The intelligent development of high-speed railways (HSRs) necessitates support for various services to ensure safe and reliable train operations while providing high-quality travel experiences for passengers. Network slicing presents a promising solution via isolated and service-specific radio resource management. However, meeting heterogeneous quality of service (QoS) requirements in HSR communications is particularly challenging due to imperfect channel state information (CSI) caused by high-speed mobility. In this work, we investigate a slicing puncture strategy in a moving network with an imperfect train-to-ground downlink, supporting passenger entertainment and safety-related services. The system includes two transmission links: outboard and inboard. Given the impact of high-speed mobility, we characterize the statistical probability distribution of the actual CSI and model the average transmission rates in the outboard link. We aim to minimize the system Resource Block (RB) and power in the above two links while satisfying the diverse QoS requirements of services. Since the resource minimization problem is a mixed-integer nonlinear programming, we decompose it into three subproblems: RB allocation, power allocation, and slicing puncture optimization. A Speed-Aware Resource allocation and Slicing puncture (SA-RS) algorithm is proposed. Specifically, analytical expressions are derived for RB allocation, and a bisection-based algorithm is designed for power allocation. Moreover, the slicing puncture strategy is obtained using a genetic-based algorithm. Simulation results demonstrate that the proposed strategy can improve the system performance compared with other baseline schemes under imperfect CSI.

In recent years, a new fraud method, namely smart contract honeypots, has emerged on the famous blockchain platform Ethereum. The difference from smart contract vulnerabilities is that the contract honeypot essentially has no vulnerabilities, luring victims to call in a seemingly vulnerable form. However, the victims ultimately cannot obtain the desired benefits and will lose certain funds. Deep learning algorithms are preferred among current contract honeypot detection methods because they can learn more general characteristics and do not rely on expert experience. Most previous works use natural language models to learn the opcodes of contract honeypots but overlook the relevant structural features of the source code. We propose a novel method called the Smart Contract Honey-pot Data Flow Graph, which utilizes a data flow graph to extract the calling relationships of critical source code within contract honeypots and employs a pre-trained model for representation learning. First, contract honeypots generally have a code that transfers money to the calling address, which is critical information for constructing a source code data flow graph. Then, the pre-trained model is used to learn the source code representation and perform downstream classification tasks. The F1-score of our model significantly outperforms the state-of-the-art approaches in the contract honeypot classification task and is close to the highest performance in the detection task. In addition, this model is an end-to-end model that can detect unknown-type contract honeypots.

The intelligent development of high-speed railways (HSRs) necessitates the support of multiple services to ensure the safe and reliable operations of trains, and high-quality travel experiences for passengers. Network slicing offers a promising solution through isolated and service-specific radio resource management. However, meeting heterogeneous quality of service (QoS) requirements in HSR communications with imperfect channel state information (CSI) presents a great challenge. In this paper, we propose a bandwidth allocation and slicing puncture strategy in a train-to-ground moving network to support safetyrelated driver assistance services (DAS) and high-throughput video-on-demand services (VDS) for passengers. We formulate the resource slicing problem to minimize system resource block allocation, considering the throughput constraint of VDS and the latency and jitter constraints of VDS. The original problem is divided into two subproblems: VDS RB allocation and DAS puncturing. For VDS RB allocation, we transform the subproblem into a convex form and derive a closed-form expression. For DAS puncturing, a Genetic-based mini-slots puncturing (GMP) algorithm is proposed to address the non-convexity. Simulation results demonstrate the effectiveness of the proposed block coordinate descent-based RB allocation and puncturing (BCDAP) algorithm under imperfect CSI conditions, outperforming the baseline schemes.

Kilometer-scale Earth system models (ESMs) necessitate exascale supercomputers to facilitate realistic simulations of weather phenomena and climate variability over a time span ranging from days to decades. We present AP3ESM, an ultra‑high‑resolution, AI‑Powered, Performance‑Portable ESM coupling atmosphere, land surface, ocean, and sea ice components. By leveraging the performance portability features of Kokkos and OpenMP, the AP3ESM operates efficiently on two heterogeneous systems while incurring minimal development overhead. Advanced optimization techniques, such as adaptive parallel algorithms, AI-enhanced physical parameterizations, and mixed-precision computations, have been implemented to further boost the computational efficiency. Breaking the 1-km resolution barrier, AP3ESM delivers 0.85 and 1.98 simulated-years-per-day (SYPD) for the standalone atmosphere and ocean components on 34.1 million Sunway cores and 16085 GPUs, respectively; the holistic AP3ESM achieves 0.54 SYPD on 37.2 million Sunway cores. Notably, the forecast experiment successfully captures Super Typhoon Doksuri in 2023 and its associated extreme rainfall across China.

