robotics. Although autonomous systems have advanced rapidly, they still struggle in complex and unstructured environments, making human-in-the-loop control indispensable for many real-world tasks. Teleoperation platforms commonly rely on motion-tracking technologies to capture detailed operator behavior, which is subsequently converted into robot control commands. However, these rich behavioral signals can also encode operator-specific biometrics, posing privacy risks such as user re-identification. While prior work shows that behavioral biometrics can be leveraged for reliable authentication, privacy leakage in teleoperation-centric motion streams has received comparatively less attention. To address this gap, we introduce a disentangled representation-learning framework based on a Variational Autoencoder (VAE) to suppress identity-revealing cues while retaining task-relevant motion patterns. We evaluate the proposed approach offline on reconstructed trajectories collected from a tele-robotic prototype, where multiple users perform a set of manipulation tasks. Our results demonstrate a substantial reduction in re-identification risk and a favorable privacy–utility trade-off in terms of task utility. More broadly, our findings highlight the need for robust privacy protections in future robotic teleoperation systems.

This paper proposes a task-oriented co-design framework that integrates communication, computing, and control to address the key challenges of bandwidth limitations, noise interference, and latency in mission-critical industrial Cyber-Physical Systems (CPS). To improve communication efficiency and robustness, we design a task-oriented Joint Source-Channel Coding (JSCC) using Information Bottleneck (IB) to enhance data transmission efficiency by prioritizing task-specific information. To mitigate the perceived End-to-End (E2E) delays, we develop a Delay-Aware Trajectory-Guided Control Prediction (DTCP) strategy that integrates trajectory planning with control prediction, predicting commands based on E2E delay. Moreover, the DTCP is co-designed with task-oriented JSCC, focusing on transmitting task-specific information for timely and reliable autonomous driving. Experimental results in the CARLA simulator demonstrate that, under an E2E delay of 1 second (20 time slots), the proposed framework achieves a driving score of 48.12, which is 31.59 points higher than using Better Portable Graphics (BPG) while reducing bandwidth usage by 99.19%.

Existing communication systems aim to reconstruct the information at the receiver side, and are known as reconstruction-oriented communications. This approach often falls short in meeting the real-time, task-specific demands of modern AI-driven applications such as autonomous driving and semantic segmentation. As a new design principle, task-oriented communications have been developed. However, it typically requires joint optimization of encoder, decoder, and modified inference neural networks, resulting in extensive cross-system redesigns and compatibility issues. This paper proposes a novel communication framework that aligns reconstruction-oriented and task-oriented communications for edge intelligence. The idea is to extend the Information Bottleneck (IB) theory to optimize data transmission by minimizing task-relevant loss function, while maintaining the structure of the original data by an information reshaper. Such an approach integrates taskoriented communications with reconstruction-oriented communications, where a variational approach is designed to handle the intractability of mutual information in high-dimensional neural network features. We also introduce a joint source-channel coding (JSCC) modulation scheme compatible with classical modulation techniques, enabling the deployment of AI technologies within existing digital infrastructures. The proposed framework is particularly effective in edge-based autonomous driving scenarios. Our evaluation in the Car Learning to Act (CARLA) simulator demonstrates that the proposed framework significantly reduces bits per service by 99.19% compared to existing methods, such as JPEG, JPEG2000, and BPG, without compromising the effectiveness of task execution.

In the realm of artificial intelligence and linguistics, the automatic generation of humor, particularly puns, remains a complex task. This paper introduces an innovative approach that employs a Generative Adversarial Network (GAN) and semantic pruning techniques to generate humorous puns. We initiate our process by identifying potential pun candidates via semantic pruning. This is followed by the use of contrastive learning to decode the unique characteristics of puns, emphasizing both correct and incorrect interpretations. The learned features from contrastive learning are utilized within our GAN model to better capture the semantic nuances of puns. Specifically, the generator exploits the pruned semantic tree to generate pun texts, while the discriminator evaluates the generated puns, ensuring both linguistic correctness and humor. Evaluation results highlight our model’s capacity to produce semantically coherent and humorous puns, demonstrating an enhancement over prior methods and approach human-level performance. This work contributes significantly to the field of computational humor, advancing the capabilities of automatic pun generation.

