Copebot: Underwater soft robot with copepod-like locomotion

It has been a great challenge to develop robots that are able to perform complex movement patterns with high speed and, simultaneously, high accuracy. Copepods are animals found in freshwater and saltwater habitats that can have extremely fast escape responses when a predator is sensed by performing explosive curved jumps. Here, we present a design and build prototypes of a combustion-driven underwater soft robot, the "copebot", that, like copepods, is able to accurately reach nearby predefined locations in space within a single curved jump. Because of an improved thrust force transmission unit, causing a large initial acceleration peak (850 Bodylength*s-2), the copebot is 8 times faster than previous combustion-driven underwater soft robots, whilst able to perform a complete 360{\deg} rotation during the jump. Thrusts generated by the copebot are tested to quantitatively determine the actuation performance, and parametric studies are conducted to investigate the sensitivities of the input parameters to the kinematic performance of the copebot. We demonstrate the utility of our design by building a prototype that rapidly jumps out of the water, accurately lands on its feet on a small platform, wirelessly transmits data, and jumps back into the water. Our copebot design opens the way toward high-performance biomimetic robots for multifunctional applications.

Deep Joint Source-Channel Coding for Wireless Image Transmission with Semantic Importance

The sixth-generation mobile communication system proposes the vision of smart interconnection of everything, which requires accomplishing communication tasks while ensuring the performance of intelligent tasks. A joint source-channel coding method based on semantic importance is proposed, which aims at preserving semantic information during wireless image transmission and thereby boosting the performance of intelligent tasks for images at the receiver. Specifically, we first propose semantic importance weight calculation method, which is based on the gradient of intelligent task's perception results with respect to the features. Then, we design the semantic loss function in the way of using semantic weights to weight the features. Finally, we train the deep joint source-channel coding network using the semantic loss function. Experiment results demonstrate that the proposed method achieves up to 57.7% and 9.1% improvement in terms of intelligent task's performance compared with the source-channel separation coding method and the deep sourcechannel joint coding method without considering semantics at the same compression rate and signal-to-noise ratio, respectively.

VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we analyze the development trend of the new generation machine learning framework and introduce the design principles of VQNet 2.0 in detail: unity, practicality, efficiency, and compatibility, as well as full particulars of implementation. We illustrate the functions of VQNet 2.0 through several basic applications, including classical convolutional neural networks, quantum autoencoders, hybrid classical-quantum networks, etc. After that, through extensive experiments, we demonstrate that the operation speed of VQNet 2.0 is higher than the comparison method. Finally, through extensive experiments, we demonstrate that VQNet 2.0 can deploy on different hardware platforms, the overall calculation speed is faster than the comparison method. It also can be mixed and optimized with quantum circuits composed of multiple quantum computing libraries.

Secure Semantic Communications: Fundamentals and Challenges

Semantic communication allows the receiver to know the intention instead of the bit information itself, which is an emerging technique to support real-time human-machine and machine-to-machine interactions for future wireless communications. In semantic communications, both transmitter and receiver share some common knowledge, which can be used to extract small-size information at the transmitter and recover the original information at the receiver. Due to different design purposes, security issues in semantic communications have two unique features compared to standard bit-wise communications. First, an attacker in semantic communications considers not only the amount of stolen data but also the meanings of stolen data. Second, an attacker in semantic communication systems can attack not only semantic information transmission as done in standard communication systems but also attacks machine learning (ML) models used for semantic information extraction since most of semantic information is generated using ML based methods. Due to these unique features, in this paper, we present an overview on the fundamentals and key challenges in the design of secure semantic communication. We first provide various methods to define and extract semantic information. Then, we focus on secure semantic communication techniques in two areas: information security and semantic ML model security. For each area, we identify the main problems and challenges. Then, we will provide a comprehensive treatment of these problems. In a nutshell,this article provides a holistic set of guidelines on how to design secure semantic communication systems over real-world wireless communication networks.

Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning

Zero-Shot Learning has been a highlighted research topic in both vision and language areas. Recently, most existing methods adopt structured knowledge information to model explicit correlations among categories and use deep graph convolutional network to propagate information between different categories. However, it is difficult to add new categories to existing structured knowledge graph, and deep graph convolutional network suffers from over-smoothing problem. In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation. Our semantic enhanced knowledge graph can further enhance the correlations among categories and make it easy to absorb new categories. To propagate information on the knowledge graph, we propose a novel Residual Graph Convolutional Network (ResGCN), which can effectively alleviate the problem of over-smoothing. Experiments conducted on the widely used large-scale ImageNet-21K dataset and AWA2 dataset show the effectiveness of our method, and establish a new state-of-the-art on zero-shot learning. Moreover, our results on the large-scale ImageNet-21K with various feature extraction networks show that our method has better generalization and robustness.