The Internet of Things (IoT) has expanded its reach through applications like environmental monitoring and smart agriculture. Its growth is often limited by the need for extensive IoT gateways and base stations. Satellite-based IoT, using low Earth orbit (LEO) satellites, offers a solution by enabling global sensing without terrestrial infrastructure. This technology allows IoT devices to transmit data to satellite gateways, which is crucial for dynamic environments like the ocean where nodes drift with currents. In such applications, position information is valuable for data analysis and tracing. Traditional GPS modules, while affordable and low-energy, can be inefficient due to the large payload space required for precise positioning data. The payload used to report the position might be as long as the sensed data itself. This paper proposes a novel approach to terrestrial IoT device positioning using LoRa or FSK modulation, where positioning is performed on the satellite, saving GPS bytes and reducing packet size. By utilizing Doppler effects and satellite motion, the method calculates device positions, forming a positioning equation set without interfering with communication tasks. Challenges in Doppler effect measurement and positioning are addressed with a fine-grained frequency shift measurement method using ZoomFFT and zero-padding FFT, and a non-linear equation set is solved to determine the positions of terrestrial nodes, with constraints added to ensure practical solutions. The framework is validated through simulations, demonstrating a significant reduction in payload length and positioning accuracy comparable to GPS. In ideal cases, the framework can locate nodes with errors less than 60 meters without any GPS information from data packets.

One attack method that actively deploys smart contract honeypots has recently become popular. A contract honeypot is a smart contract that pretends to have vulnerabilities, enticing victims who call the contract to lose funds. However, previous works detected contract honeypots by individual characteristics, such as codes and ledger details. They overlooked the connection between the two parties in the transaction. Therefore, we propose the HoneyRank algorithm, which uses known honeypots as initial seeds to construct a contract honeypot transaction relationship network (HoneyNet) and source code text similarity detection to discover 0-day honeypots that previous work missed in the same detected block height range. This low-cost method detects only a few highly suspicious smart contracts and does not require machine learning training. Specifically, we trace transaction history data to collect the accounts and relationships of honeypot seeds, attackers, and victims and construct a HoneyNet. Based on transaction behavior inference, we label and calculate the source code similarity between high-risk smart contracts and ground truth honeypots. Finally, we select the high-similarity smart contracts to confirm honeypots manually. Besides, we analyze the criminal associations in a HoneyNet. As far as we know, we are the first to construct a HoneyNet and use it to find new honeypots. These honeypots visually reveal the potential connections between the attackers (creators of the honeypot) and the victims. We discovered 54 0-day honeypots never found by previous methods and mined 11 attacker communities composed of attackers and puppet accounts for the first time.

Abstract We present swRender, a new parallel rendering pipeline based on the new Sunway many-core architecture (SW26010P) for the Monte Carlo path-tracing algorithm. Previous parallel rendering schemes are unsuitable for our task due to issues such as vast differences in hardware architectures and bottlenecks in I/O communication efficiency. To that end, we create a new two-level parallel tile rendering framework to fully utilize the Sunway computing resources, a practical tile-grouping load-balancing method to maintain the framework’s stability, and a novel many-core acceleration optimization to improve the rendering performance at the pixel level. Our method achieves (1) an average speedup of 16x in multiple benchmarks when compared to the baseline path-tracing model on the Sunway architecture, and (2) an average speedup of 2x when compared to state-of-the-art CPU, co-processor, and GPU-based parallel rendering approaches. Moreover, we scale swRender to run on 15 million cores and obtain high scalable parallel efficiency of 92%.

DNS protocol is a plaintext domain name resolution protocol, which has the risk of privacy disclosure. DNS over HTTPS (DOH) protocol is designed to encrypt DNS traffic, which solves the privacy problem. However, many network attackers use the DOH tunnel for malicious transmission. From the passive traffic, there is no obvious difference between normal DOH traffic and DOH tunnel traffic, which brings great challenges to identify them. At present, researches mainly focus on the plaintext DNS covert tunnel, but less on the encrypted DOH tunnel. In this paper, we propose DOH covert tunnel detection method based on core features and machine learning method using two steps. Firstly, we detect DOH traffic according to the threshold of features. On this basis, we use core features and machine learning methods to detect tunnel traffic in all DOH traffic. Finally, we use self collected and public datasets to verify our method. The results show that the method achieves up to 99 % precision and recall that is superior to state of the art method.

Supporting mesh clients roaming without negative impact on the services with high priority is an important challenge in the design and deployment of a wireless mesh network which has emerged as a promising last mile broadband wireless access technology. In this paper, to improve the network performance, a handoff management scheme is proposed for wireless mesh networks. When the mesh client (MC) roams to the overlapping region between its associated mesh router (MR) and a new MR, these two MR transmit the information MC required simultaneously to decrease the error probability. On the other hand, when MC needs to handoff, it selects an optimal MR using priority and bandwidth requirements as metrics to ensure the quality of service of the on-going traffic. Simulation results show that the error probability and the total blocked traffic are decreased.

In this paper, we describe the formatting guidelines for ACM SIG Proceedings. Recently time division duplex based wireless mesh networks (TDD-based WMNs) are emerging as a crucial technology for wireless multihop networks and broadband wireless access (BWA). Supporting real-time traffic in TDD-based WMNs is difficult and one of the main challenges is to provide strict end-to-end time delay guarantee from source node to destination node. In this paper, the cross-layer framework is proposed considering routing schemes, queueing model and resource scheduling schemes. Furthermore, an end-to-end realtime traffic scheduling algorithm is proposed. In the algorithm, a number of control slots are reserved for transmitting real-time routing messages to guarantee delay requirements while data reservation is fulfilled during the process of routing path discovery. Numerical results show that the proposed scheduling algorithm achieves low time delay and can satisfy real-time delay requirements.