Smart contracts are decentralized applications running on Blockchain. A very large number of smart contracts has been deployed on Ethereum. Meanwhile, security flaws of contracts have led to huge pecuniary losses and destroyed the ecological stability of contract layer on Blockchain. It is thus an emerging yet crucial issue to effectively and efficiently detect vulnerabilities in contracts. Existing detection methods like Oyente and Securify are mainly based on symbolic execution or analysis. These methods are very time-consuming, as the symbolic execution requires the exploration of all executable paths or the analysis of dependency graphs in a contract. In this work, we propose ContractWard to detect vulnerabilities in smart contracts with machine learning techniques. First, we extract bigram features from simplified operation codes of smart contracts. Second, we employ five machine learning algorithms and two sampling algorithms to build the models. ContractWard is evaluated with 49502 real-world smart contracts running on Ethereum. The experimental results demonstrate the effectiveness and efficiency of ContractWard. The predictive Micro-F1 and Macro-F1 of ContractWard are over 96% and the average detection time is 4 seconds on each smart contract when we use XGBoost for training the models and SMOTETomek for balancing the training sets.

Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This article reviews recent achievements in this field to bridge this gap and motivate future research. We start with a brief overview of collaboration schemes. After that, we systematically summarize the collaborative perception methods for ideal scenarios and real-world issues. The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application. Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks. Finally, we highlight gaps and overlooked challenges between current academic research and real-world applications.

The visible-infrared person re-identification (VI-ReID) is a challenging ReID task, which aims to retrieve and match the same identity's images between the heterogeneous visible and infrared modalities. Thus, the core of this task is to bridge the huge gap between these two modalities. The existing methods mainly face the problem of insufficient perception of modality information, and can not learn good discriminative modality-invariant embeddings for identities, which limits their performance. To solve these problems, we propose a new cross-modality transformer-based method (CMTR) for this visible-infrared person re-identification task, which can explicitly mine the information of each modality and generate better discriminative features based on it. Specifically, to capture inherent characteristics of modalities, we design the novel modality embeddings, which are fused with token embeddings to encode modality information directly. Moreover, to enhance representation of modality embeddings and adjust the distribution of embeddings, we further propose a modality-aware enhancement loss based on the learned modality information, which contains two components to reduce intra-class distance and enlarging inter-class distance simultaneously. To our knowledge, this is the first exploration of applying pure transformer network to the cross-modality re-identification task. We implement extensive experiments on the public SYSU-MM01 and RegDB datasets, and compared with previous methods, our method achieves good performance with more compact and powerful embeddings for the cross-modality retrieval.

Driverless train control systems are becoming popular in the world. Notably, this technology has already been applied for several years. However, these kinds of Automatic Train Operation systems are still under the framework that network elements and movement authority are generated from the ground control center. Hence, the aforementioned rail systems are “automated” but not “autonomous”, and the architecture utilization and train tracking interval are limited. With the development of advanced sensors and control algorithms, the train intends to be equipped with a decision-making capability. In order to further improve train movement coordination and efficiency, an Autonomous Train Control System (ATCS) is proposed. This paper provides three main contributions: Firstly, the system structure is described, and the information following in ATCS is discussed with general operation scenarios. Secondly, data isolation is broken under ATCS, and data prediction and edge-based information fusion are applied to process the real dynamic data and estimate the train's speed and position. Thirdly, with the assistant of data fusion, model predictive and data-based control methodologies are discussed.

Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct. The key to deal with such problem is to disambiguate the candidate label sets and obtain the correct assignments between instances and their candidate labels. In this paper, we interpret such assignments as instance-to-label matchings, and reformulate the task of PLL as a matching selection problem. To model such problem, we propose a novel Graph Matching based Partial Label Learning (GM-PLL) framework, where Graph Matching (GM) scheme is incorporated owing to its excellent capability of exploiting the instance and label relationship. Meanwhile, since conventional one-to-one GM algorithm does not satisfy the constraint of PLL problem that multiple instances may correspond to the same label, we extend a traditional one-to-one probabilistic matching algorithm to the many-to-one constraint, and make the proposed framework accommodate to the PLL problem. Moreover, we also propose a relaxed matching prediction model, which can improve the prediction accuracy via GM strategy. Extensive experiments on both artificial and real-world data sets demonstrate that the proposed method can achieve superior or comparable performance against the state-of-the-art methods.

Metaverse and intelligent transportation system (ITS) are disruptive technologies that have the potential to transform the current transportation system by decreasing traffic accidents and improving driving safety. The integration of Metaverse and transportation technology, called metaverse transportation system (MTS), can greatly improve the intelligence of real transportation system. The digital models built in MTS help to simulate the full life cycle of physical entities, which equip the virtual space with controllability and flexibility. In this article, we concentrate on the field of environment perception, which is the basic function of intelligent vehicles in MTS. To overcome the poor scalability of traditional environment perception methods, we develop the framework of parallel vision for ITS in metaverse (PVITS), consisting of construction of virtual transportation space, model learning based on computational experiments, and feedback optimization based on parallel execution. This article highlights opportunities brought by PVITS in terms of model precision and generalization improvement. Then, the challenges of PVITS are discussed, i.e., distribution difference between virtual and real transportation space, structure design and theoretical interpretation of vision models, and data security and privacy in virtual transportation space. After that, we present several solutions to tackle the application challenges and fully exploit the superior characteristics of PVITS while attenuating their negative side effects. Some potential applications are also given to represent the effectiveness and reliability of PVITS.