Due to the dynamics of underlying physics and external influences, the uncertainty of time series often varies over time. However, existing Denoising Diffusion Probabilistic Models (DDPMs) often fail to capture this non-stationary nature, constrained by their constant variance assumption from the additive noise model (ANM). In this paper, we innovatively utilize the Location-Scale Noise Model (LSNM) to relax the fixed uncertainty assumption of ANM. A diffusion-based probabilistic forecasting framework, termed Non-stationary Diffusion (NsDiff), is designed based on LSNM that is capable of modeling the changing pattern of uncertainty. Specifically, NsDiff combines a denoising diffusion-based conditional generative model with a pre-trained conditional mean and variance estimator, enabling adaptive endpoint distribution modeling. Furthermore, we propose an uncertainty-aware noise schedule, which dynamically adjusts the noise levels to accurately reflect the data uncertainty at each step and integrates the time-varying variances into the diffusion process. Extensive experiments conducted on nine real-world and synthetic datasets demonstrate the superior performance of NsDiff compared to existing approaches. Code is available at https://github.com/wwy155/NsDiff.

Unknown smart contract vulnerabilities objectively exist in addition to common vulnerabilities, and their potential risks cannot be ignored. Therefore, it is crucial to enhance the model's ability to detect these unknown threats. Detection models are typically trained for specific types of vulnerability data. As a result, their effectiveness in detecting new vulnerabilities can be unsatisfactory. When a vulnerability is not defined in the model, it is considered an unknown vulnerability. Simulating attack scenarios or manual auditing is typically required for unknown threat detection. However, this results in a limited number of unknown threat samples available in the smart contract dataset, which greatly hinders the effectiveness of unknown threat detection due to dataset imbalance. In this paper, in order to improve the detection ability of unknown vulnerabilities of smart contracts, we propose a new method of unknown threat detection, which firstly expands the unknown threat samples by equalizing the data features of opcodes and source codes through a variational autoencoder. Then, a domain-adaptive training model DSN is used to learn the private and public features of the source and target domains of smart contracts, respectively. Trans-ferring features and adversarial learning between the source and target domains is achieved through domain classification and reconfiguration tasks. The experiments demonstrate that this method can significantly enhance the ability to identify unknown threats in smart contracts.

Unsupervised timeseries anomaly detection (UTAD) aims to identify abnormal patterns within time series data and is of immense importance in extensive applications. Contrastive learning has been seen as an effective candidate for UTAD as it can learn invariants existing in two contrastive views. However, as most time series contain complex multi-periodic and non-periodic signals, the huge difference between sequences with different periodicity would make contrastive learning hard to learn representative temporal and/or spatial patterns that are essential for anomaly detection. To address this issue, we propose PACdetector, a periodicity association-based contrastive framework for UTAD. Specifically, we perceive time series as an aggregation of various periodic sequences, and for each point in the periodical sequences, we employ self-attention maps to calculate its association with points within the period (intraperiod association) and points at the same phase across different periods (interperiod association). We then perform contrastive learning between the two associations to preserve temporal consistency and obtain a distinguishable criterion between normal points and anomalies, which we refer to as Periodicity Association Discrepancy. Extensive experiments show that PACdetector outperforms various state-of-the-art algorithms, achieving the best performance across six benchmark datasets.

Time series forecasting typically needs to address non-stationary data with evolving trend and seasonal patterns. To address the non-stationarity, reversible instance normalization has been recently proposed to alleviate impacts from the trend with certain statistical measures, e.g., mean and variance. Although they demonstrate improved predictive accuracy, they are limited to expressing basic trends and are incapable of handling seasonal patterns. To address this limitation, this paper proposes a new instance normalization solution, called frequency adaptive normalization (FAN), which extends instance normalization in handling both dynamic trend and seasonal patterns. Specifically, we employ the Fourier transform to identify instance-wise predominant frequent components that cover most non-stationary factors. Furthermore, the discrepancy of those frequency components between inputs and outputs is explicitly modeled as a prediction task with a simple MLP model. FAN is a model-agnostic method that can be applied to arbitrary predictive backbones. We instantiate FAN on four widely used forecasting models as the backbone and evaluate their prediction performance improvements on eight benchmark datasets. FAN demonstrates significant performance advancement, achieving 7.76\%$\sim$37.90\% average improvements in MSE. Our code is publicly available at http://github.com/icannotnamemyself/FAN.

Effective data imputation demands rich latent ``structure" discovery capabilities from ``plain" tabular data. Recent advances in graph neural networks-based data imputation solutions show their structure learning potentials by translating tabular data as bipartite graphs. However, due to a lack of relations between samples, they treat all samples equally which is against one important observation: ``similar sample should give more information about missing values." This paper presents a novel Iterative graph Generation and Reconstruction framework for Missing data imputation(IGRM). Instead of treating all samples equally, we introduce the concept: ``friend networks" to represent different relations among samples. To generate an accurate friend network with missing data, an end-to-end friend network reconstruction solution is designed to allow for continuous friend network optimization during imputation learning. The representation of the optimized friend network, in turn, is used to further optimize the data imputation process with differentiated message passing. Experiment results on eight benchmark datasets show that IGRM yields 39.13% lower mean absolute error compared with nine baselines and 9.04% lower than the second-best. Our code is available at https://github.com/G-AILab/IGRM.

Abstract Wireless Sensor Network (WSN) has been widely used with its main characteristics of resilience, scalability, and mobility. However, the node battery capacity in WSN is limited. In addition, in scenes with emergencies such as forest ecological monitoring, data packets of wildfire emergencies have higher real‐time requirements than periodic normal data. Therefore, this paper proposes an energy‐efficient MAC protocol based on Receiver initiation and Multi‐Priority Backoff (RMP‐MAC). RMP‐MAC adopts the wake‐up matching mechanism initiated by the receiver. The sender adjusts its schedule according to the wake‐up information of the receiver to match the receiver, which reduces the energy consumption of idle listening. In addition, RMP‐MAC proposes a multi‐priority factor adaptive backoff mechanism based on event priority, node remaining energy level, and the number of data packets waiting in the transmission queue, ensuring the transmission of emergency data in the actual situation of forest ecological monitoring. Simulation experiments show that compared with PB‐MAC, AP‐MAC, and MPQ‐MAC, RMP‐MAC can reduce energy consumption by 20.22%, 12.47%, and 6.62% respectively. In addition, it has improved in the average collisions, network throughput, and average delay of packets.