Holistic risk assessment of inference attacks in machine learning

As machine learning expanding application, there are more and more unignorable privacy and safety issues. Especially inference attacks against Machine Learning models allow adversaries to infer sensitive information about the target model, such as training data, model parameters, etc. Inference attacks can lead to serious consequences, including violating individuals privacy, compromising the intellectual property of the owner of the machine learning model. As far as concerned, researchers have studied and analyzed in depth several types of inference attacks, albeit in isolation, but there is still a lack of a holistic rick assessment of inference attacks against machine learning models, such as their application in different scenarios, the common factors affecting the performance of these attacks and the relationship among the attacks. As a result, this paper performs a holistic risk assessment of different inference attacks against Machine Learning models. This paper focuses on three kinds of representative attacks: membership inference attack, attribute inference attack and model stealing attack. And a threat model taxonomy is established. A total of 12 target models using three model architectures, including AlexNet, ResNet18 and Simple CNN, are trained on four datasets, namely CelebA, UTKFace, STL10 and FMNIST.

FakeEdge: Alleviate Dataset Shift in Link Prediction

Link prediction is a crucial problem in graph-structured data. Due to the recent success of graph neural networks (GNNs), a variety of GNN-based models were proposed to tackle the link prediction task. Specifically, GNNs leverage the message passing paradigm to obtain node representation, which relies on link connectivity. However, in a link prediction task, links in the training set are always present while ones in the testing set are not yet formed, resulting in a discrepancy of the connectivity pattern and bias of the learned representation. It leads to a problem of dataset shift which degrades the model performance. In this paper, we first identify the dataset shift problem in the link prediction task and provide theoretical analyses on how existing link prediction methods are vulnerable to it. We then propose FakeEdge, a model-agnostic technique, to address the problem by mitigating the graph topological gap between training and testing sets. Extensive experiments demonstrate the applicability and superiority of FakeEdge on multiple datasets across various domains.

X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection

Detecting sarcasm and verbal irony from people's subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios. This paper describes the X-PuDu system that participated in SemEval-2022 Task 6, iSarcasmEval - Intended Sarcasm Detection in English and Arabic, which aims at detecting intended sarcasm in various settings of natural language understanding. Our solution finetunes pre-trained language models, such as ERNIE-M and DeBERTa, under the multilingual settings to recognize the irony from Arabic and English texts. Our system ranked second out of 43, and ninth out of 32 in Task A: one-sentence detection in English and Arabic; fifth out of 22 in Task B: binary multi-label classification in English; first out of 16, and fifth out of 13 in Task C: sentence-pair detection in English and Arabic.

Self-attention based high order sequence feature reconstruction of dynamic functional connectivity networks with rs-fMRI for brain disease classification

Dynamic functional connectivity networks (dFCN) based on rs-fMRI have demonstrated tremendous potential for brain function analysis and brain disease classification. Recently, studies have applied deep learning techniques (i.e., convolutional neural network, CNN) to dFCN classification, and achieved better performance than the traditional machine learning methods. Nevertheless, previous deep learning methods usually perform successive convolutional operations on the input dFCNs to obtain high-order brain network aggregation features, extracting them from each sliding window using a series split, which may neglect non-linear correlations among different regions and the sequentiality of information. Thus, important high-order sequence information of dFCNs, which could further improve the classification performance, is ignored in these studies. Nowadays, inspired by the great success of Transformer in natural language processing and computer vision, some latest work has also emerged on the application of Transformer for brain disease diagnosis based on rs-fMRI data. Although Transformer is capable of capturing non-linear correlations, it lacks accounting for capturing local spatial feature patterns and modelling the temporal dimension due to parallel computing, even equipped with a positional encoding technique. To address these issues, we propose a self-attention (SA) based convolutional recurrent network (SA-CRN) learning framework for brain disease classification with rs-fMRI data. The experimental results on a public dataset (i.e., ADNI) demonstrate the effectiveness of our proposed SA-CRN method.