759 research outputs found
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network
Federated Graph Neural Network (FedGNN) has recently emerged as a rapidly
growing research topic, as it integrates the strengths of graph neural networks
and federated learning to enable advanced machine learning applications without
direct access to sensitive data. Despite its advantages, the distributed nature
of FedGNN introduces additional vulnerabilities, particularly backdoor attacks
stemming from malicious participants. Although graph backdoor attacks have been
explored, the compounded complexity introduced by the combination of GNNs and
federated learning has hindered a comprehensive understanding of these attacks,
as existing research lacks extensive benchmark coverage and in-depth analysis
of critical factors. To address these limitations, we propose Bkd-FedGNN, a
benchmark for backdoor attacks on FedGNN. Specifically, Bkd-FedGNN decomposes
the graph backdoor attack into trigger generation and injection steps, and
extending the attack to the node-level federated setting, resulting in a
unified framework that covers both node-level and graph-level classification
tasks. Moreover, we thoroughly investigate the impact of multiple critical
factors in backdoor attacks on FedGNN. These factors are categorized into
global-level and local-level factors, including data distribution, the number
of malicious attackers, attack time, overlapping rate, trigger size, trigger
type, trigger position, and poisoning rate. Finally, we conduct comprehensive
evaluations on 13 benchmark datasets and 13 critical factors, comprising 1,725
experimental configurations for node-level and graph-level tasks from six
domains. These experiments encompass over 8,000 individual tests, allowing us
to provide a thorough evaluation and insightful observations that advance our
understanding of backdoor attacks on FedGNN.The Bkd-FedGNN benchmark is
publicly available at https://github.com/usail-hkust/BkdFedGCN
Review of Energy Transition Policies in Singapore, London, and California
The paper contains the online supplementary materials for "Data-Driven
Prediction and Evaluation on Future Impact of Energy Transition Policies in
Smart Regions". We review the renewable energy development and policies in the
three metropolitan cities/regions over recent decades. Depending on the
geographic variations in the types and quantities of renewable energy resources
and the levels of policymakers' commitment to carbon neutrality, we classify
Singapore, London, and California as case studies at the primary, intermediate,
and advanced stages of the renewable energy transition, respectively
Longitudinal brain MR retrieval with diffeomorphic demons registration: What happened to those patients with similar changes?
Current medical content-based retrieval (MCBR) systems for neuroimaging data mainly focus on retrieving the cross-sectional neuroimaging data with similar regional or global measurements. The longitudinal pathological changes along different time-points are usually neglected in such MCBR systems. We propose the cross-registration based retrieval for longitudinal MR data to retrieve patients with similar structural changes as an extension to the existing MCBR systems. The diffeomorphic demons registration is used to extract the tissue deformation between two adjacent MR volumes. An asymmetric square dissimilarity matrix is designed for indexing the patient changes within a specific interval. A visual demonstration is given to show the registration displacement fields of the query as compared to the simulated results. The experimental performance with the mean average precision (mAP) and the average top-K accuracy (aACC) are reported for evaluation
Three-Terminal Noise Source Extraction From A Qi-Based Wireless Power Transfer System For Predicting Conducted Emissions
An equivalent method to extract a three-terminal noise source from a Qi-based wireless power transfer system is proposed in this article. This method is capable of extracting a source for both the positive (P) and negative (N) lines with respect to the third terminal (the ground chassis or reference plane). The extracted sources are independent of the setup configuration and can be used to predict currents or voltages on the P and N lines on a setup that is different from the extraction setup. In the following article, a three-terminal model independent of the setup configuration is first extracted. The extracted sources are then verified by predicting currents on a setup that is different from the original setup (such as a different load impedance and height of the device above reference plane). Finally, the extracted sources are used to predict the voltages on a 1-m cable harness. The predicted currents and voltages agree within 5 dB against measurement data over a frequency range from 0.5 to 30 MHz. The proposed method can be used to predict currents and voltages on any power converter with a single-phase input configuration
: Application au Parc national des calanques de Marseille Cassis La Ciotat
International audienceThis paper presents the objectives, methodology and initial results of an interdisciplinary research project (geography, information and communication sciences) based on the site of the Calanques National Park. This project is founded by the LabEx DRIIHM-CNRS and the OHM Littoral méditerranéen. To this end, we present a semi-automatic methodology to identify and analyse descriptors related to the territory of the Calanques National Park from Twitter social network.À partir du terrain constitué par le Parc national des Calanques, cette communication présente les objectifs, la méthodologie et les premiers résultats d'un projet de recherche interdisci-plinaire (géographie, sciences de l'information, informatique) soutenu par le LabEx DRII-HM-CNRS et OHM Littoral méditerranéen. La méthodologie semi-automatisée que nous présentons vise à identifier et analyser les thématiques mentionnées et les acteurs qui s'ex-priment sur le territoire d'études à partir de Twitter
UFO2: A unified pre-training framework for online and offline speech recognition
In this paper, we propose a Unified pre-training Framework for Online and
Offline (UFO2) Automatic Speech Recognition (ASR), which 1) simplifies the two
separate training workflows for online and offline modes into one process, and
2) improves the Word Error Rate (WER) performance with limited utterance
annotating. Specifically, we extend the conventional offline-mode
Self-Supervised Learning (SSL)-based ASR approach to a unified manner, where
the model training is conditioned on both the full-context and dynamic-chunked
inputs. To enhance the pre-trained representation model, stop-gradient
operation is applied to decouple the online-mode objectives to the quantizer.
Moreover, in both the pre-training and the downstream fine-tuning stages, joint
losses are proposed to train the unified model with full-weight sharing for the
two modes. Experimental results on the LibriSpeech dataset show that UFO2
outperforms the SSL-based baseline method by 29.7% and 18.2% relative WER
reduction in offline and online modes, respectively.Comment: Accepted by ICASSP 202
- …