The emergence of Large Language Models (LLMs) provides a new solution to text generation tasks that involve high complexity, such as text style transfer (TST) tasks. However, previous studies have not fully explored the TST capabilities of different LLMs, and have faced issues with a lack of uniform standards in the human evaluation stage. This makes the results of human evaluation difficult to reproduce and less credible. To address this, this paper designs a prompt template to guide the cutting-edge LLMs to perform effective text style transfer and carries out an in-depth comparative analysis of various small-scale language models. In the stage of human evaluation, this paper eschews the conventional rating system, opting instead for a comparative human assessment methodology, which we refer to as duel-ranking. This method determines the relative ranking of models through mutual comparison, serving as an alternative to direct scoring. Detailed evaluation instructions are provided herein, to enhance the reproducibility of this method and ensure consistency throughout the evaluation process. This manual evaluation process reveals that GPT-3.5 and GPT-4 exhibit excellent performance in the TST tasks.

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization (C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization (PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk (CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error.

As drones become widely used in different applications, drone authentication becomes increasingly important due to various security risks, e.g., drone impersonation attacks. In this paper, we propose an idea of drone authentication based on Mel-frequency cepstral coefficient (MFCC) using an acoustic fingerprint that is physically embedded in each drone. We also point out that the uniqueness of the drone’s sound comes from the combination of bodies (motors) and propellers. In the experiment with 8 drones, we compare the authentication accuracy of different feature extraction settings. Three kinds of different sound features are used: MFCC, delta MFCC (DMFCC), and delta-delta MFCC (DDMFCC). We choose the feature extraction settings and the sound features according to the best authentication result. In the experiment with 24 drones, we compare the closed set authentication performance of eight machine learning methods in terms of recall under the influence of additive white Gaussian noise (AWGN) with different levels of signal-to-noise ratio (SNR). Furthermore, we conduct an open set drone authentication experiment. Our results show that Quadratic Discriminant Analysis (QDA) outperforms other methods in terms of the highest average recall (94.19%) in the authentication of registered drones and the third highest average recall (82.35%) in the authentication of unregistered drones.

Xianbing Zhou, Yang Yong, Xiaochao Fan, Ge Ren, Yunfeng Song, Yufeng Diao, Liang Yang, Hongfei Lin. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Pre-trained distributed word representations have been proven useful in various natural language processing (NLP) tasks. However, the effect of words’ geometric structure on word representations has not been carefully studied yet. The existing word representations methods underestimate the words whose distances are close in the Euclidean space, while overestimating words with a much greater distance. In this paper, we propose a word vector refinement model to correct the pre-trained word embedding, which brings the similarity of words in Euclidean space closer to word semantics by using manifold learning. This approach is theoretically founded in the metric recovery paradigm. Our word representations have been evaluated on a variety of lexical-level intrinsic tasks (semantic relatedness, semantic similarity) and the experimental results show that the proposed model outperforms several popular word representations approaches.

Heterographic pun plays a critical role in human writing and literature, which usually has a similar sounding or spelling structure. It is important and difficult research to recognize the heterographic pun because of the ambiguity. However, most existing methods for this task only focus on designing features with rule-based or machine learning methods. In this paper, we propose an end-to-end computational approach - Pronunciation Spelling Understanding Gated Attention (PSUGA) network. For pronunciation, we exploit the hierarchical attention model with phoneme embedding. While for spelling, we consider the character-level, word-level, tag-level, position-level and contextual-level embedding with attention model. To deal with the two parts, we present a gated attention mechanism to control the information integration. We have conducted extensive experiments on SemEval2017 task7 and Pun of the Day datasets. Experimental results show that our approach significantly outperforms state-of-the-art methods.

Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.