We propose a family of statistical models for social network evolution over time, which represents an extension Exponential Random Graph Models (ERGMs). Many the methods ERGMs are readily adapted these models, including maximum likelihood estimation algorithms. discuss this type and their properties, give examples, as well demonstration use hypothesis testing classification. believe our temporal ERG represent useful new framework modeling time-evolving networks, rewiring networks from other...

In a dynamic social or biological environment, interactions between the underlying actors can undergo large and systematic changes. Each actor assume multiple roles their degrees of affiliation to these also exhibit rich temporal phenomena. We propose state space mixed membership stochastic blockmodel which track across time evolving actors. derive an efficient variational inference procedure for our model, apply it Enron email networks, rewiring gene regulatory networks yeast. both cases,...

Abstract Accurately forecasting regional distributed photovoltaic (DPV) power is crucial in mitigating the negative impact of high DPV penetration on reliability and resilience distribution network. However, most current methods suffer from two key problems: (1) ignoring asymmetric influence relationship among sites; (2) lack consideration dynamic spatiotemporal correlation sites. As a result, these are unable to fully adapt characteristics DPV, making it challenging directly apply existing...

The increasing parameters and expansive dataset of large lan- guage models (LLMs) highlight the urgent demand for a technical solution to audit underlying privacy risks copyright issues associated with LLMs. Existing studies have partially addressed this need through an exploration pre-training data detection problem, which is instance membership inference attack (MIA). This problem involves determining whether given piece text has been used during phase target LLM. Although existing methods...

Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Prior attempts have quantified the privacy risks of language models (LMs) via MIAs, but there is still no consensus on existing MIA algorithms can cause remarkable leakage practical Large Language Models (LLMs). Existing MIAs designed LMs be classified into two categories: reference-free and reference-based attacks. They are both based hypothesis that records consistently...

Abstract Motivation: Identifying transcription factor binding sites (TFBSs) encoding complex regulatory signals in metazoan genomes remains a challenging problem computational genomics. Due to degeneracy of nucleotide content among site instances or motifs, and intricate ‘grammatical organization’ motifs within cis-regulatory modules (CRMs), extant pattern matching-based silico motif search methods often suffer from impractically high false positive rates, especially the context analyzing...

Accurate distributed photovoltaic power prediction plays a crucial role in supporting retailers making optimal trading strategies and distribution network operators reasonable scheduling plans. The mining utilization of correlation is an effective way to improve the forecasting accuracy target station. However, current methods must rely on data sharing mining, which may lead serious privacy problems. In order protect during modeling while ensuring spatiotemporal correlations, federated...

Membership Inference Attack (MIA) identifies whether a record exists in machine learning model's training set by querying the model. MIAs on classic classification models have been well-studied, and recent works started to explore how transplant MIA onto generative models. Our investigation indicates that existing designed for mainly depend overfitting target However, can be avoided employing various regularization techniques, whereas demonstrate poor performance practice. Unlike...

Accurately predicting individual-level infection state is of great value since its essential role in reducing the damage epidemic. However, there exists an inescapable risk privacy leakage fine-grained user mobility trajectories required by prediction. In this article, we focus on developing a framework privacy-preserving prediction based federated learning (FL) and graph neural networks (GNN). We propose Falcon , F ederated gr A ph L earning method for infe C tion predicti ON . It utilizes...

We propose a family of statistical models for social network evolution over time, which represents an extension Exponential Random Graph Models (ERGMs). Many the methods ERGMs are readily adapted these models, including maximum likelihood estimation algorithms. discuss this type and their properties, give examples, as well demonstration use hypothesis testing classification. believe our temporal ERG represent useful new framework modeling time-evolving networks, rewiring networks from other...

Speech Emotion Recognition (SER) is still a complex task for computers with average recall rates usually about 70% on the most realistic datasets. Most SER systems use hand-crafted features extracted from audio signal such as energy, zero crossing rate, spectral information, prosodic, mel frequency cepstral coefficient (MFCC), and so on. More recently, using raw waveform training neural network becoming an emerging trend. This approach advantageous it eliminates feature extraction pipeline....

The increasing parameters and expansive dataset of large language models (LLMs) highlight the urgent demand for a technical solution to audit underlying privacy risks copyright issues associated with LLMs. Existing studies have partially addressed this need through an exploration pre-training data detection problem, which is instance membership inference attack (MIA). This problem involves determining whether given piece text has been used during phase target LLM. Although existing methods...

A video coding system based on spatial-temporal down-sampling/up-sampling and super-resolution reconstruction method is proposed. The an exploration providing ideas for our future work. pre-reduces the spatial resolutions frame rates of source videos before being encoded by a standardized codec such as HEVC/H.265. On reception side, decoded up-sampled to four times resolution, rate also increased original rate. We tested performance in combination with 2K 60P HEVC/H.265 encoder. experiment...

Accurately predicting individual-level infection state is of great value since its essential role in reducing the damage epidemic. However, there exists an inescapable risk privacy leakage fine-grained user mobility trajectories required by prediction. In this paper, we focus on developing a framework privacy-preserving prediction based federated learning (FL) and graph neural networks (GNN). We propose Falcon, Federated grAph Learning method for infeCtion predictiON. It utilizes novel...